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A systematic literature review of how and whether social media data can complement traditional survey data to study public opinion

Maud reveilhac.

1 Lausanne University (Switzerland), Faculty of Social and Political Sciences, Institute of Social Sciences, Life Course and Social Inequality Research Centre, Lausanne, Switzerland

Stephanie Steinmetz

Davide morselli.

2 Swiss Centre of Expertise in Life Course Research LIVES, Lausanne, Switzerland

Associated Data

Please refer to the appendix.

In this article, we review existing research on the complementarity of social media data and survey data for the study of public opinion. We start by situating our review in the extensive literature (N = 187) about the uses, challenges, and frameworks related to the use of social media for studying public opinion. Based on 187 relevant articles (141 empirical and 46 theoretical) - we identify within the 141 empircal ones six main research approaches concerning the complementarity of both data sources. Results show that the biggest share of the research has focused on how social media can be used to confirm survey findings, especially for election predictions. The main contribution of our review is to detail and classify other growing complementarity approaches, such as comparing both data sources on a given phenomenon, using survey measures as a proxy in social media research, enriching surveys with SMD, recruiting individuals on social media to conduct a second survey phase, and generating new insight on “old” or “under-investigated” topics or theories using SMD. We discuss the advantages and disadvantages associated with each of these approaches in relation to four main research purposes, namely the improvement of validity, sustainability, reliability, and interpretability. We conclude by discussing some limitations of our study and highlighting future paths for research.

Introduction

This paper provides a systematic literature review of how social media data (SMD) and traditional survey data have been used complementarily to study public opinion (PO) over the last decade. As social media users represent more than half of the world’s population (see [ 26 ]) and provide continuous reactions to daily socio-political events, it is not surprising that traditional survey research has been concerned about whether such data would make surveys obsolete or whether they could be used complementarily. Addressing these questions is particularly relevant in the area of PO. Social media plays a growing role in the formation of PO as user-generated content on these platforms is increasingly deployed as representations of PO (e.g. [ 27 , 56 ]). In addition, politicians increasingly consider social media, especially Twitter, to be a “barometer” of PO [ 44 ].

Despite the extensive literature about the benefits and challenges of using SMD to answer social and political questions, as well as about SMD as a possible replacement for traditional surveys, a comprehensive overview of the complementarity of both data sources remains limited. The aim of this paper is to fill this gap by providing a systematic literature review focusing on how SMD and survey data can complement each other to study PO. Inspired by the influential study of Japec et al. [ 45 ] which elaborated on the complementarity of survey data and “big data” (rather broadly defined), we want to concentrate, however, on one type of “big data”, namely SMD. There are two main reasons for this choice. First, SMD are a specific type of “non-survey” data which possess specific arrangements (or conventions) and paradata that are different from other types of administrative or “big data”, especially when it come to the assessment of PO. Second, whereas there is substantial research on augmenting survey data with administrative (e.g. electricity or water consumption) or other type of “web data” (e.g. Google searches or citation metrics) to improve estimates of PO or official statistics, we still lack an overarching picture of the (new) developments and approaches of complementing SMD and surveys with each other.

Our analysis is based on an extensive survey of the literature capturing a representative sample of the best published theoretical and empirical scientific papers on the topic (N = 187). We have restricted the analytical period to the last decade (2010–2020) as the discussion on complementarity is still a young field of study (e.g. [ 58 ]). On this basis, we have been able to identify six complementarity approaches which can be synthesised to four major purposes, namely predicting, substituting, comparing, and linking SMD and survey data.

In the next section, we situate our review within the existing literature by demonstrating how the scientific discussion surrounding the opportunities and challenges offered by SMD within survey research has evolved, especially by highlighting the complementary understanding of PO offered by both data sources. Then, we discuss more specifically which research approaches have emerged, and we classify them according to four main research purposes using both data sources complementarily. The analysis of the empirical studies aims to act as a guide for other researchers by identifying research gaps and highlighting the pros and cons of each approach. Furthermore, we underline areas for future improvements and point to technical and ethical considerations. We conclude by mentioning the main contributions and limitations of our review.

Background – The complementary understandings of PO

Surveys have long been the most predictive and accurate tools for collecting and measuring opinion. However, over the last decade, decreasing response rates have called into question the potential of using a random sample of individuals to represent an entire population (e.g. [ 37 , 49 ]), thus posing important concerns about the sustainability of survey research. Even by adapting to new modes, such as push-to-web, to increase response rates, it remains unclear whether surveys will maintain this dominant role as communication habits continue to change (e.g. [ 68 ]). Given the recent “survey crisis” (e.g. [ 13 , 22 ]), an increasingly rich source of PO data is commonly referred to as “big data”. These “new” data take the form of extraordinarily large and complex datasets. There are three attributes that are generally agreed upon to describe this type of data (e.g. [ 19 ]), namely volume, velocity, and variety. Social media are a sub-type of big data where people express their thoughts and opinions with the purpose of sharing them with others [ 18 ]. Due to their inherent properties, SMD have been seen as a promising complementary, and even alternative, source of data for exploring PO. However, researchers acknowledged early on that, almost universally, SMD are non-random, and thus discouraged using them as a means of making generalisable claims. This challenge is well highlighted by Schober et al. [ 68 ], who claim that, while the social media researcher seeks to achieve topic coverage, the survey researcher emphasises population coverage as a central endeavour.

An entire strand of research thus focussed on how surveys and social media differ in several aspects. Table ​ Table1 1 attempts to classify the most prominent differences along which SMD and survey data are typically compared. We have identified several dimensions based on recurring criteria mentioned in the literature concerning the nature of and the relationship between both data sources. Often-cited criteria include the type of population and data signal, the unit of observation and analysis, and the available meta-data (for a thorough discussion of the differences see [ 18 , 68 ], and [ 77 ]).

Differences between SMD and survey data to study PO

To understand how to best use both data sources complementarily, it is also essential to reflect on how they construct PO differently. This is increasingly important, as what constitutes “the public” tends to be forged by the methods and data from which it is derived [ 56 ]. In survey research, PO is equivalent to the private opinion of a representative public, operationalised as a set of positions on a given topic. PO can thus be conceptualised as a reflection of a shared position among citizens on specific issues that are then amplified and reviewed by news media and political actors [ 42 ]. Survey measures of PO are constrained by the scope of the questionnaires, which usually provide little room for spontaneous expressions of opinion (except in open-ended survey questions). The diversity of opinions is thereby reduced into a set of discrete and aggregate data (e.g. [ 75 ]). Conversely, the reliance on social media for measuring PO expands the societal and collective components of opinions [ 59 ] by conceptualising it in Habermas’ [ 39 ] terms as a complex system of representations. In this respect, SMD are better suited to capturing the conversational and relational nature of PO formation [ 3 ]. Hence, where survey data weigh precision and standardisation, SMD excel in multidimensionality and polyphony. In addition to their focus on solicited private opinions, surveys are also less reactive to opinion changes than SMD. In theory, opinion changes could be assessed by frequent short opinion surveys (e.g. every two months). However, the advantage of SMD is that they can cover opinion change more rapidly (and on an ad hoc basis), thus reacting faster to events, which is almost impossible for surveys (e.g. it takes more time to set up probability-based surveys for the study of COVID compared to what can be done with SMD).

Despite the advantages offered by social media for measuring more social and timelier opinions, the reliance on SMD raises important questions for empirical research on (automated) measurements of opinions and on the choice of the indicators employed to model opinions. Indeed, constructing measures of PO based on SMD can be very time consuming and can involve a lot of pre-processing effort before the data can be translated into meaningful measures of expressed opinions. Furthermore, it sometimes remains quite difficult to know what is driving the evolution of ideas and concerns found in online conversations. Consequently, a current strand of research seeks to better understand the issues of representativeness of social media communities and the validity of measured opinion, especially opinions stemming from sentiment analysis. While there is a rising interest in applying SMD to understand opinion, and even to replace traditional surveys (e.g. [ 3 , 32 ]), SMD alone are of limited use for social scientific research as they usually provide incomplete and imprecise information. However, the issues associated with SMD are not necessarily fatal to the proposition that they can be used to generate social insights, especially in complementing survey data. An efficient strategy to enhance research lies, therefore, in the analysis of how both data sources can complement each other in ways that maximise their strengths.

In the next sections, we aim to show that there is a plethora of research practices in which both data sources complement each other for the study of PO. To date, however, there is still no consensus about the best way to use SMD for studying PO [ 58 ]. We are now at a point where we should reflect on what has been done so far, what lessons we can learn from it, and then specify suitable trends for social research. In this paper, we seek to fill this gap by reviewing research that uses both data sources complementarily for the purposes of measuring PO and by providing a critical evaluation of the identified research paths.

Method of analysis: Building a corpus of relevant articles

To build our corpus of scientific articles, we carried out several searches in bibliographic databases (focusing on Scopus and Google Scholar ) using the software PublishOrPerish [ 40 ]. We obtained an initial corpus of 3596 unique papers, which we reduced to papers that were relevant for the scope of our review. The initial corpus was deliberately based on a search-query that was broad enough to collect the relevant literature, while not missing important papers. We used the query “(social media OR twitter OR facebook OR instagram OR reddit) AND (survey OR surveys OR polls)” and specified that it should appear in the body of the text (using the keyword field) instead of appearing only in the title or abstract, which were found to be too restrictive to capture the literature of interest. The query was designed to restrict the focus of our review to SMD, thus ignoring other types of “big data” or “digital trace” data.

A first filter was applied to reduce the number of papers to journal articles, book chapters, and scientific reports (thus excluding books, theses, and conference papers) as we wanted to concentrate on high-valued scientific sources which have already been approved by the scientific community. In this respect, including conference papers would have drastically inflated the number of (duplicated) papers concerned with predictions and with replicating previous studies using alternative methods of analysis and algorithms. Among the remaining papers, we applied two eligibility criteria to disregard those that were not pertinent to the analysis as i) their focus was not on PO, ii) they were oriented towards a specific aspect of data treatment (e.g. estimating socio-demographics from texts or profile pictures) or an analytical strategy (e.g. elaborating algorithms). We also excluded articles mentioning survey findings without an explicit aim of supplementing, comparing, or combining those with SMD.

Results of the literature review on the uses of social media as a complement to surveys

Overall, the collection protocol left us with 187 papers - 141 of an empirical and 46 of a theoretical nature (these papers can be found in the Appendix). Most of these papers stem from political communication and computational social sciences journals. Although the sample of 187 papers may not cover the whole corpus of research on the subject, it is nonetheless sufficient to highlight the main research directions that have been endorsed on the topic of complementarity. Figure ​ Figure1 1 provides an overview of the yearly repartition of the retrieved papers differentiating between those with a theoretical (N = 46) and an empirical (N = 141) focus. While the number of theoretical papers remains stable over the years, we can see a steady increase in empirical papers over time.

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Number of empirical and theoretical articles according to our meta-review of the existing literature using surveys and SMD

Theoretical insights

Starting with the theoretical papers in our review (N = 46, see Table ​ Table2 2 in the Appendix), survey and social media researchers have explored ways in which social media and survey data can yield congruent conclusions (e.g. [ 68 ]). One part of these articles (n = 14) tries to establish a framework regarding the predictive power of SMD as a potential substitute for surveys. This line of research stems principally from the fields of election and economy forecasting (for recent reviews see [ 15 , 66 ]).

List of theoretical papers focusing on the combination of survey and social media data (n = 46, continues next pages)

Another strand of theoretical articles (n = 14) focuses instead on the compliance of social media research with established reporting standards so as to guarantee transparency and replicability (e.g. [ 51 ]). Finding ways of integrating data obtained from different sources (n = 3) also constitutes a fertile path of research [ 46 ]. In this respect, Stier et al. [ 72 ] provide the most advanced guide on how to systematically link survey data with information from external data sources, including SMD, at different level of analysis. The authors demonstrate that integrating traditional survey data and digital trace data is of growing interest, notably because of the limited reliability of self-reported behavioural measures and declining response rates. Additionally, enriching survey data with SMD could also help to reduce unit non-response and to control for the unrepresentativeness of SMD, as they are limited to those respondents having social media profiles and consenting to the linkage. Finally, a smaller share of research (n = 5) focuses on developing a quality assessment framework for SMD which is similar to the Total Survey Error (TSE) [ 11 , 38 ]. The TSE framework has been extended to encompass SMD and their inherent quality challenges (see the studies by Sen et al. [ 70 ] on Twitter-based studies and Jungherr [ 47 ] for a measurement theory to account for the pitfalls of digital traces). In a similar vein, Hsieh and Murphy [ 43 ] analysed the potential benefits of evaluating estimates from surveys and SMD in common terms and arrived at a general error framework for Twitter opinion research. Olteanu et al. [ 61 ] went a step further by pointing to the errors and biases that could potentially affect studies based on digital behavioural data, outlining them in an idealised study framework. The paper by Sen et al. [ 70 ] provides the most advanced framework to date. It involves potential measurement and representation errors in a digital trace-based study lifecycle where they are classified according to their sources.

Other research (n = 5) tackles the ontology of SMD as compared to survey data. In these papers, prevalent discussions revolve around the conception of opinion as measured by both data sources, as well as debates related to the evolution of “new” research “paradigms” or “digital hermeneutics”. The remaining papers concentrate on behavioural research (n = 2), demographic research (n = 2), and small data analysis in political communication (n = 1).

Overall, the considered theoretical articles stress the importance of developing a framework that accounts for possible biases of SMD while remaining in, or mirroring, the TSE. Moreover, they also emphasize the need, in this debate, to focus on the complementarity rather than the replacing aspect, notably by developing clear and reliable linking strategies. These articles also encourage researchers to go beyond the dominant model for understanding PO from probability sample surveys to encompass other (“new”) expressions of opinions (e.g. Murphy et al. 2014) that can possibly supplement or even replace survey-based approaches.

Empirical insights

The empirical literature (N = 141) focuses on a rather narrow set of topics, such as elections, political issues, and approval ratings for the presidency (64%). Another important area of PO research using SMD complementarily with survey data is related to health (e.g. vaccination, drugs, etc.), equality issues, and climate or environment-related concerns. Most empirical studies in our review are based on Twitter data (73%), followed by Facebook (18%) and other social media (9%). This is related to the fact that not all social media platforms provide the same degree of data accessibility [ 8 ]. For instance, Facebook imposes severe limitations on the scope of retrievable data, whereas Twitter has less strong privacy settings, allowing researchers to get access to Twitter’s historical data.

Overall, we derived six major approaches on how survey data and SMD can complement each other namely i) predicting social and political outcomes using SMD (n = 48), ii) comparing both data sources on a given phenomenon (n = 26), iii) using survey measures as a proxy in social media research (n = 18), iv) enriching surveys with SMD (n = 9), v) recruiting individuals on social media to conduct a second survey phase (n = 8), and vi) generating new insight on “old” or “under-investigated” topics or theories using SMD (n = 32). These approaches can be synthesised in four, partly overlapping, ‘data complementing’ research purposes: i) validating survey findings with SMD, ii) improving the sustainability of the research by diversifying the views on a phenomenon, iii) improving the reliability of survey measures by specifying measurements, and iv) improving the interpretability of social or political issues. Figure ​ Figure2 2 summarises the relationship between the six approaches and the four research purposes. Furthermore, it shows that each purpose leads to a typical way of using both data sources complementarily. For instance, improving reliability by specifying a research question involves data linkage strategies, while generating new insights involves a sequential use of social media and survey stages.

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Complementary approaches using SMD and survey data for the study of PO

The analysis of our corpus suggests that the biggest part of research concentrates on whether SMD can potentially substitute survey data (n = 48, see Table ​ Table3 3 in the Appendix). This has mostly been done by trying to replicate survey findings by using SMD for forecasting (see recent review by [ 66 ]). The aim to predict real-world outcomes with SMD in the realm of PO has essentially been applied to elections. Most of these papers directly refer to the much-cited study of O’Connor et al. [ 60 ] which purpose is to validate SMD against survey findings. While research in this area has tested a range of different methodologies, the results remain inconclusive, and only in some cases could elections be accurately predicted (e.g. [ 31 , 47 ]). Recent literature reviews on the use of SMD for running electoral predictions (e.g. [ 15 ]) classify studies according to the employed methods of prediction, such as volume, sentiment, or network approaches. These reviews show considerable variance in the accuracy of predictions, which, on average, lag behind the established survey measurements. A common problem of the aforementioned studies lies in the decision about which approach can most accurately yield predictions (but also which social media platforms are better suited, and how that varies in different geographical or temporal contexts). This inference problem is quite complex as various elements are involved in skewing the samples in social media debates. To date, the inconclusive state of the research has led to a research agenda aiming to respond to the plea from Gayo-Avello et al. [ 33 ] for a “model explaining the predictive power of social media” (p. 490). In this realm, for instance, the study of Pasek et al. (2019) assesses how patterns of approval among population subgroups compare to tweets about the president, while disentangling effects at the individual and group levels of analysis. On a more theoretical level, the study by Schober et al. [ 69 ] seeks to elaborate when and under what conditions SMD can be used to make valid inferences. However, the inconclusive state of the research may also be linked to the fact that predictions are often done based on the content created by users and overlook the characteristics of the creating users. For instance, SMD can be biased towards a particular group (see [ 5 , 24 ]). Moreover, interactions on social media platforms are not always the product of individuals, but also bots, organisations, political parties, etc. [ 80 ]. Based on the evaluation of the body of articles falling under the ‘substitution paradigm’, a path for future research could be to better account for the characteristics of social media users, insofar as these characteristics can be useful for assessing how individual tweets can be converted into meaningful measures of expressed opinion. To do so, future studies could survey social media users identified using relevant key terms (e.g. hashtags or mentions) to gauge the relationship between social media measures of their sentiment and survey measures of their attitudes.

List of publications combining social media and survey data for prediction purposes (n = 48, continues next pages)

The second dominant approach in our review is related to how surveys can be enriched with SMD (n = 9, see Table ​ Table4 4 in the Appendix). Here, SMD are collected with the intention of improving the reliability of survey measures at the individual or aggregate level. Replication of survey-based opinions can be difficult, either because of improper interpretation of the findings or because insufficient information has been provided. Such issues undermine the credibility of survey research and make it difficult to evaluate the contributions of a given study. Research aiming to enrich surveys with SMD most often implies the adoption of a data-linking strategy. This can be done, for instance, either at the user level, public actor level, geographic level, or temporal level (see [ 72 ]). Enriching surveys with SMD can serve several goals. First, it can help to augment the explanatory potential of survey measures. For instance, De Sio & Weber [ 23 ] adopted an innovative research design to explain election outcomes based on party strategy on social media with respect to policy issue salience. They did this by linking representative mass surveys from six European countries with Twitter analysis of campaign activity. Second, enrichment of survey data with SMD can also help to test research hypotheses by relying on “true” behavioural measures (instead of self-reported survey measures). For instance, Karlsen and Enjolras [ 48 ] linked candidate survey data with Twitter data to study styles of social media campaigning. These differences in campaigning styles were then related to the extent to which candidates were successful on Twitter. Third, SMD also offer an opportunity to address issues of item non-response and calibration of novel measures. For instance, Shin [ 71 ] studied the extent to which social media users selectively consumed like-minded news stories by linking survey responses from Twitter users with their media following and exposure to news via their friends. The study further showed some differences between self-reports and digital measures, such as more pronounced patterns of selective exposure in the SMD. Finally, linking social survey and SMD further provides an opportunity to explore the relationship between attitudes and beliefs reported through surveys and content (and behaviours) generated online. For instance, Cardenal et al. [ 14 ] combined survey and Web-tracking data to analyse how Facebook-referred news consumption influenced social media users’ agendas. They found that selective exposure increased with amplified news consumption. The core problem in these studies lies in gaining consent to carry out the data linkage. This constitutes a complex procedure in which issues of anonymity, security, and disclosure all come to the fore. An additional problem is that social media measurements provide only one partial view of opinions. For instance, while researchers can measure how many times a given message has been liked, shared, or retweeted, it is much harder to account for (or measure) how often a given message has been seen or has attracted attention. Moreover, our corpus shows that research relying on linking strategies tends to remain at the individual and public actor levels of analysis, which requires requesting consent to use the linked data. This may, in turn, introduce consent or selection bias. To mitigate such difficulties, future studies should also explore the potentials of linking both data sources at higher levels of analysis, such as country or according to topicality level.

List of publications combining social media and survey data for enrichment purposes (n = 9)

A third purpose is to use surveys as a proxy in social media research. This approach therefore reverses the logic that SMD are always used as a complementary (side) element of the main survey-based analyses. In this kind of “survey proxy approach” (n = 18, see Table ​ Table5 5 in the Appendix), SMD are used as the main source of analysis, while the survey data are used for contextualising or calibrating SMD. A first strand of research relies on SMD to complement traditional research approaches in political communication and citizens’ political engagement. For instance, the assessment of the importance of given public concerns in PO has been measured extensively with the “most important problem” survey item. Social media provide another way to measure this concern in an unintrusive way by (semi-)automatically classifying the content of social media texts, while also accounting for the extent to which different actors are responsive to these concerns. Following this logic, the study conducted by Eberl et al. [ 28 ] investigated the effects of sentiment and issue salience on emotionally labelled responses to posts written by political actors on Facebook. Another study, by Plescia et al. [ 64 ], analysed the responsiveness of populist parties to the issue salience amongst the public. They did this by relying on survey data to measure public salience and tweets to assess salience issue for parties. A second strand of studies aims at facilitating cross-national comparisons. For instance, a possible application consists in using survey data for classifying parties and voters along important dimensions (e.g. see [ 30 ]). Here, parties were placed on a left and right spectrum using the Chapel Hill Expert Survey [ 4 ]. Party score on the overall ideological stance was then used as an explanatory variable in subsequent analysis. Another example is the study by Park et al. [ 62 ] which investigated the consumption of popular YouTube videos in countries that differ in cultural values, language, gross domestic product, and Internet penetration rate. A possible issue encountered by these studies is linked to spurious effects between survey and social media measurements (e.g. misleading or unexplained correlations). Furthermore, these studies tend to remain poorly equipped to explain actual motives behind social media users’ expression of opinions or reactions. The “survey as proxy” approach requires a considerable dose of ingenuity and methodological innovation to mine social media for producing opinion estimates that can be merged with survey estimates. For instance, SMD corpora often deviate from a predefined (survey) coding scheme. Substantively, a future path of research should take advantage of the fact that a growing number of societal issues have become transnational, such as immigration, terrorism, women’s rights, and climate change. Such research could involve the combination of word embeddings and survey opinion measures at the country level.

List of publications using survey as proxy with social media data (n = 18, continues next page)

A fourth approach aims to compare SMD with survey responses that directly measure PO. Studies comparing SMD with survey data (n = 26, see Table ​ Table6 6 in Appendix) essentially aim at improving sustainability of the research, which consists in the ability to gauge PO consistently over time. Sustainability thus implies that we should develop designs that include opportunities for “holistic merging” of the data that will generate more inclusive and fine-grained research insights. There are several reasons that comparing both data sources is meaningful for social research. Firstly, comparing SMD and survey data can be very useful in times of protests and collective actions, notably due to the difficulty of generating survey data to properly assess these disruptive changes (see critique of survey data by Lee [ 53 ]). The timing of an event might indeed not coincide with the timing of a survey, which is often done ex-post. For instance, Davis et al. [ 21 ] examined the extent to which tweets about the affordable care act (“Obamacare”) could be used to measure PO over time. Secondly, social media can be compared to surveys for research questions that require chronicity, on a weekly or daily basis, thus going beyond the few ongoing surveys that collect data monthly or yearly. For instance, Diaz et al. [ 25 ] demonstrated how social media activity functions like an “opt-in panel” where users repeatedly discuss the same topics. This allows us to study, longitudinally, quite rapid shifts in individual opinions and behaviours, thus complementing survey panels which are prohibitively expensive. Another example is the study by Loureiro & Alló [ 54 ], which aimed to complement surveys by providing up-to-date measurements about social concerns when debating mitigation and energy transition paths. Thirdly, survey questions are often designed to capture internal attitudes toward a specific object. However, the relevance of certain survey questions might vary over time and, in some cases, might no longer correspond to the issues discussed spontaneously online. For instance, at a geographical level, the study by Scarborough [ 67 ] compared gender equality attitudes found in survey data to sentiments emanating from tweets. Fourth, SMD can produce quicker and less expensive statistics for enabling informed policy and program decisions. However, this requires gaining knowledge of where any possible disparities in attitude distributions between SMD and survey data may lie. In this respect, the study by Amaya et al. [ 2 ] presented recent advancements. The authors compared attitude distributions between Reddit users and survey measures of political leaning, political interest, and policy issues. They showed that Reddit users tend to have more centrist and normally distributed scores than the survey data, skewing estimates toward the conservative end of the spectrum on all attitude measures. Another study, from Pasek et al. (2020), explained that SMD might be better conceived as providing insights about public attention rather than (“survey like”) attitudes or opinions. To do so, the authors compared tweets mentioning the presidential candidates and open-ended survey questions about the candidates to assess whether spikes surrounding political events correlate between both data sources. Results display some support for the correlation between social media attention and survey data, but they also show systematic differences that need to be better understood to assess when SMD can best generate insights about select topics. The research comparing both data sources tends to remain focused on volume analysis and tonality assessment. This type of research also tends to pay little attention to the domain-specificity of the SMD collected as well as to ways of mitigating replicability and consistency issues (e.g. [ 34 ]). For instance, the evolution of search queries around a given theme might lack precision and consistency over time. The connotation of hashtags can change or whole hashtags can even disappear. Better combining both data sources also requires elaborating more sophisticated measures of opinion and attitudes. One could think about pushing forward “stance detection” in complement to “sentiment detection”, but also about advancing “narrative analysis” in complement to “topic or frame detection”. These are avenues where computational social research would benefit from the expertise of applied computational linguistics.

List of publications combining social media and survey data for comparison purposes (n = 26, continues next pages)

A fifth approach implicates using SMD to generate new insights. This is especially useful when survey data are not available or when survey data are not recent enough (n = 32, see Table ​ Table7 7 in Appendix). Here, the main purpose is to improve the interpretability of the research by adopting an “ethnographic” methodology. By avoiding rigid research design plans, SMD can remain responsive to, and pursue, new paths of discovery as they emerge. Based on the papers collected, we found typical reasons for relying on SMD to generate new insights, such as capturing emergent opinions, expanding the scope of survey measures, validating survey measures, proposing novel approaches to get a more nuanced or dynamic perspective on PO, and making causal analyses (see column “Reason to complement” in Table ​ Table6 6 in Appendix). When used for capturing emergent opinions, SMD allow us to study the topical and normative climate around specific issues for which we have no theoretically grounded ideas yet. In this exploratory design, social media can provide survey researchers with a snapshot of important societal and political concerns worth surveying in future research. This is especially useful for emerging topics, such as nuclear power (e.g. [ 50 ]) or health-related policies [ 65 , 74 ]. On these emerging issues, SMD can be used in an exploratory or ethnographic perspective to generate initial and qualitative insights into under-studied research objects in order to develop quantitative survey measurements. SMD can also be useful for expanding the scope of survey measures on topics that are difficult to survey. For instance, Hatipoğlu et al. [ 41 ] used SMD to study international relationships with a case study on Turkish sentiments towards Syrian refugees using Twitter. Another study by Guan et al. (2020) relied on the social media platform Weibo to study Chinese views of the United States. SMD can also be useful for validating survey measures. For instance, the study by Dahlberg et al. [ 20 ] investigated the meanings of democracy in a cross-country perspective to better understand differences in the usage of the term “democracy” across languages and countries. The authors’ findings aimed to inform survey measurements about the different conceptualisations of democracy, notably by highlighting translations and language equivalence issues in survey items. Another reason is to propose novel approaches for achieving a more nuanced or dynamic perspective on PO. For instance, researchers can add new components and improve “old findings”, which are difficult to measure with survey data. In this view, the study by Barberá et al. [ 7 ] modelled policy issue responsiveness using Twitter data, thus going beyond the more static perspective on issue congruence offered by surveys. In another study, Clark et al. [ 16 ] investigated organisational legitimacy in a case study about public reactions on social media to the Supreme Court’s same-sex marriage cases. The authors argued that SMD can lessen some of the limitations of survey research in the field, notably by accessing not just policy positioning among individuals but also a variety of features of political discourse, such as opinion intensity and emotions like anger or happiness. SMD can also be used to make causal inferences in order to understand changes in opinion before and after an event, such as measuring the effect of a promulgated law on PO [ 1 ]. Here, SMD allow researchers to rely on spontaneous opinions expressed online rather than on retrospective survey questions, and this can help develop policy initiatives. For instance, Tavoschi et al. [ 73 ] used Twitter as a “sentinel system” to assess the orientation of PO in relation to vaccination. Despite the advantages of SMD in providing new research insights, these studies tend to lack a rigorous contextualisation of the findings derived from SMD. In this respect, a reliance on SMD would benefit from implementing sequential designs, where social media help to identify specific populations or sub-topics, which could then lead to a second quantitative survey phase. Whenever possible, SMD would further benefit from a comparison with longitudinal surveys to assess the extent to which both data sources reveal similar dynamics of change. Future studies could further exploit SMD’s ability to generate new insights for research in sensitive fields, such as war, racism, sexual orientation, and religious beliefs. These are often topics on which it remains difficult to collect survey data, notably because of the social desirability bias (e.g. [ 52 ]) and the like (e.g. extreme response style, moderacy bias, and acquiescence), but also because of the fear of being denounced or because the topic is controversial.

List of publications combining social media and survey data for generating new insights (n = 32, continues next pages)

The last approach using SMD and survey data complementarily focuses on using social media to recruit survey respondents. However, in comparison with the previous approach, the studies collected here usually analyse SMD and survey data in sequential phases. As we only consider papers that are in some way also related to PO and are not solely about recruitment of survey respondents and their socio-demographic characteristics, the number of studies we were able to analyse is much smaller (n = 8, see Table ​ Table8 8 in the Appendix). Our review demonstrates that the papers essentially tackle the problem surveys have in recruiting specific politically involved sub-groups of the population. In particular, the research relies on social media to access representative samples of social media users, for instance, those who commented on their countries’ elections (see [ 9 , 12 ]) or who posted at least one election-related tweet [ 79 ]. Furthermore, in these studies, ethical concerns (e.g. privacy, tracking, etc.), but also the technical affordability of the social media platform used, are discussed. The latter issue is important, as each social media platform has particular arrangements which are likely to influence the group of individuals that can be reached. Overall, future studies could think about extending the recruitment approach to enhance our knowledge of reactions to systematic events, topics, or other repetitive features (such as supporting an issue or taking part in actions), while eliminating recall errors. Furthermore, relying on SMD can help researchers pre-test their hypotheses for future surveys by uncovering relevant underlying discursive patterns or by making smaller-scale qualitative observations.

List of publications using social media as a recruitment tool (n = 8)

Summary and concluding remarks

The aim of this article was to provide a review of published papers on the complementarity of SMD and survey data for PO research. We started this review by situating our work within theoretical advances concerning the complementarity of both data source. There has been extensive work underlying the opportunities and (quality) challenges of SMD for answering social research questions. However, research attention has only recently turned to SMD as a source of expression of PO and of its measurement. Consequently, there is a need for more research to uncover the ways in which SMD can be best used for fostering the understanding of PO.

The main contribution of our review is to provide a complete picture of the empirical research on the topic while calling attention to the pros and cons of each approach and possible future paths of advancements. Though this review might not be exhaustive, it has enabled us to show six major complementarity approaches which were identified as responding to four different research purposes. Below we highlight the main research paths for each approach. Using both data sources complementarily for prediction purposes was by far the most prominent approach and it remains a research area which raises many questions about the potential generalisability of the findings, namely in terms of the representativeness and validity of social media measurements of PO. We believe that the most important difficulty lies perhaps in the manner in which these studies deduce political opinions or attitudes from SMD. Survey researchers readily admit that opinions are more difficult to measure than behaviour because they involve what people think and not just how they act. Thereby, the choice to rely on sentiment analysis or merely on volume metrics (such as the number of retweets or mentions) seems unclear, at least for the near future.

Approaches concerned with improving sustainability have a significant potential for advancing social research, as they allow researchers to combine the richness of SMD content with established survey measures. When SMD are used in similar contexts to survey data, we believe that a critical view should prevail, informed by current social science best practices and expertise. For instance, whereas surveys draw a sample of carefully worded and standardised questions, social media can cover many topics as well as different facets of the same topic, which are not necessarily defined a priori on a theoretical basis. This research avenue is most likely to be fruitful for studies aiming to augment surveys by mapping discussions that are topical on social media, while allowing variations at country or regional levels of analysis to be discerned (e.g. Bennett et al. [ 10 ] on climate change opinions). Studies aiming to compare both data sources are certainly the most suitable to help improve our understanding about when and how both data sources can be validly combined. Survey methodologists can play a decisive role, notably by paying attention to the type of (open-ended) questions that can be more directly comparable with SMD. This direction can also inform the lack of consistent evidence for the first prediction approach.

Alternatively, studies aiming to improve reliability see research as mostly requiring control for the still severe limitations of using SMD appropriately in a PO context. In this respect, studies enriching survey data with SMD offer a solution to the fact that social media often lack relevant individual information, such as respondent’s attributes (e.g. sociodemographic characteristics or personality traits) or key outcome variables (e.g. voting, social, or political attitudes). Additionally, the “survey as proxy” approach enables researchers to calibrate SMD according to standardized survey measures at the actor (e.g. political candidates or parties) or context levels by reversing the data linking strategy. Future paths for both approaches implicate opening up the analysis to non-individual levels.

Studies aiming to improve the interpretability of survey research by generating new insights or by recruiting respondents on social media for a second survey phase, and that use both data sources complementarily, offer additional fertile ways to consider for new analyses that would not be possible using survey data alone. In this view, SMD do not aim to replace opinion surveys, but aim to provide a broader context for interpreting opinion, which will then serve to improve the quality of survey questions. This research avenue is most likely to be useful for knowing more about hard-to-reach populations (e.g. the LGBTQI* or disabled persons communities) or topics that are difficult to survey (e.g. violence and racism), especially when conducting iterative phases of analysis. It is also useful to get “opinion climates” about topics which have long been under survey scrutiny (e.g. emerging concerns related to feminism or social inclusion) in order to develop “updated” survey measurements.

Bringing together the opportunities offered by these different approaches shows that samples of social media users do not necessarily have to be representative of the general public to be used meaningfully as a complement to surveys. Most importantly, we believe that SMD should supplement, but not replace, traditional methods and data sources in the study of PO. By keeping up with current developments, we believe that remaining in the framework of survey research when using both data sources complementarily is paramount for identifying potential non-survey data sources, accessing them, and assessing their quality and usefulness for the study of PO. Like mixed-method approaches combining qualitative and quantitative data (e.g. [ 36 ]), the primary motive for complementing survey and SMD with one another is to allow researchers to mix datasets in a meaningful way for developing an overall interpretation.

Technical and ethical note

Regarding sustainability, it is important to consider that the patterns of social media consumption are influenced not only by user preferences, but also by technological changes and the availability of the platforms. For instance, social media companies may not survive and whole platforms could disappear, thus impeding data access. With changes in consumption patterns, PO may be difficult to measure consistently over time. From a more technical perspective, it is also important to assess the extent to which databases composed of social media texts collected by different means (e.g. different search queries or different platform algorithms) might raise consistency and replicability issues (e.g. [ 34 ]). As for reliability, several issues are worth considering. Even though SMD can provide complementary information to survey estimates though linkage, there are sometimes concerns about the veracity or honesty of the information collected. For instance, SMD may increase the potential for social stigmatisation, causing users to be more reluctant to share their true opinions [ 63 ]. However, the opposite may also be true: users could express more radical opinions to gain social approval (e.g. disinhibition effect). The identity of those who post can also raise veracity concerns [ 55 ], and it may be difficult to distinguish sarcastic content from texts that are straight-forwardly positive or negative (e.g. [ 35 ]). Another important issue is that we usually know how many people have liked a post, clicked on a link, or retweeted a message, but we rarely know how many people have seen the item and chosen not to take any action [ 77 ]. Furthermore, due to algorithms that favour selective exposure and homophily of opinion [ 6 , 17 ], it is important to assess the extent to which findings derived from online opinion generate more polarised opinions than the ones that would be obtained through the private setting of surveys.

When researchers aim to generate new insights, they should consider that each social media platform has particular arrangements. For instance, the orientation of the content (e.g. political, family-oriented, business-oriented) as well as the scope of the content (e.g. possible bias toward more visible events) can play a decisive role on what content is available and which user profiles are most likely to be active on the social media platform. Furthermore, the nature of the platform allows for different levels of engagement in debates (e.g. Twitter is mostly used for short text content, while YouTube and Instagram allow sharing and commenting on videos and pictures). Functional capabilities can not only influence the ways of recruiting respondents for a second survey phase (e.g. direct messages), but also the identifying of sub-groups of interest (e.g. differences between friend and follower networks, and the reciprocity of follower networks). In addition, social media platforms may give users control over the availability of the information (e.g. to suppress or filter unwanted comments), which will again impact what is available from whom and on what.

For each research purpose, we should also consider that there are important ethical factors that are likely to influence the possible paths of research relying on SMD. Each platform has its own rules which are subject to change at any time. For instance, anonymity settings also affect the content of SMD, with growing concerns about surveillance and the resulting loss of privacy [ 29 , 76 , 78 ], thus influencing what people are willing to post. There are also evolving rules about the banning of particular words and behaviours, as well as users, which may influence research findings (especially when conducting longitudinal research). SMD are private property of tech companies and can be arbitrarily erased or made inaccessible, compromising the replicability of research.

Our review has several limitations. First, it focuses on social media but do not include other data sources that are frequently compared to survey data to model PO (e.g. Google trends, mainstream media, or administrative data). We thus encourage future research to extend the proposed complementary framework to additional data sources. This would allow the building of knowledge about the most suitable ways of combining these data for answering specific research purposes. Furthermore, our review entails a conceptual aim with less focus on the variety of methods used to either collect, clean, analyse, and aggregate the data to generate statistics. Discussing the pros and cons of methodologies employed by these papers could constitute the object of another review.

Notwithstanding these limitations, our study is not only of interest for social and political scientists concerned by the declining response rates and restrictive budgeting for survey research [ 57 ]. As social media have been established as multifunctional tools, and many companies and researchers implement strategies based on social media to collect opinions, make predictions, study behaviours, conduct experiments, or recruit hard-to-reach populations, this review is also of interest for practitioners.

Extracting PO from social media text can foster social sciences by moving it forward as an applied field, thus bridging gaps between computational models and interpretative research. We see this collaboration as particularly important for developing more advanced and reliable measures of opinion from social media texts. This also constitutes an opportunity to challenge the opposition of the so-called data-driven and theory-driven approaches , a simplistic dichotomy which further consolidates the misconception that social research can be conducted by relying solely on text-based data. We encourage researchers to acknowledge the different conceptualisation of opinion when measured by SMD and surveys, and we advise them to adopt a mixed-method strategy where the complementarity of both data is paramount.

Code availability

Not applicable;

Authors contribution

All authors contributed to the design of the study. Maud Reveilhac planned the study, conducted the data analysis, and wrote the manuscript. All authors contributed to the review of the manuscript and approved the final version.

Open access funding provided by University of Lausanne.

Data availability

Declarations.

We have no conflict of interests to disclose;

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Maud Reveilhac, Email: [email protected] .

Stephanie Steinmetz, Email: [email protected] .

Davide Morselli, Email: [email protected] .

Book cover

Modern Socio-Technical Perspectives on Privacy pp 113–147 Cite as

Social Media and Privacy

  • Xinru Page 7 ,
  • Sara Berrios 7 ,
  • Daricia Wilkinson 8 &
  • Pamela J. Wisniewski 9  
  • Open Access
  • First Online: 09 February 2022

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With the popularity of social media, researchers and designers must consider a wide variety of privacy concerns while optimizing for meaningful social interactions and connection. While much of the privacy literature has focused on information disclosures, the interpersonal dynamics associated with being on social media make it important for us to look beyond informational privacy concerns to view privacy as a form of interpersonal boundary regulation. In other words, attaining the right level of privacy on social media is a process of negotiating how much, how little, or when we desire to interact with others, as well as the types of information we choose to share with them or allow them to share about us. We propose a framework for how researchers and practitioners can think about privacy as a form of interpersonal boundary regulation on social media by introducing five boundary types (i.e., relational, network, territorial, disclosure, and interactional) social media users manage. We conclude by providing tools for assessing privacy concerns in social media, as well as noting several challenges that must be overcome to help people to engage more fully and stay on social media.

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1 Introduction

The way people communicate with one another in the twenty-first century has evolved rapidly. In the 1990s, if someone wanted to share a “how-to” video tutorial within their social networks, the dissemination options would be limited (e.g., email, floppy disk, or possibly a writeable compact disc). Now, social media platforms, such as TikTok, provide professional grade video editing and sharing capabilities that give users the potential to both create and disseminate such content to thousands of viewers within a matter of minutes. As such, social media has steadily become an integral component for how people capture aspects of their physical lives and share them with others. Social media platforms have gradually altered the way many people live [ 1 ], learn [ 2 , 3 ], and maintain relationships with others [ 4 ].

Carr and Hayes define social media as “Internet-based channels that allow users to opportunistically interact and selectively self-present, either in real time or asynchronously, with both broad and narrow audiences who derive value from user-generated content and the perception of interaction with others” [ 5 ]. Social media platforms offer new avenues for expressing oneself, experiences, and emotions with broader online communities via posts, tweets, shares, likes, and reviews. People use these platforms to talk about major milestones that bring happiness (e.g., graduation, marriage, pregnancy announcements), but they also use social media as an outlet to express grief and challenges, and to cope with crises [ 6 , 7 , 8 ]. Many scholars have highlighted the host of positive outcomes from interpersonal interactions on social media including social capital, self-esteem, and personal well-being [ 9 , 10 , 11 , 12 ]. Likewise, researchers have also shed light on the increased concerns over unethical data collection and privacy abuses [ 13 , 14 ].

This chapter highlights the privacy issues that must be addressed in the context of social media and provides guidance on how to study and design for social media privacy. We first provide an overview of the history of social media and its usage. Next, we highlight common social media privacy concerns that have arisen over the years. We also point out how scholars have identified and sought to predict privacy behavior, but many efforts have failed to adequately account for individual differences. By reconceptualizing privacy in social media as a boundary regulation, we can explain these gaps from previous one-size-fits-all approaches and provide tools for measuring and studying privacy violations. Finally, we conclude with a word of caution about the consequences of ignoring privacy concerns on social media.

2 A Brief History of Social Media

Section highlights.

Social media use has quickly increased over the past decade and plays a key role in social, professional, and even civic realms. The rise of social media has led to “networked individualism.”

This enables people to access a wider variety of specialized relationships , making it more likely they can meet a variety of needs. It also allows people to project their voice to a wider audience.

However, people have more frequent turnover in their social networks , and it takes much more effort to maintain social relations and discern (mis)information and intention behind communication.

The initial popularity of social media harkened back to the historical rise of social network sites (SNSs). The canonical definition of SNSs is attributed to Boyd and Ellison [ 15 ] who differentiate SNSs from other forms of computer-mediated communication. According to Boyd and Ellison, SNS consists of (1) profiles representing users and (2) explicit connections between these profiles that can be traversed and interacted with. A social networking profile is a self-constructed digital representation of oneself and one’s social relationships. The content of these profiles varies by platform from profile pictures to personal information such as interests, demographics, and contact information. Visibility also varies by platform and often users have some control over who can see their profile (e.g., everyone or “friends”). Most SNSs also provide a way to leave messages on another’s profile, such as posting to someone’s timeline on Facebook or sending a mention or direct message to someone on Twitter.

Public interest and research initially focused on a small subset of SNSs (e.g., Friendster [ 16 ] and MySpace [ 17 , 18 , 19 ]), but the past decade has seen the proliferation of a much broader range of social networking technologies, as well as an evolution of SNSs into what Kane et al. term social media networks [ 20 ]. This extended definition emphasizes the reach of social media content beyond a single platform. It acknowledges how the boundedness of SNSs has become blurred as platform functionality that was once contained in a single platform, such as “likes,” are now integrated across other websites, third parties, and mobile apps.

Over the past decade, SNSs and social media networks have quickly become embedded in many facets of personal, professional, and social life. In that time, these platforms became more commonly known as “social media.” In the USA, only 5% of adults used social media in 2005. By 2011, half of the US adult population was using social media, and 72% were social users by 2019 [ 21 ]. MySpace and Facebook dominated SNS research about a decade ago, but now other social media platforms, such as YouTube, Instagram, Snapchat, Twitter, Kik, TikTok, and others, are popular choices among social media users. The intensity of use also has drastically increased. For example, half of Facebook users log on several times a day, and three-quarters of Facebook users are active on the platform at least daily [ 21 ]. Worldwide, Facebook alone has 1.59 billion users who use it on a daily basis and 2.41 billion using it at least monthly [ 22 ]. About half of the users of other popular platforms such as Snapchat, Instagram, Twitter, and YouTube also report visiting those sites daily. Around the world, there are 4.2 billion users who spend a cumulative 10 billion hours a day on social networking sites [ 23 ]. However, different social networking sites are dominant in different cultures. For example, the most popular social media in China, WeChat (inc. Wēixìn 微信), has 1.213 billion monthly users [ 23 ].

While SNS profiles started as a user-crafted representation of an individual user, these profiles now also often consist of information that is passively collected, aggregated, and filtered in ways that are ambiguous to the user. This passively collected information can include data accessed through other avenues (e.g., search engines, third-party apps) beyond the platform itself [ 24 ]. Many people fail to realize that their information is being stored and used elsewhere. Compared to tracking on the web, social media platforms have access to a plethora of rich data and fine-grained personally identifiable information (PII) which could be used to make inferences about users’ behavior, socioeconomic status, and even their political leanings [ 25 ]. While online tracking might be valuable for social media companies to better understand how to target their consumers and personalize social media features to users’ preferences, the lack of transparency regarding what and how data is collected has in more recent years led to heightened privacy concerns and skepticism around how social media platforms are using personal data [ 26 , 27 , 28 ]. This has, in turn, contributed to a loss of trust and changes in how people interact (or not) on social media, leading some users to abandon certain platforms altogether [ 26 , 29 ] or to seek alternative social media platforms that are more privacy focused.

For example, WhatsApp, a popular messaging app, updated its privacy policy to allow its parent company, Facebook, and its subsidiaries to collect WhatsApp data [ 30 ]. Users were given the option to accept the terms or lose access to the app. Shortly after, WhatsApp rival Signal reported 7.5 million installs globally over 4 days. Recent and multiple social media data breaches have heightened people’s awareness around potential inferences that could be made about them and the danger in sensitive privacy breaches. Considering the invasive nature of such practices, both consumers and companies are increasingly acknowledging the importance of privacy, control, and transparency in social media [ 31 ]. Similarly, as researchers and practitioners, we must acknowledge the importance of privacy on social media and design for the complex challenges associated with networked privacy. These types of intrusions and data privacy issues are akin to the informational privacy issues that have been investigated in the context of e-commerce, websites, and online tracking (see Chap. 9 ).

While early research into social media and privacy largely focused on these types of concerns, researchers have uncovered how the social dynamics surrounding social media have led to a broader array of social privacy issues that shape people’s adoption of platforms and their usage behaviors. Rainie and Wellman explain how the rise of social technologies, combined with ubiquitous Internet and mobile access, has led to the rise of “networked individualism” [ 32 ]. People have access to a wider variety of relationships than they previously did offline in a geographically and time-bound world. These new opportunities make it more likely that people can foster relationships that meet their individual needs for havens (support and belonging), bandages (coping), safety nets (protect from crisis), and social capital (ability to survive and thrive through situation changes). Additionally, social media users can project their voice to an extended audience, including many weak ties (e.g., acquaintances and strangers). This enables individuals to meet their social, emotional, and economic needs by drawing on a myriad of specialized relationships (different individuals each particularly knowledgeable in a specific domain such as economics, politics, sports, caretaking). In this way, individuals are increasingly networked or embedded within multiple communities that serve their interests and needs.

Inversely, networked individualism has also made people less likely to have a single “home” community, dealing with more frequent turnover and change in their social networks. Rainie and Wellman describe how people’s social routines are different from previous generations that were more geographically bound – today, only 10% of people’s significant ties are their neighbors [ 32 ]. As such, researchers have questioned and studied the extent to which people can meaningfully maintain interpersonal relationships on social media. The upper limit for doing so has been estimated at 150 connections or “friends” [ 33 ], but social media connections often well exceed this number. With such large networks, it also takes users much more effort to distinguish (mis)information, when communication is intended for the user, and the intent behind that communication. The technical affordances of social media can also help or hinder their (in)ability to capture the nuances of the various relationships in their social network. On many social media platforms, relationships are flattened into friends and followers, making them homogenous and lacking differentiation between, for instance, casual acquaintance and trusted confidant [ 16 , 34 ]. These characteristics of social media lead to a host of social privacy issues which are crucial to address. In the next section, we summarize some of the key privacy challenges that arise due to the unique characteristics of social media.

3 Privacy Challenges in Social Media

Information disclosure privacy issues have been a dominant focus in online technologies and the primary focus for social media. It focuses on access to data and defining public vs. private disclosures . It emphasizes user control over who sees what.

With so many people from different social circles able to access a user’s social media content, the issues of context collapse occur. Users may post to an imagined audience rather than realizing that people from multiple social contexts are privy to the same information.

The issues of self-presentation jump to the foreground in social media. Being able to manage impressions is a part of privacy management.

The social nature of social media also introduces the issues of controlling access to oneself , both in terms of availability and physical access.

Despite all of these privacy concerns, there is a noted privacy paradox between what people say they are concerned about and their resulting behaviors online.

Early focus of social media privacy research was focused on helping individuals meet their privacy needs in light of four key challenges: (1) information disclosure, (2) context collapse, (3) reputation management, and (4) access to oneself. This section gives an overview of these privacy challenges and how research sought to overcome them. The remainder of this chapter shows how the research has moved beyond focusing on the individual when it comes to social media and privacy; rather, social media privacy has been reconceptualized as a dynamic process of interpersonal boundary regulation between individuals and groups.

3.1 Information Disclosure/Control over Who Sees What

A commonality among early social media privacy research is that the focus has been on information privacy and self-disclosure [ 35 ]. Self-disclosure is the information a person chooses to share with other people or websites, such as posting a status update on social media. Information privacy breaches occur when a website and/or person leaks private information about a user, sometimes unintentionally. Many studies have focused on informational privacy and on sharing information with, or withholding it from, the appropriate people [ 36 , 37 , 38 ] on social media. Privacy settings related to self-disclosure have also been studied in detail [ 39 , 40 , 41 ]. Generally, social media platforms help users control self-disclosure in two ways. First is the level of granularity or type of information that one can share with others. Facebook is the most complex, allowing users to disclose and control more granular information for profile categories such as bio, website, email addresses, and at least eight other categories at the time of writing this chapter. Others have fewer information groupings, which make user profiles chunkier, and thus self-disclosure boundaries less granular. The second dimension is one’s access level permissions, or with whom one can share personal information. The most popular social media platforms err on the side of sharing more information to more people by allowing users to give access to categories such as “Everyone,” “All Users,” or “Public.” Similarly, many social media platforms give the option for access for “friends” or “followers” only.

Many researchers have highlighted how disclosures can be shared more widely than intended. Tufekci examined disclosure mechanisms used by college students on MySpace and Facebook to manage the boundary between private and public. Findings suggest that students are more likely to adjust profile visibility rather than limiting their disclosures [ 42 ]. Other research points out how users may not want their posts to remain online indefinitely, but most social media platforms default to keeping past posts visible unless the user specifies otherwise [ 43 ]. Even when the platform offers ways to limit post sharing, there are often intentional and unintentional ways this content is shared that negates the users’ wishes. For example, Twitter is a popular social media platform where users can choose to have their tweets available only to their followers. However, millions of private tweets have been retweeted, exposing private information to the public [ 44 ]. Even platforms like Snapchat, which make posts ephemeral by default, are susceptible to people taking screenshots of a snap and distributing through other channels. Thus, as social media companies continue to develop social media platforms, they should consider how to protect users from information disclosure and teach people to practice privacy protective habits.

Although some users adjust their privacy settings to limit information disclosures, they may be unaware of third-party sites that can still access their information. Scholars have emphasized the importance of educating users on the secondary use of their data, such as when third-party software takes information from their profiles [ 45 ]. Data surveillance continues to expand, and the business model of social media corporations tends to favor getting more information about users, which makes it difficult for users that want to control their disclosure [ 46 ]. Third-party apps can also access information about social media users’ connections without consent of the person whose information is being stored [ 47 ].

3.2 Unique Considerations for Managing Disclosures Within Social Media

As mentioned earlier, social media can expand a person’s network, but as that network expands and diversifies, users have less control over how their personal information is shared with others. Two unique privacy considerations for social media that arise from this tension are context collapse and imagined audiences, which we describe in more detail in the subsections below. For example, as Facebook has become a social gathering place for adults, one’s “friends” may include family members, coworkers, colleagues, and acquaintances all in one virtual social sphere. Social media users may want to share information with these groups but are concerned about which audiences are appropriate for sharing what types of information. This is because these various social spheres that intersect on Facebook may not intersect as readily in the physical world (e.g., college buddies versus coworkers) [ 48 ]. These distinct social circles are brought together into one space due to social media. This concept is referred to as “context collapse” since a user’s audience is no longer limited to one context (e.g., home, work, school) [ 15 , 49 , 50 ]. We highlight research on the phenomenon of the privacy paradox and explain how context collapse and imagined audiences may help explain the apparent disconnect between users’ stated privacy concerns and their actual privacy behavior.

Context Collapse

Nuanced differences between one’s relationships are not fully represented on social media. While real-life relationships are notorious for being complex, one of the biggest criticisms of social media platforms is that they often simplify relationships to a “binary” [ 51 ] or “monolithic” [ 52 ] dimension of either friend or not friend. Many platforms just have one type of relationship such as a “friend,” and all relationships are treated the same. Once a “friend” has been added to one’s network, maintaining appropriate levels of social interactions in light of one’s relationship context with this individual (and the many others within one’s network) becomes even more problematic [ 53 ]. Since each friend may have different and, at times, mutually exclusive expectations, acting accordingly within a single space has become a challenge. As Boyd points out, for instance, teenagers cannot be simultaneously cool to their friends and to their parents [ 53 ]. Due to this collapsed context of relationships within social media, acquaintances, family, friends, coworkers, and significant others all have the same level of access to a social media user once added to one’s network – unless appropriately managed.

Research reveals that the way people manage context collapses varies. Working professionals might deal with context collapse by limiting posts containing personal information, creating different accounts, and avoiding friending those they worked with [ 54 ]. As another example, many adolescents manage context collapse by keeping their family members separate from their personal accounts [ 55 ]. Other mechanisms for managing context collapse include access-level permission to request friendship, denying friend requests, and unfriending. While there is limited support for manually assigning different privileges to each friend, the default is to start out the same and many users never change those defaults.

Privacy incidents resulting from mixing work and social media show the importance of why context collapse must be addressed. Context collapse has been shown to negatively affect those seeking employment [ 56 ], as well as endangering those who are employed. For example, a teacher in Massachusetts lost her job because she did not realize her Facebook posts were public to those who were not her friends; her complaints about parents of students getting her sick led to her getting fired from her job [ 57 ]. Many others have shared anecdotes about being fired after controversial Facebook and Twitter posts [ 58 , 59 ]. Even celebrities who live in the public eye can suffer from context collapse [ 60 , 61 ]. Kim Kardashian, for example, received intense criticism from Internet fans when she posted a photo on social media of her daughter using a cellphone and wearing makeup while Kim was getting ready for hair and wardrobe [ 62 ]. Many online users criticized her parenting style for not limiting screen time and Kim subsequently shared a photo of a stack of books that the kids have access to while she works.

Nevertheless, context collapse can also increase bridging social capital, which is the potential social benefit that can come through having ties to a wider audience. Context collapse enables this to occur by allowing people to increase their connections to weak ties and creating serendipitous situations by sharing with people beyond whom one would normally share [ 60 ]. For example, job hunters may increase their chances of finding a job by using social media to network and connect with those they would not normally be associated with on a daily basis. Getting out a message or spreading the word can also be accomplished more easily. For instance, finding people to contribute to natural disaster funds can be effective on social media because multiple contexts can be easily reached from one account [ 63 ]. In addition to managing context collapse, social media users also have to anticipate whether they are sharing disclosures with their intended audiences.

Imagined Audiences

The disconnect between the real audience and the imagined audience on social media poses privacy risks. Understanding who can see what content, how, when, and where is key to deciding what content to share and under what circumstances. Yet, research has consistently demonstrated how users do not accurately anticipate who can potentially see their posts. This manifests as wrongly anticipating that a certain person can see content (when they cannot), as well as not realizing when another person can access posted content. Users have an “imagined audience” [ 64 , 65 ] to whom they are posting their content, but it often does not match the actual audience viewing the user’s content. Social media users typically imagine that the audience for their social media posts are like-minded people, such as family or close friends [ 65 ]. Sometimes, online users think of specific people or groups when creating content such as a daughter, coworkers, people who need cleaning tips, or even one’s deceased father [ 65 ]. Despite these imagined audiences, privacy settings may be set so that many more people can see these posts (acquaintances, strangers, etc.). While users do tend to limit who sees their profile to a defined audience [ 44 , 66 , 67 ], they still tend to believe their posts are more private than they actually are [ 49 , 68 ].

Some users adopt privacy management strategies to counter potential mismatch in audience. Vitak identified several privacy management tactics users employ to disclose information to a limited audience [ 69 ]:

Network-based . Social media users decide who to friend or follow, therefore filtering their network of people. Some Facebook users avoid friending people they do not know. Others set friends’ profiles to “hidden,” so that they do not have to see their posts, but avoid the negative connotations associated with “unfriending.”

Platform-based . Some users choose to use the social media sites’ privacy settings to control who sees their posts. A common approach on Facebook is to change the setting to be “friends only,” so that only a user’s friends may see their posts.

Content-based . These users control their privacy by being careful about the information they post. If they knew that an employer could see their posts, then they would avoid posting when they were at work.

Profile-based . A less commonly used approach is to create multiple accounts (on a single platform or across platforms). For example, a professional, personal, and fun account.

As another example, teenagers often navigate public platforms by posting messages that parents or others would not understand their true meaning. For instance, by posting a song lyric or quote that is only recognized by specific individuals as a reference to a specific movie scene or ironic message, they therefore creatively limit their audience [ 49 , 70 ]. Others manage their audience by using more self-limiting privacy tactics like self-censorship [ 70 ], choosing just to not post something they were considering in the first place. These various tactics allow users to control who can see what on social media in different ways.

3.3 Reputation Management Through Self-Presentation

Technology-mediated interactions have led to new ways of managing how we present ourselves to different groups of friends (e.g., using different profiles on the same platform based on the audience) [ 71 ]. Being able to control the way we come across to others can be a challenging privacy problem that social media users must learn to navigate. Features to limit audience can also help with managing self-presentation. Nonetheless, reputation or impression management is not just about avoiding posts or limiting access to content. Posting more content, such as selfies, is another approach used to control the way others perceive a user [ 72 ]. In this case, it is important to present the content that helps convey a certain image of oneself. Research has revealed that those who engage more in impression management tend to have more online friends and disclose more personal information [ 73 ]. Those who feel online disclosures could leave them vulnerable to negativity, such as individuals who identify as LGBTQ+, have also been found to put an emphasis on impression management in order to navigate their online presence [ 74 ]. However, studies still show that users have anxieties around not having control over how they are presented [ 75 ]. Social media users worry not only about what they post, but are concerned about how others’ postings will reflect on them [ 42 ].

Another dimension that affects impression management attitudes is how social media platforms vary in their policies on whether user profiles must be consistent with their offline identities. Facebook’s real name policy, for instance, requires that people use their real name and represent themselves as one person, corresponding to their offline identities. Research confirms that online profiles actually do reflect users’ authentic personalities [ 76 ]. However, some platforms more easily facilitate identity exploration and have evolved norms encouraging it. For example, Finsta accounts popped up on Instagram a few years after the company started. These accounts are “Fake Instagram” accounts often sharing content that the user does not want to associate with their more public identity, allowing for more identity exploration. This may have arisen from the social norm that has evolved where Instagram users often feel like they need to present an ideal self. Scholars have observed such pressure on Instagram more than on other platforms like Snapchat [ 77 ]. While the ability to craft an online image separate from one’s offline identity may be more prevalent on platforms like Instagram, certain types of social media such as location-sharing social networks are deeply tied to one’s offline self, sharing actual physical location of its users. Users of Foursquare, a popular location-sharing app, have leveraged this tight coupling for impression management. Scholars have observed that users try to impress their friends or family members about the places they spend their time while skipping “check-in” at places like McDonald’s or work for fear of appearing boring or unimpressive [ 78 ].

Regardless of how tightly one’s online presence corresponds with their offline identity, concerns about self-presentation can arise. For example, users may lie about their location on location-sharing platforms as an impression management tactic and have concerns about harming their relationships with others [ 79 ]. On the other hand, Finstas are meant to help with self-presentation by hiding one’s true identity. Ironically, the content posted may be even more representative of the user’s attitudes and activities than the idealized images on one’s public-facing account. These contrasting examples illustrate how self-presentation concerns are complicated.

What further complicates reputation management is that social media content is shared and consumed by a group of people and not just individuals or dyads. Thus, self-presentation is not only controlled by the individual, but by others who might post pictures and/or tag that individual. Even when friends/followers do not directly post about the user, their actions can reflect on the user just by virtue of being connected with them. The issues of co-owned data and how to negotiate disclosure rules are a key area of privacy research on the rise. We refer you to Chap. 6 , which goes in-depth on this topic.

3.4 Access to Oneself

A final privacy challenge many social media users encounter is controlling accessibility others have to them. Some social media platforms automatically display when someone is online, which may invite interaction whether users want to be accessible or not. Controlling access to oneself is not as straightforward as limiting or blocking certain people’s access. For instance, studies have also shown that social pressures influence individuals to accept friend requests from “weak ties” as well as true friends [ 53 , 80 ]. As a result, the social dynamics on social media are becoming more complex, creating social anxiety and drama for many social media users [ 52 , 53 , 80 ]. Although a user may want to control who can interact with him or her, they may be worried about how using privacy features such as “blocking” other accounts may send the wrong signal to others and hurt their relationships [ 81 ]. In fact, an online social norm called “hyperfriending” [ 82 ] has developed where only 25% of a user’s online connections represent true friendship [ 83 ]. This may undermine the privacy individuals wished they had over who interacts with them on their various accounts. Due to social norms or etiquette, users may feel compelled to interact with others online [ 84 ]. Even if users do not feel like they need to interact, they can sometimes get annoyed or overwhelmed by seeing too much information from others [ 85 ]. Their mental state is being bombarded by an overload of information, and they may feel their attention is being captured.

Many social media sites now include location-sharing features to be able to tell people where they are by checking in to various locations, tag photos or posts, or even share location in real time. Therefore, privacy issues may also arise when sharing one’s location on social media and receiving undesirable attention. Studies point out user concerns about how others may use knowledge of that location to reach out and ask to meet up, or even to physically go find the person [ 86 ]. In fact, research has found that people may not be as concerned about the private nature of disclosing location as they are concerned for disturbing others or being disturbed oneself as a result of location sharing [ 87 ]. This makes sense given that analysis of mobile phone conversations reveals that describing one’s location plays a big role in signaling availability and creating social awareness [ 87 , 88 ].

Some scholars focus on the potential harm that may come because of sharing their location. Tsai et al. surveyed people about perceived risks and found that fear of potential stalkers is one of the biggest barriers to adopting location-sharing services [ 89 ]. Nevertheless, studies have also found that many individuals believe that the benefits of using location sharing outweigh the hypothetical costs. Foursquare users have expressed fears that strangers could use the application to stalk them [ 78 ]. These concerns may explain why users share their location more often with close relationships [ 37 ].

Geotagging is another area of privacy concern for online users. Geotagging is when media (photo, website, QR codes) contain metadata with geographical information. More often the information is longitudinal and latitudinal coordinates, but sometimes even time stamps are attached to photos people post. This poses a threat to individuals that post online without realizing that their photos can reveal sensitive information. For example, one study assessed Craigslist postings and demonstrated how they could extract location and hours a person would likely be home based on a photo the individual listed [ 90 ]. The study even pinpointed the exact home address of a celebrity TV host based on their posted Twitter photos. Researchers point out how many users are unaware that their physical safety is at risk when they post photos of themselves or indicate they are on vacation [ 22 , 90 , 91 ]. Doing so may make them easy targets for robbers or stalkers to know when and where to find them.

3.5 Privacy Paradox

While researchers have investigated these various privacy attitudes, perceptions, and behaviors, the privacy paradox (where behavior does not match with stated privacy concerns) has been especially salient on social media [ 92 , 93 , 94 , 95 , 96 , 97 ]. As a result, much research focuses on understanding the decision-making process behind self-disclosure [ 98 ]. Scholars that view disclosure as a result of weighing the costs and the benefits of disclosing information use the term “privacy calculus” to characterize this process [ 99 ]. Other research draws on the theory of bounded rationality to explain how people’s actions are not fully rational [ 100 ]. They are often guided by heuristic cues which do not necessarily lead them to make the best privacy decisions [ 101 ]. Indeed, a large body of literature has tried to dispel or explain the privacy paradox [ 94 , 102 , 103 ].

4 Reconceptualizing Social Media Privacy as Boundary Regulation

By reconceptualizing privacy in social media as a boundary regulation , we can see that the seeming paradox in privacy is actually a balance between being too open or disclosing too much and being too inaccessible or disclosing too little. The latter can result in social isolation which is privacy regulation gone wrong.

In the context of social media, there are five different types of privacy boundaries that should be considered.

People use various methods of coping with privacy violations , many not tied to disclosing less information.

Drawing from Altman’s theories of privacy in the offline world (see Chap. 2 ), Palen and Dourish describe how, just like in the real world, social media privacy is a boundary regulation process along various dimensions besides just disclosure [ 104 ]. Privacy can also involve regulating interactional boundaries with friends or followers online and the level of accessibility one desires to those people. For example, if a Facebook user wants to limit the people that can post on their wall, they can exclude certain people. Research has identified other threats to interpersonal boundary regulation that arise out of the unique nature of social media [ 42 ]. First, as mentioned previously, the threat to spatial boundaries occurs because our audiences are obscured so that we no longer have a good sense of whom we may be interacting with. Second, temporal boundaries are blurred because any interaction may now occur asynchronously at some time in the future due to the virtual persistence of data. Third, multiple interpersonal spaces are merging and overlapping in a way that has caused a “steady erosion of clearly situated action” [ 5 ]. Since each space may have different and, at times, mutually exclusive behavioral requirements, acting accordingly within those spaces has become more of a challenge to manage context collapses [ 42 ]. Along with these problems, a major interpersonal boundary regulation challenge is that social media environments often take control of boundary regulation away from the end users. For instance, Facebook’s popular “Timeline” automatically (based on an obscure algorithm) broadcasts an individual’s content and interactions to all of his or her friends [ 41 ]. Thus, Facebook users struggle to keep up to date on how to manage interactions within these spaces as Facebook, not the end user, controls what is shared with whom.

4.1 Boundary Regulation on Social Media

One conceptualization of privacy that has become popular in the recent literature is viewing privacy on social media as a form of interpersonal boundary regulation. These scholars have characterized privacy as finding the optimal or appropriate level of privacy rather than the act of withholding self-disclosures. That is, it is just as important to avoid over disclosing as it is to avoid under disclosing. Therefore, disclosure is considered a boundary that must be regulated so that it is not too much or too little. Petronio’s communication privacy management (CPM) theory emphasizes how disclosing information (see Chap. 2 ) is vital for building relationships, creating closeness, and creating intimacy [ 105 ]. Thus, social isolation and loneliness resulting from under disclosure can be outcomes of privacy regulation gone wrong just as much as social crowding can be an issue. Similarly, the framework of contextual integrity explains that context-relative informational norms define privacy expectations and appropriate information flows and so a disclosure in one context (such as your doctor asking you for your personal medical details) may be perfectly appropriate in that context but not in another (such as your employer asking you for your personal medical details) [ 106 ]. Here it is not just about an information disclosure boundary but about a relationship boundary where the appropriate disclosure depends on the relationship between the discloser and the recipient.

Drawing on Altman’s theory of boundary regulation, Wisniewski et al. created a useful taxonomy detailing the various types of privacy boundaries that are relevant for managing one’s privacy on social media [ 107 ]. They identified five distinct privacy boundaries relevant to social media:

Relationship . This involves regulating who is in one’s social network as well as appropriate interactions for each relationship type.

Network . This consists of regulating access to one’s social connections as well as interactions between those connections.

Territorial . This has to do with regulating what content comes in for personal consumption and what is available in interactional spaces.

Disclosure . The literature commonly focuses on this aspect which consists of regulating what personal and co-owned information is disclosed to one’s social network.

Interactional . This applies to regulating potential interaction with those within and outside of one’s social network.

Of these boundary types, Wisniewski et al. emphasize the most important is maintaining relationship boundaries between people. Similarly, Child and Petronio note that “one of the most obvious issues emerging from the impact of social network site use is the challenge of drawing boundary lines that denote where relationships begin and end” [ 108 ]. Making sure that social media facilitates behavior appropriate to each of the user’s relationships is a major challenge.

Each of these interpersonal boundaries can be further classified into regulation of more fine-grained dimensions. In Table 7.1 , we summarize the different ways that each of these five interpersonal boundaries can be regulated on social media.

Next, we describe each of these interpersonal boundaries in more detail.

Self- and Confidant Disclosures

The information disclosure concerns described in the previous “Privacy Challenges” section are the focus of privacy around disclosure boundaries. Posting norms on social media platforms often encourage the disclosure of one’s personal information (e.g., age, sexual orientation, location, personal images) [ 109 , 110 ]. Disclosing such information can leave one open to financial, personal, and professional risks such as identity theft [ 46 , 111 ]. However, there are motivations for disclosing personal information. For example, research suggests that posting behaviors on social media platforms have a significant relationship with a desire for positive self-presentation [ 112 , 113 ]. Privacy management is necessary for balancing the benefits of disclosure and its associated risks. This involves regulating both self-disclosure for information about one’s self and confidant-disclosure boundaries for information that is “co-owned” with others [ 105 ] (e.g., a photograph that includes other people, or information about oneself that is shared with another in confidence).

There are a variety of disclosure boundary regulation mechanisms on social media interfaces. Many platforms offer users the freedom to selectively share various types of information, create personal biographies, share links to their websites, or post their birthday. Self-disclosure can also be maintained through privacy settings such as granular control over who has access to specific posts. The level of information one wishes to disclose could be managed by various privacy settings. Many social media platforms encourage multiparty participation with features such as tagging, subtweeting, or replying to others’ posts. This level of engagement promotes the celebration of shared moments or co-owned information/content. At the same time, it increases possibilities for breaching confidentiality and can create unwanted situations such as posting congratulations to a pregnancy that has not yet been announced to most family members or friends. Some ways that people manage violations of disclosure boundaries are to reactively confront the violator in private or to stop using the platform after the unexpected disclosure [ 114 ].

Relationship Connection and Context

Relationship boundaries have to do with who the user accepts into his or her “friend group” and consequently shapes the nature of online interactions within a person’s social network. Social media platforms have embedded the idea of “friend-based privacy” where information and interactional access is primarily dependent on one’s connections. The structure of one’s network can affect the level of engagement and the types of disclosures made on a platform. Individuals with more open relationship boundaries may have higher instances of weak ties compared to others who may employ stricter rules for including people into their inner circles. For example, studies have found people who engage in “hyper-adding,” namely, adding a significant number of persons to their network which could result in a higher distribution of “weak ties” [ 53 , 82 ].

After users accept friends and make connections, they must manage overlapping contexts such as work, family, or acquaintances. This leads to the types of privacy issues discussed under “Context Collapse” in the previous “Privacy Challenges” section. Research shows that boundary violations are hardly remedied by blocking or unfriending unless in extreme cases [ 115 ]. Furthermore, users rarely organize their friends into groups (and some social media platforms do not offer that functionality) [ 114 ]. People are either unaware of the feature, think it takes too much time, or are concerned that the wrong person would still see their information. As a result, users often feel they have to sacrifice being authentic online to control their privacy.

Network Discovery and Interaction

An individual’s social media network is often public knowledge, and there are advantages and disadvantages of having friends being aware of one’s social connections (aka friends list or followers). Network boundary mechanisms enable people to identify groups of people and manage interactions between the various groups. We highlight two types of network boundaries, namely, network discovery and network intersection boundaries. First, network discovery boundaries are primarily centered around the act of regulating the type of access others have to one’s network connections. Implementing an open approach to network discovery boundaries may create problems that may arise including competition as competitors within the same industry could steal clients by carefully selecting from a publicly facing friend list. Another issue arises when a person’s friend does not have a good reputation and that connection is negatively received by others within that social group. Sometimes the result is positive, for example, when friends or family find they have mutual connections, thus building social capital. Some social media platforms offer the ability to hide friend groups from everyone.

Network intersection boundaries involve the regulation of the interactions among different friend groups within one’s social network. Social media users have expressed the benefits of engaging in discourse online with people who they may not personally know offline [ 116 ]. In contrast, clashes within one’s friend list due to opposing political views or personal stances could create tensions that would make moderating a post difficult. These boundaries could be harder to control and sometimes lead to conflict if one is forced to choose which friends can participate in discussions.

Inward- and Outward-Facing Territories

Territorial boundaries include “places and objects in the environment” to indicate “ownership, possession, and occasional active defense” [ 117 ]. Within social media, there are features that are either inward-facing territories or outward-facing territories. Inward-facing territories are commonly characterized as spaces where users could find updates on their friends and see the content their connections were posting (such as the “news feed” on Facebook or “updates” on LinkedIn). To control their inward-facing territories, individuals could hide posts from specific people, adjust their privacy settings, and use filters to find specific information.

These territories are constantly being updated with photos, videos, and news articles that are personalized and not public facing which contributes to an overall low priority for territorial management [ 114 ]. Most choose to ignore content that is irrelevant to them rather than employing privacy features. In addition, once privacy features are used to hide content from particular friends, users rarely revisit that decision to reconsider including content within that territory from that person.

It is important to note that the key characteristic of outward-facing territory management is the regulation of potentially unsatisfactory interactions rather than a fear of information exposure. One example of an outward-facing territory is Facebook’s wall/timeline, where a person’s friend may contribute to your social media presence. Outward-facing territories fall between a public and private place, which creates more risk of unintended boundary violations. Altman argues that “because of their semipublic quality [outward-facing territories] often have unclear rules regarding their use and are susceptible to encroachment by a variety of users, sometimes inappropriately and sometimes predisposing to social conflict” [ 117 ]. Similar to confidant disclosure described above, connections may post (unwanted) content on a user’s wall that could lead to turbulence if that content is later deleted.

Interactional Disabling and Blocking

Interactional boundaries limit the need for other boundary regulations discussed because a person reduces access to oneself by disabling features [ 114 ]. For example, a user may deactivate Facebook Messenger to avoid receiving messages but reactivate the app when they deem that interaction to be welcomed. In a similar regard, disabling semipublic features of the interface (such as the wall on Facebook) could assist users in having a greater sense of control. This manifestation of interaction withdrawal is typically not directed at reducing interaction with a specific person; rather, it may be motivated by a high desire to control one’s online spaces. As such, disabling features are associated with perceptions of mistrust within one’s network and a desire to limit interruptions [ 115 ]. On the more extreme end, blocking could also be employed to regulate interactional boundaries. Unlike other withdrawal mechanisms such as disabling your wall, picture tagging, or chat, blocking is inherently targeted. The act represents the rejection and revocation of access to oneself from a particular party. Some social media platforms allow users to block other people or pages, meaning that the blocked person may not contact or interact with the user in any form. Generally, blocking a person results from a negative experience such as stalking or being bombarded with unwanted content [ 118 ].

4.2 Coping with Social Media Privacy Violations

Overtime, many social media platforms have implemented new privacy features that attempt to address evolving privacy risks and users’ need for more granular control online. While this effort is commendable, Ellison et al. argue that “privacy behaviors on social networking sites are not limited to privacy settings” [ 41 ]. Thus, social media users still venture outside the realm of privacy settings to achieve appropriate levels of social interactions. Coping mechanisms can be viewed as behaviors utilized to maintain or regain interpersonal boundaries [ 107 ]. Although these coping approaches may often be suboptimal, Wisniewski et al.’s framework of coping strategies for maintaining one’s privacy provides insight into the struggles many social media users face in maintaining these boundaries.

This approach is often defined as the “reduction of intensity of inputs” [ 117 ]. Filtering includes selecting whom one will accept into their online social circle and is often used in the management of relational boundaries. Filtering techniques may include relying on social cues (e.g., viewing the profile picture or examining mutual friends) before confirming the addition of a new connection. Other methods leverage non-privacy-related features that are repurposed to manage interactions based on relation context, for example, creating multiple accounts on the same platform to separate professional connections from personal friends.

The vast amount of information on social media could easily become overwhelming and difficult to consume. Therefore, social media users may opt to ignore posts or skim through information to decide which ones should receive priority for engagement. Ignoring is most common for inward-facing territories such as your “Feed” page. The overreliance on this approach might increase the chances of missing critical moments that connections shared.

Blocking is a more extreme approach to interactional boundary management compared to filtering and ignoring, which contributes to lower levels of reported usage [ 119 ]. As an alternative, users have developed other technology-supported mechanisms that would allow them to avoid unwanted interactions. As an example, Wisniewski et al. describe using pseudonyms on Facebook to make it more difficult to find a user on the platform [ 107 ]. Another method for blocking unwanted interactions is to use the account of a close friend or loved one to enjoy the benefits of the content on the platform without the hassle of expected interactions. Page et al. highlight this type of secondary use for those who avoid social media because of social anxieties, harassment, and other social barriers [ 120 ].

When some users feel they are losing control, they withdraw from social media by doing one of the following: deleting their account, censoring their posts, or avoiding confrontation. As a result, a common technique is limiting or adjusting the information shared (even avoiding posts that may be received negatively) [ 121 ]. Das and Kramer found that “people with more boundaries to regulate censor more; people who exercise more control over their audience censor more content; and, users with more politically and age diverse friends censor less, in general” [ 122 ]. Withdrawal suggests that some users think the risks outweigh the benefits of social media.

Unlike offensive coping mechanisms such as filtering, blocking, or withdrawal, social media users resort to more defensive mechanisms when the intention is to create interactions that may be confrontational. Aggressive behavior is displayed when the goal is to seek revenge or garner attention from specific people or groups. Some users may choose to exploit subliminal references in their posts to indirectly address or offend specific persons (e.g., an ex-partner, coworker, family member).

Compliance is giving in to pressures (external or internal) and adjusting one’s interpersonal boundary preferences for others. Altman describes this as “repeated failures to achieve a balance between achieved and desired levels of privacy” [ 117 ]. Relinquishing one’s interactional privacy needs to accommodate pressures of disclosure, nondisclosure, or friending preferences could result in a perceived loss of control over social interactions.

A healthy strategy for managing social media boundary violations is communicating with the other person involved and finding a resolution. Prior work indicates that most users that compromise do so offline [ 107 ]. These compromises are mostly with closer friends who the user can contact through email, phone call, or messaging. These more private scenarios avoid other people becoming involved online. Also, many compromises are about tagging someone in photos or sharing personal information about another user (i.e., confidant disclosure).

In addition to this coping framework for social media privacy, Stutzman examined the creation of multiple profiles on social media websites, primarily Facebook, as an information regulation mechanism. Through grounded theory, he identified three types of information boundary regulation within this context (pseudonymity, practical obscurity, and transparent separations) and four overarching motives for these mechanisms (privacy, identity, utility, and propriety) [ 71 ]. Lampinen et al. created a framework of strategies for managing private versus public disclosures. It defined three dimensions by which strategies differed: behavioral vs. mental, individual vs. collaborative, and preventative vs. corrective [ 71 , 123 ]. The various coping frameworks conceptualize privacy as a process of interpersonal boundary regulation. However, they do not solve the problem of managing privacy on these platforms. They do attempt to model the complexity of privacy management in a way that better reflects the complex nature of interpersonal relationships rather than as a matter of withholding versus disclosing private information.

5 Addressing Privacy Challenges

Rather than just measuring privacy concerns, researchers and designers should focus on understanding attitudes towards boundary regulation. Validated tools for measuring boundary preservation concern and boundary enhancement expectations are provided in this chapter.

Privacy features need to be designed to account for individual differences in how they are perceived and used. While some feel features like untag, unfriend, and delete are useful, others are worried about how using such features will impact their relationships.

Unaddressed privacy concerns can serve as a barrier to using social media. It is crucial to design for not only functional privacy concerns (e.g., being overloaded by information, guarding from inappropriate data access) but social privacy concerns as well (e.g., unwelcome interactions, pressures surrounding appropriate self-presentation).

This section describes how to better identify privacy concerns by measuring them from a boundary regulation perspective. We also emphasize the importance of individual differences when designing privacy features. Finally, we elaborate on a crucial set of social privacy issues that we feel are a priority to address. While many social media users may feel these types of social pressures to some degree, these problems have pushed some of society’s most vulnerable to complete abandonment of social media despite their desire for social connection. We call on social media designers and researchers to focus on these problems which are a side effect of the technologies we have created.

5.1 Understanding People and Their Privacy Concerns

Understanding social media privacy as a boundary regulation allows us to better conceptualize people’s attitudes and behaviors. It helps us anticipate their concerns and balance between too little or too much privacy. However, many existing tools for measuring privacy come from the information privacy perspective [ 124 , 125 , 126 ] and focus on data collection by organizations, errors, secondary use, or technical control of data. In detailing the various types of privacy boundaries that are relevant for managing one’s privacy on social media, Wisniewski et al. [ 114 ] emphasized that the most important is maintaining relationship boundaries between people.

Page et al. [ 86 , 127 ] similarly found that concerns about damaging relationship boundaries are actually at the root of low-level privacy concerns such as worrying about who sees what, being too accessible, or being bothered or bothering others by sharing too much information. For instance, a typically cited privacy concern such as being worried about a stranger knowing one’s current location turns out to be a privacy concern only if an individual expects that a stranger might violate typical relationship expectations. Their research revealed that many people were unconcerned about strangers knowing their location and explained that no one would care enough to use that information to come find them. They did not expect anyone to violate relationship boundaries and so were privacy unconcerned. On the other hand, those who felt there was a likelihood of someone using their location for nefarious purposes were privacy concerned. Social media enabling a negative change in relationship boundaries and the types of interactions that are now possible (such as strangers now being able to locate me) drives privacy concerns.

In fact, while scholars have used many lower-level privacy concerns such as being worried about sharing information to predict social media usage and adoption, they have met with mixed success leading to the commonly observed privacy paradox. However, research shows that preserving one’s relationship boundaries is at the root of these low-level online privacy concerns (e.g., informational, psychological, interactional, and physical privacy concerns) and is a significant predictor of social media usage [ 86 , 127 ]. In other words, concerns about social media damaging one’s relationships (aka relationship boundary regulation) are what drives privacy concerns.

5.2 Measuring Privacy Concerns

Boundary regulation plays a key role in maintaining the right level of privacy on social media, but how do we evaluate whether a platform is adequately supporting it? A popular scale for testing users’ awareness of secondary access is the Internet Users’ Information Privacy Concerns (IUIPC) scale, which measures their perceptions of collection, control, and awareness of user data [ 125 ]. An important finding is that users “want to know and have control over their information stored in marketers’ databases.” This indicates that social media should be designed such that people know where their data goes. However, throughout this chapter, it is evident that research on social media privacy has found concerns about social privacy more salient. In fact, the focus on relationship boundaries is a key privacy boundary to consider and measure in evaluating privacy concerns. Thus, having a scale to measure relationship boundary regulation would allow researchers and designers to better evaluate social media privacy.

Here we present validated relationship boundary regulation survey items developed by Page et al. which predict adoption and usage for various social media including Facebook, Twitter, LinkedIn, Instagram, and location-sharing social media [ 127 , 128 ]. These survey items can be used to evaluate privacy concerns for use of existing social media platforms, as well as capturing attitudes about new features or platforms. The survey items capture attitudes about one’s ability to regulate relationship boundaries when using a social media platform and are administered with a 7-point Likert scale (−3 = Disagree Completely, −2 = Disagree Mostly, −1 Disagree Slightly, 0 = Neither agree nor disagree, 1 = Agree Slightly, 2 = Agree Mostly, 3 = Agree Completely). These items measure both concerns and positive expectations.

When evaluating a new or existing social media platform, the relationship boundary preservation concern (BPC) items can be used to gauge user’s concerns about harming their relationships. A higher score would indicate that more support for privacy management is needed on a given platform. The relationship boundary enhancement expectation (BEE) items can also be used to evaluate whether users expect that using the platform will improve the user’s relationships. A high score is important to driving adoption and usage – having low concerns alone is not enough to drive usage. Along similar lines, even if users have high concerns, they may be counteracted by a perceived high level of benefits and so users remain frequent users of a platform. For instance, Facebook, one of the most widely used platforms, was shown to both invoke high levels of concern as well as high levels of enhancement expectation [ 127 ]. However, note that high frequency of use does not necessarily mean high levels of engagement (e.g., posting, commenting) or that users do not employ suboptimal workarounds (e.g., being vague in their posts) [ 81 ]. On the other hand, Twitter has a higher level of concerns compared to perceived enhancement and, accordingly, lower levels of usage [ 127 ].

In the validation studies, the set of survey items representing BPC were treated as a scale and factor analysis used to compute a single score. Similarly, the ones representing BEE were used to generate a single factor score to represent that construct. These could be used to evaluate new features or platforms in the lab or after deployment. For instance, after performing tasks on a new feature or platform, the user can answer these questions and the designer can compare the responses between different designs in A/B testing, or to predict usage frequency and adoption intentions (e.g., see [ 127 , 129 ] for detailed examples). Moreover, by correlating BPC or BEE with demographics or other customer segmentations (e.g., age, whether they are new customers, purpose for using the platform), product designers may be able to identify attitudes that are connected with certain segments of their customer base and address it directly.

5.3 Designing Privacy Features

When designing for privacy features, a crucial aspect to consider is individual differences. Privacy is not one-size-fits-all: there are many variations in how people feel, what they expect, and how they behave. Because social media connects individuals with diverse needs and expectations, and from a myriad of contexts, a necessity in addressing social media privacy is understanding individual differences in privacy attitudes and behaviors. Many individual differences have been identified that shape privacy needs and preferences [ 15 ] and behaviors [ 6 , 24 , 99 ].

Scholars have established that privacy as a construct is not limited to informational privacy (i.e., understanding the flow of data) but also includes social privacy concerns that may be more interactional (e.g., accessibility) or psychological in nature (e.g., self-presentation) [ 111 , 130 ]. Thus, a host of attitudes and experiences could shape an individual’s view on what it means to have privacy online. For example, people’s preferences for privacy tools could be heavily influenced by the type of data being shared or the recipient of that data [ 36 , 131 , 132 ]. Likewise, prior experiences (negative or positive) could shape how people interact online which could affect disclosure [ 133 ]. Context and relevance have also been found to significantly influence privacy behavior online. Drawing from the contextual integrity framework, many researchers argue that when people perceive data collection to be reasonable or appropriate, they are more likely to share information [ 134 ]. On the other hand, research has shown that when faced with uncomfortable scenarios, people employ privacy protective behaviors such as nondisclosure or falsifying information [ 135 ]. Research has also pointed to personal characteristics that could shape digital privacy behavior such as personality, culture, gender, age, and social norms [ 64 , 106 , 136 , 137 , 138 , 139 , 140 ].

While identifying concerns about damaging one’s relationships is important to measure, understanding the individual differences that can lead someone to be concerned can provide insight into addressing these concerns. For instance, through a series of investigations, Page et al. uncovered a communication style that predicts concerns about preserving relationship boundaries on many different social media platforms [ 127 , 128 , 129 ]. This communication style is characterized by wanting to put information out there so that the individual does not need to proactively inform others. Those who prefer an FYI (For Your Information) communication style are less concerned about relationship boundary preservation and, as a result, exhibit higher levels of engagement, interactions, and use of social media than low FYI communicators. For example, the survey items that capture an FYI communication style preference for location-sharing social media are: “I want the people I know to be aware of my location, without having to bother to tell them,” “I would prefer to make my location available to the people I know, so that they can see it whenever they need it,” and “The people I know should be able to get my location whenever they feel they need it.” Each item is administered with a 7-point Likert scale (Disagree strongly, Disagree moderately, Disagree slightly, Neutral, Agree slightly, Agree moderately, Agree strongly). For other social media platforms, the information type is adjusted (i.e., “what I’m up to” instead of “my location”).

Consequently, this raises concern over implications for non-FYI communicators since the design of major social media platforms is catered to FYI communicators [ 127 , 128 ]. Drawing on this insight, Page demonstrated how considering the user’s communication style when designing location-sharing social media interfaces can alleviate boundary preservation concerns [ 129 ]. Certain design choices such as choosing a request-based location-sharing interaction can lower concerns for non-FYI communicators, while continuous location-sharing and check-in type interactions that are typical in social media may be fine for FYI communicators.

This demonstrates that researchers should consider in the design of social media individual differences that affect privacy attitudes. Another individual difference in attitudes towards privacy features is a user’s apprehension that using common features such as untag, delete, or unfriend/unfollow can act as a hindrance in their relationships with others. Page et al. identified that while many use privacy features and perceive them as a tool useful for protecting their privacy, there are also many who are concerned about how using privacy features could hurt their relationships with others (e.g., being worried about offending others by untagging or unfriending) [ 81 ]. Instead, those individuals would use alternative privacy management tactics such as vaguebooking (not sharing specific details and using vague posts). Designers need to be aware that privacy features also need to be catered to individual variations in attitudes as well or else they may be ineffective and unused by certain segments of the user population.

5.4 Privacy Concerns and Social Disenfranchisement

A significant amount of research within the domain of social media nonuse has been focused on functional barriers that hinder adoption. In many cases, nonuse is traced to a lack of access (e.g., limited access to technology, financial resources, or the Internet). However, the push against adoption and subsequent usage can be voluntary [ 141 ] due to functional privacy concerns such as concerns about data breaches, information overload, or annoying posts [ 120 ]. Several social media companies have also implemented features such as time limits to help users counter overuse [ 142 ].

Likewise, it is equally important to consider social barriers that prevent social media engagement for people who really could use the social connection. Sharing about distressing experiences can be beneficial and reduce stigma, improve connection and interpersonal relationships with one’s network, and enhance well-being [ 6 , 7 , 143 , 144 ]. However, Page et al. identified a class of barriers that highlight social privacy concerns rooted in social anxiety or concerns about being overly influenced by others on social media. This is in contrast to the prior school of thought that focused primarily on functional motivations as barriers that influence nonuse (see Fig. 7.1 ) [ 120 ]. They point out that many who are already vulnerable avoid social media due to social barriers such as online harassment or paralysis over making decisions pertaining to online social interactions. Yet, they are also the ones who could benefit greatly from social connection and who end up losing touch with friends and social support by being off social media. They term this lose-lose situation of negative social consequences that arise when using social media as well as consequences from not using it, social disenfranchisement . They call on designers to address such social barriers and to realize that in designing the user experience to connect users so well, they are implicitly designing the nonuser experience of being left out. Given that social media usage may not always be a viable option, designers should design to alleviate the negative consequences of nonuse.

figure 1

Extension of Wyatt’s frame that divided nonusers along the dimensions of whether someone has used the technology in the past and the motivation for adoption (extrinsic, e.g., organizationally imposed, versus intrinsic, e.g., desire to communicate through technology). Page et al. differentiate between functional motivations/barriers of use (which has been the focus of much research) versus social motivations/barriers to use. Other frameworks consider additional temporal states of adoption (whether they are currently using and whether they will in the future). See [ 120 ] for more detailed descriptions

5.5 Guidelines for Designing Privacy-Sensitive Social Media

Now that you have learned about various privacy problems related to social media use, how do you apply that to designing or studying social media? Here are some practical guidelines.

Identifying Privacy Attitudes

Measuring privacy attitudes is a tricky task. Using existing informational privacy scales, users often say they are concerned, but this does not end up matching their actual behavior. By approaching it from a boundary regulation perspective, it will be easier to identify the proper balance between sharing too much and sharing too little. The survey items described in this chapter offer a way to measure concerns about boundary regulation as well as positive expectations. Considering both are key to more accurately predicting user behaviors.

Understanding Your Target Population

Some key characteristics are described in this chapter. Identifying these in your target population can help you be aware of individual differences that might affect privacy preferences on social media. When you are measuring privacy concerns, matching the preferences of your audience makes it more likely that they will have a good user experience. Pay particular attention to traits that have been identified as being related to usage and adoption of social media platforms, such as the FYI communication style which can be measured using the survey items provided in this chapter.

Evaluating Privacy Features

Focus on understanding whether users perceive your privacy features as useful or perhaps as posing a relational hindrance. The survey items provided in this chapter can help you do so. When anticipating privacy needs of your social media users, make sure you identify features that may impact boundary regulation both positively and negatively. You can compare attitudes between the existing feature and the newer version of the feature that will/has been deployed. You can also correlate attitudes towards privacy features with individual characteristics – some subpopulation of users may see privacy features as useful, while others may consider them a relational hindrance.

6 Chapter Summary

Social media has been widely adopted and quickly become an integral part of social, personal, economic, political, professional, and instrumental welfare. Understanding how mediated social interactions change the assumptions around audience management, disclosure, and self-presentation is key to working towards reconciling offline privacy assumptions with new realities. Moreover, given the rapidly changing landscape of widely available social media platforms, researchers and designers need to continually re-evaluate the privacy implications of new services, features, and interaction modalities.

With the rise of networked individualism, an especially strong emphasis must be placed on understanding individual characteristics and traits that can shape a user’s privacy expectations and needs. Given the inherently social nature of social media, understanding social norms and the influence of larger cultural and structural factors is also important for interpreting expectations of privacy and the significance around various social media behaviors.

Privacy does not have a one-size-fits-all solution. It is a normative construct that is context dependent and can change over time, from culture to culture, and person to person. It needs to be weighed across different individuals and against other important goals and values of the larger group or society. Because people and their social interactions can be complex, designing for social media privacy is usually not a straightforward task. However, the consequences of not addressing privacy issues can range from irritating to devastating. Using this chapter as a guide and taking the steps to think through privacy needs and expectations of your social media users is an integral part of designing for social media.

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The Effects of Social Media on Academic Performance among PUP Parañaque Students Chapter 1 1.1. Introduction

Profile image of Arron Lexter Tiongkiao

Social media is a tool or platform that is mainly used to share, exchange, gather, learn, and create different kinds of data and information like ideas, interests, thoughts, consider even photos and videos. This allows almost everyone; sometimes, companies, and other organizations to use different platforms of social media like Facebook, Instagram, Twitter, and YouTube to use it as a tool to surf or promote and share on the internet. Not only it is used to create, gather, and share information, it is also used as a platform to give or provide leisure and entertainment. Different Social Media applications provides a vast range of entertainment and leisure based options like Facebook, where they offer different type of games that varies depending on what the user wants, and in YouTube where you can watch a wide range of videos, social media applications are undeniably entertaining but it also has its own setbacks, for example, these social media sites are very addicting to the users specially to the youth. There are a lot of people that uses different social media applications and majority of them tends to spend more hours on social media sites than it is recommended resulting into various unwanted consequences. The main purpose of our research is to determine how these Social Media sites are greatly affecting the academic performance of students on PUP Parañaque Campus. 1.2. Background of the Study Social Media is one of the backbones of the internet. It plays a big role on the internet as whole. Nowadays these social media site has been widely used as a medium of learning, socializing, and sharing. Various institutions are also using social media sites to create their own communities and group. One of the great percent that participates on the social media users are youths, mainly focused ranging from high school to college students. Social media sites engage them on socializing with their fellow students and also giving them the ability to gather and learn of new information. The use of Social Media sites has evolved with the growth on its applications. From the roots of its use-to be used as a socializing platform and sharing, it has evolved from an activity to a phenomenon. The social media sites evolved from only a socializing platform to an application that is now used as a pivotal tool on some people's lives where they depend on the sites to use it as an app to form communities, share ideas and interests, chatting, and even blogging. Not only a person is using it as a tool, some people consider it as part of their lives.

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Social networking sites have become a trend now-a-days to communicate throughout the world. These are online internet based media where people can communicate with each other, and share anything they want. Social Media is very popular among the youth, many researches were carried on to study the impact of social media on youth and their education. Studies of social media usage have focused strictly on its impact on dynamics that have been shown in effect on grades of college student engagement and involvement. These studies stop short of assessing the direct effect of social media use on grades, but prior research on traditional forms of academic engagement and involvement has emphasized the role of these dynamics in influencing GPA and other academic outcomes. This study explores the advantages and disadvantages of student's use of social networking for study. Findings indicated that social network connect them with one another so that they not even bother to solve their home tasks and they contact elders and seniors to help them in cloning their educational material. The study was conducted through descriptive survey method on sample of 180 undergraduate students from Sukkur and nearby areas including male and female and were selected on the basis of randomized techniques of sampling from different government and private universities. Out of 180 questionnaires, 160 questionnaires are returned to the investigator .Of which 8 questionnaire are rejected due to incomplete data. The study has revealed that despite the benefits that come with the participation of students on social media networks, it could impact negatively on their academic performance if not used properly. A lot of benefits abound in the use of social media networks such as sharing information and ideas, improving reading skills etc. Despite the benefits that comes with the participation of students on social media networks, its misuse could affect the academic life of the student and thereby their performance.

Interal Res journa Managt Sci Tech

Within the past decade or so, social media such as Twitter, Facebook, MySpace, YouTube, Flickr, Yahoo Messenger, LinkedIn, Whats app messenger, Skype, Google talk, Google Messenger and others have grown at a very fast pace. As per a statistics report from a website statista.com, there are 2.6 billion social media users in the world as compared to around 1 billion users in the year 2010. It is expected that the number of social media users will cross 3 billion by 2021. This report also claims that of the total internet users, 71% are social media users and Facebook is the leader in social media applications with 1.86 billion active users. Social media have inevitably become an integral part of the contemporary classroom, of advertising and public relations industries, of political campaigning, and of numerous other aspects of our daily existence. This research paper presents the both negative and positive sides of the effects of social media on students and their academic performance.

calqus wutos

Social networking media has been the major source of communication between individuals in the world over, hence, the label cyber-world. This includes Facebook, Twitter, MySpace, Instagram, Flicker, Frienster, Blogs, Podcast, Youtube, Tumblr and Skype, among others. Users of these forms of media made use of such technology gadgets as cell phones, tablets, laptops, desktop computers, and e-readers. Researchers all over the world have varied findings on the effects of these forms of media on the academic performance of students. Those students who used the media wisely, their academic performance improved. However, those who failed to regulate their use of these social networking tools negatively affected their studies which oftentimes led to their addicted use. In general, the study found out that the exposure of the IMEAS students to the social networking media positively affected their academic performance. Hence, the University must implement policies and projects designed for more easy access of the students to the Facebook network site in the school campus. In contrast, there is also this very disturbing finding of the study which disclosed that the students of IMEAS, University of Southern Mindanao, Kabacan, Cotabato used the social networking media almost daily since majority of them answered to have used said form of media 5-6 days a week at an average of about 1-2 hours every session. With this data, it is recommended that the USM must regulate the proper time usage by the students of social networking media in the campus in order not to destruct with their classroom activities.

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Today social media networks such as Facebook, Twitter, Youtube, Whatsapp, Instagram etc. become an integral part of youth's life. Youth cannot imagine themselves without using social media network. They are active on social media from early in the morning to late night. Students use social media networks in the examination periods also. These new social communication channels have been adopted by all the age groups in India. Social media have a significant impact on the society especially on the youth. Social media networks have negative as well as positive impact on our society. It is important to know the positive and negative impact of social networking sites and applications on today's young generation. It is also important to know the benefits of social networking for youth. This paper is an attempt to study the impact of social networking sites and applications on young generation. It is a result of a survey conducted on youth of Jalgaon and Dhule Districts. The sample size of 100 respondents was obtained by distributing well structured questionnaires. Convenience sampling method was used. The scope of the study was limited to the youth of Jalgaon and Dhule district. The result shows that there is a significant impact of social media sites and applications on today's youth. It is also seen that there are benefits of social networks for youth. This study also describes that there were some drawbacks of social networking.

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Social media research: We are publishing more but with weak influence

Affiliations.

  • 1 Department of Marketing, National University of Singapore, Singapore, Singapore.
  • 2 Department of Business Administration, Holy Spirit University of Kaslik, Jounieh, Lebanon.
  • PMID: 38330028
  • PMCID: PMC10852228
  • DOI: 10.1371/journal.pone.0297241

The purpose of this paper is to address the chasm between academic research on social media as an expanding academic discipline and at the same time a growing marketing function. A bibliometric analysis indicated the evolution of academic research on social media. The results of a survey of 280 social media practitioners shed the light on the gap between academic social media research and the practice of professionals. A qualitative study also offered novel insights and recommendations for future developments in academic research on social media. The findings of this paper showed that academic research on social media is growing in terms of the number of publications but is struggling in three areas: visibility, relevance, and influence on practitioners. This study contributes to the body of knowledge on social media. The implications of our study are derived from the importance of our findings on the directions to publish more relevant and timely academic research on social media. While extensive studies exist on social media, their influence on practitioners is still limited.

Copyright: © 2024 Elhajjar, Yacoub. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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  • Published: 19 February 2024

‘It depends’: what 86 systematic reviews tell us about what strategies to use to support the use of research in clinical practice

  • Annette Boaz   ORCID: orcid.org/0000-0003-0557-1294 1 ,
  • Juan Baeza 2 ,
  • Alec Fraser   ORCID: orcid.org/0000-0003-1121-1551 2 &
  • Erik Persson 3  

Implementation Science volume  19 , Article number:  15 ( 2024 ) Cite this article

Metrics details

The gap between research findings and clinical practice is well documented and a range of strategies have been developed to support the implementation of research into clinical practice. The objective of this study was to update and extend two previous reviews of systematic reviews of strategies designed to implement research evidence into clinical practice.

We developed a comprehensive systematic literature search strategy based on the terms used in the previous reviews to identify studies that looked explicitly at interventions designed to turn research evidence into practice. The search was performed in June 2022 in four electronic databases: Medline, Embase, Cochrane and Epistemonikos. We searched from January 2010 up to June 2022 and applied no language restrictions. Two independent reviewers appraised the quality of included studies using a quality assessment checklist. To reduce the risk of bias, papers were excluded following discussion between all members of the team. Data were synthesised using descriptive and narrative techniques to identify themes and patterns linked to intervention strategies, targeted behaviours, study settings and study outcomes.

We identified 32 reviews conducted between 2010 and 2022. The reviews are mainly of multi-faceted interventions ( n  = 20) although there are reviews focusing on single strategies (ICT, educational, reminders, local opinion leaders, audit and feedback, social media and toolkits). The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. Furthermore, a lot of nuance lies behind these headline findings, and this is increasingly commented upon in the reviews themselves.

Combined with the two previous reviews, 86 systematic reviews of strategies to increase the implementation of research into clinical practice have been identified. We need to shift the emphasis away from isolating individual and multi-faceted interventions to better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice. This will involve drawing on a wider range of research perspectives (including social science) in primary studies and diversifying the types of synthesis undertaken to include approaches such as realist synthesis which facilitate exploration of the context in which strategies are employed.

Peer Review reports

Contribution to the literature

Considerable time and money is invested in implementing and evaluating strategies to increase the implementation of research into clinical practice.

The growing body of evidence is not providing the anticipated clear lessons to support improved implementation.

Instead what is needed is better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice.

This would involve a more central role in implementation science for a wider range of perspectives, especially from the social, economic, political and behavioural sciences and for greater use of different types of synthesis, such as realist synthesis.

Introduction

The gap between research findings and clinical practice is well documented and a range of interventions has been developed to increase the implementation of research into clinical practice [ 1 , 2 ]. In recent years researchers have worked to improve the consistency in the ways in which these interventions (often called strategies) are described to support their evaluation. One notable development has been the emergence of Implementation Science as a field focusing explicitly on “the scientific study of methods to promote the systematic uptake of research findings and other evidence-based practices into routine practice” ([ 3 ] p. 1). The work of implementation science focuses on closing, or at least narrowing, the gap between research and practice. One contribution has been to map existing interventions, identifying 73 discreet strategies to support research implementation [ 4 ] which have been grouped into 9 clusters [ 5 ]. The authors note that they have not considered the evidence of effectiveness of the individual strategies and that a next step is to understand better which strategies perform best in which combinations and for what purposes [ 4 ]. Other authors have noted that there is also scope to learn more from other related fields of study such as policy implementation [ 6 ] and to draw on methods designed to support the evaluation of complex interventions [ 7 ].

The increase in activity designed to support the implementation of research into practice and improvements in reporting provided the impetus for an update of a review of systematic reviews of the effectiveness of interventions designed to support the use of research in clinical practice [ 8 ] which was itself an update of the review conducted by Grimshaw and colleagues in 2001. The 2001 review [ 9 ] identified 41 reviews considering a range of strategies including educational interventions, audit and feedback, computerised decision support to financial incentives and combined interventions. The authors concluded that all the interventions had the potential to promote the uptake of evidence in practice, although no one intervention seemed to be more effective than the others in all settings. They concluded that combined interventions were more likely to be effective than single interventions. The 2011 review identified a further 13 systematic reviews containing 313 discrete primary studies. Consistent with the previous review, four main strategy types were identified: audit and feedback; computerised decision support; opinion leaders; and multi-faceted interventions (MFIs). Nine of the reviews reported on MFIs. The review highlighted the small effects of single interventions such as audit and feedback, computerised decision support and opinion leaders. MFIs claimed an improvement in effectiveness over single interventions, although effect sizes remained small to moderate and this improvement in effectiveness relating to MFIs has been questioned in a subsequent review [ 10 ]. In updating the review, we anticipated a larger pool of reviews and an opportunity to consolidate learning from more recent systematic reviews of interventions.

This review updates and extends our previous review of systematic reviews of interventions designed to implement research evidence into clinical practice. To identify potentially relevant peer-reviewed research papers, we developed a comprehensive systematic literature search strategy based on the terms used in the Grimshaw et al. [ 9 ] and Boaz, Baeza and Fraser [ 8 ] overview articles. To ensure optimal retrieval, our search strategy was refined with support from an expert university librarian, considering the ongoing improvements in the development of search filters for systematic reviews since our first review [ 11 ]. We also wanted to include technology-related terms (e.g. apps, algorithms, machine learning, artificial intelligence) to find studies that explored interventions based on the use of technological innovations as mechanistic tools for increasing the use of evidence into practice (see Additional file 1 : Appendix A for full search strategy).

The search was performed in June 2022 in the following electronic databases: Medline, Embase, Cochrane and Epistemonikos. We searched for articles published since the 2011 review. We searched from January 2010 up to June 2022 and applied no language restrictions. Reference lists of relevant papers were also examined.

We uploaded the results using EPPI-Reviewer, a web-based tool that facilitated semi-automation of the screening process and removal of duplicate studies. We made particular use of a priority screening function to reduce screening workload and avoid ‘data deluge’ [ 12 ]. Through machine learning, one reviewer screened a smaller number of records ( n  = 1200) to train the software to predict whether a given record was more likely to be relevant or irrelevant, thus pulling the relevant studies towards the beginning of the screening process. This automation did not replace manual work but helped the reviewer to identify eligible studies more quickly. During the selection process, we included studies that looked explicitly at interventions designed to turn research evidence into practice. Studies were included if they met the following pre-determined inclusion criteria:

The study was a systematic review

Search terms were included

Focused on the implementation of research evidence into practice

The methodological quality of the included studies was assessed as part of the review

Study populations included healthcare providers and patients. The EPOC taxonomy [ 13 ] was used to categorise the strategies. The EPOC taxonomy has four domains: delivery arrangements, financial arrangements, governance arrangements and implementation strategies. The implementation strategies domain includes 20 strategies targeted at healthcare workers. Numerous EPOC strategies were assessed in the review including educational strategies, local opinion leaders, reminders, ICT-focused approaches and audit and feedback. Some strategies that did not fit easily within the EPOC categories were also included. These were social media strategies and toolkits, and multi-faceted interventions (MFIs) (see Table  2 ). Some systematic reviews included comparisons of different interventions while other reviews compared one type of intervention against a control group. Outcomes related to improvements in health care processes or patient well-being. Numerous individual study types (RCT, CCT, BA, ITS) were included within the systematic reviews.

We excluded papers that:

Focused on changing patient rather than provider behaviour

Had no demonstrable outcomes

Made unclear or no reference to research evidence

The last of these criteria was sometimes difficult to judge, and there was considerable discussion amongst the research team as to whether the link between research evidence and practice was sufficiently explicit in the interventions analysed. As we discussed in the previous review [ 8 ] in the field of healthcare, the principle of evidence-based practice is widely acknowledged and tools to change behaviour such as guidelines are often seen to be an implicit codification of evidence, despite the fact that this is not always the case.

Reviewers employed a two-stage process to select papers for inclusion. First, all titles and abstracts were screened by one reviewer to determine whether the study met the inclusion criteria. Two papers [ 14 , 15 ] were identified that fell just before the 2010 cut-off. As they were not identified in the searches for the first review [ 8 ] they were included and progressed to assessment. Each paper was rated as include, exclude or maybe. The full texts of 111 relevant papers were assessed independently by at least two authors. To reduce the risk of bias, papers were excluded following discussion between all members of the team. 32 papers met the inclusion criteria and proceeded to data extraction. The study selection procedure is documented in a PRISMA literature flow diagram (see Fig.  1 ). We were able to include French, Spanish and Portuguese papers in the selection reflecting the language skills in the study team, but none of the papers identified met the inclusion criteria. Other non- English language papers were excluded.

figure 1

PRISMA flow diagram. Source: authors

One reviewer extracted data on strategy type, number of included studies, local, target population, effectiveness and scope of impact from the included studies. Two reviewers then independently read each paper and noted key findings and broad themes of interest which were then discussed amongst the wider authorial team. Two independent reviewers appraised the quality of included studies using a Quality Assessment Checklist based on Oxman and Guyatt [ 16 ] and Francke et al. [ 17 ]. Each study was rated a quality score ranging from 1 (extensive flaws) to 7 (minimal flaws) (see Additional file 2 : Appendix B). All disagreements were resolved through discussion. Studies were not excluded in this updated overview based on methodological quality as we aimed to reflect the full extent of current research into this topic.

The extracted data were synthesised using descriptive and narrative techniques to identify themes and patterns in the data linked to intervention strategies, targeted behaviours, study settings and study outcomes.

Thirty-two studies were included in the systematic review. Table 1. provides a detailed overview of the included systematic reviews comprising reference, strategy type, quality score, number of included studies, local, target population, effectiveness and scope of impact (see Table  1. at the end of the manuscript). Overall, the quality of the studies was high. Twenty-three studies scored 7, six studies scored 6, one study scored 5, one study scored 4 and one study scored 3. The primary focus of the review was on reviews of effectiveness studies, but a small number of reviews did include data from a wider range of methods including qualitative studies which added to the analysis in the papers [ 18 , 19 , 20 , 21 ]. The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. In this section, we discuss the different EPOC-defined implementation strategies in turn. Interestingly, we found only two ‘new’ approaches in this review that did not fit into the existing EPOC approaches. These are a review focused on the use of social media and a review considering toolkits. In addition to single interventions, we also discuss multi-faceted interventions. These were the most common intervention approach overall. A summary is provided in Table  2 .

Educational strategies

The overview identified three systematic reviews focusing on educational strategies. Grudniewicz et al. [ 22 ] explored the effectiveness of printed educational materials on primary care physician knowledge, behaviour and patient outcomes and concluded they were not effective in any of these aspects. Koota, Kääriäinen and Melender [ 23 ] focused on educational interventions promoting evidence-based practice among emergency room/accident and emergency nurses and found that interventions involving face-to-face contact led to significant or highly significant effects on patient benefits and emergency nurses’ knowledge, skills and behaviour. Interventions using written self-directed learning materials also led to significant improvements in nurses’ knowledge of evidence-based practice. Although the quality of the studies was high, the review primarily included small studies with low response rates, and many of them relied on self-assessed outcomes; consequently, the strength of the evidence for these outcomes is modest. Wu et al. [ 20 ] questioned if educational interventions aimed at nurses to support the implementation of evidence-based practice improve patient outcomes. Although based on evaluation projects and qualitative data, their results also suggest that positive changes on patient outcomes can be made following the implementation of specific evidence-based approaches (or projects). The differing positive outcomes for educational strategies aimed at nurses might indicate that the target audience is important.

Local opinion leaders

Flodgren et al. [ 24 ] was the only systemic review focusing solely on opinion leaders. The review found that local opinion leaders alone, or in combination with other interventions, can be effective in promoting evidence‐based practice, but this varies both within and between studies and the effect on patient outcomes is uncertain. The review found that, overall, any intervention involving opinion leaders probably improves healthcare professionals’ compliance with evidence-based practice but varies within and across studies. However, how opinion leaders had an impact could not be determined because of insufficient details were provided, illustrating that reporting specific details in published studies is important if diffusion of effective methods of increasing evidence-based practice is to be spread across a system. The usefulness of this review is questionable because it cannot provide evidence of what is an effective opinion leader, whether teams of opinion leaders or a single opinion leader are most effective, or the most effective methods used by opinion leaders.

Pantoja et al. [ 26 ] was the only systemic review focusing solely on manually generated reminders delivered on paper included in the overview. The review explored how these affected professional practice and patient outcomes. The review concluded that manually generated reminders delivered on paper as a single intervention probably led to small to moderate increases in adherence to clinical recommendations, and they could be used as a single quality improvement intervention. However, the authors indicated that this intervention would make little or no difference to patient outcomes. The authors state that such a low-tech intervention may be useful in low- and middle-income countries where paper records are more likely to be the norm.

ICT-focused approaches

The three ICT-focused reviews [ 14 , 27 , 28 ] showed mixed results. Jamal, McKenzie and Clark [ 14 ] explored the impact of health information technology on the quality of medical and health care. They examined the impact of electronic health record, computerised provider order-entry, or decision support system. This showed a positive improvement in adherence to evidence-based guidelines but not to patient outcomes. The number of studies included in the review was low and so a conclusive recommendation could not be reached based on this review. Similarly, Brown et al. [ 28 ] found that technology-enabled knowledge translation interventions may improve knowledge of health professionals, but all eight studies raised concerns of bias. The De Angelis et al. [ 27 ] review was more promising, reporting that ICT can be a good way of disseminating clinical practice guidelines but conclude that it is unclear which type of ICT method is the most effective.

Audit and feedback

Sykes, McAnuff and Kolehmainen [ 29 ] examined whether audit and feedback were effective in dementia care and concluded that it remains unclear which ingredients of audit and feedback are successful as the reviewed papers illustrated large variations in the effectiveness of interventions using audit and feedback.

Non-EPOC listed strategies: social media, toolkits

There were two new (non-EPOC listed) intervention types identified in this review compared to the 2011 review — fewer than anticipated. We categorised a third — ‘care bundles’ [ 36 ] as a multi-faceted intervention due to its description in practice and a fourth — ‘Technology Enhanced Knowledge Transfer’ [ 28 ] was classified as an ICT-focused approach. The first new strategy was identified in Bhatt et al.’s [ 30 ] systematic review of the use of social media for the dissemination of clinical practice guidelines. They reported that the use of social media resulted in a significant improvement in knowledge and compliance with evidence-based guidelines compared with more traditional methods. They noted that a wide selection of different healthcare professionals and patients engaged with this type of social media and its global reach may be significant for low- and middle-income countries. This review was also noteworthy for developing a simple stepwise method for using social media for the dissemination of clinical practice guidelines. However, it is debatable whether social media can be classified as an intervention or just a different way of delivering an intervention. For example, the review discussed involving opinion leaders and patient advocates through social media. However, this was a small review that included only five studies, so further research in this new area is needed. Yamada et al. [ 31 ] draw on 39 studies to explore the application of toolkits, 18 of which had toolkits embedded within larger KT interventions, and 21 of which evaluated toolkits as standalone interventions. The individual component strategies of the toolkits were highly variable though the authors suggest that they align most closely with educational strategies. The authors conclude that toolkits as either standalone strategies or as part of MFIs hold some promise for facilitating evidence use in practice but caution that the quality of many of the primary studies included is considered weak limiting these findings.

Multi-faceted interventions

The majority of the systematic reviews ( n  = 20) reported on more than one intervention type. Some of these systematic reviews focus exclusively on multi-faceted interventions, whilst others compare different single or combined interventions aimed at achieving similar outcomes in particular settings. While these two approaches are often described in a similar way, they are actually quite distinct from each other as the former report how multiple strategies may be strategically combined in pursuance of an agreed goal, whilst the latter report how different strategies may be incidentally used in sometimes contrasting settings in the pursuance of similar goals. Ariyo et al. [ 35 ] helpfully summarise five key elements often found in effective MFI strategies in LMICs — but which may also be transferrable to HICs. First, effective MFIs encourage a multi-disciplinary approach acknowledging the roles played by different professional groups to collectively incorporate evidence-informed practice. Second, they utilise leadership drawing on a wide set of clinical and non-clinical actors including managers and even government officials. Third, multiple types of educational practices are utilised — including input from patients as stakeholders in some cases. Fourth, protocols, checklists and bundles are used — most effectively when local ownership is encouraged. Finally, most MFIs included an emphasis on monitoring and evaluation [ 35 ]. In contrast, other studies offer little information about the nature of the different MFI components of included studies which makes it difficult to extrapolate much learning from them in relation to why or how MFIs might affect practice (e.g. [ 28 , 38 ]). Ultimately, context matters, which some review authors argue makes it difficult to say with real certainty whether single or MFI strategies are superior (e.g. [ 21 , 27 ]). Taking all the systematic reviews together we may conclude that MFIs appear to be more likely to generate positive results than single interventions (e.g. [ 34 , 45 ]) though other reviews should make us cautious (e.g. [ 32 , 43 ]).

While multi-faceted interventions still seem to be more effective than single-strategy interventions, there were important distinctions between how the results of reviews of MFIs are interpreted in this review as compared to the previous reviews [ 8 , 9 ], reflecting greater nuance and debate in the literature. This was particularly noticeable where the effectiveness of MFIs was compared to single strategies, reflecting developments widely discussed in previous studies [ 10 ]. We found that most systematic reviews are bounded by their clinical, professional, spatial, system, or setting criteria and often seek to draw out implications for the implementation of evidence in their areas of specific interest (such as nursing or acute care). Frequently this means combining all relevant studies to explore the respective foci of each systematic review. Therefore, most reviews we categorised as MFIs actually include highly variable numbers and combinations of intervention strategies and highly heterogeneous original study designs. This makes statistical analyses of the type used by Squires et al. [ 10 ] on the three reviews in their paper not possible. Further, it also makes extrapolating findings and commenting on broad themes complex and difficult. This may suggest that future research should shift its focus from merely examining ‘what works’ to ‘what works where and what works for whom’ — perhaps pointing to the value of realist approaches to these complex review topics [ 48 , 49 ] and other more theory-informed approaches [ 50 ].

Some reviews have a relatively small number of studies (i.e. fewer than 10) and the authors are often understandably reluctant to engage with wider debates about the implications of their findings. Other larger studies do engage in deeper discussions about internal comparisons of findings across included studies and also contextualise these in wider debates. Some of the most informative studies (e.g. [ 35 , 40 ]) move beyond EPOC categories and contextualise MFIs within wider systems thinking and implementation theory. This distinction between MFIs and single interventions can actually be very useful as it offers lessons about the contexts in which individual interventions might have bounded effectiveness (i.e. educational interventions for individual change). Taken as a whole, this may also then help in terms of how and when to conjoin single interventions into effective MFIs.

In the two previous reviews, a consistent finding was that MFIs were more effective than single interventions [ 8 , 9 ]. However, like Squires et al. [ 10 ] this overview is more equivocal on this important issue. There are four points which may help account for the differences in findings in this regard. Firstly, the diversity of the systematic reviews in terms of clinical topic or setting is an important factor. Secondly, there is heterogeneity of the studies within the included systematic reviews themselves. Thirdly, there is a lack of consistency with regards to the definition and strategies included within of MFIs. Finally, there are epistemological differences across the papers and the reviews. This means that the results that are presented depend on the methods used to measure, report, and synthesise them. For instance, some reviews highlight that education strategies can be useful to improve provider understanding — but without wider organisational or system-level change, they may struggle to deliver sustained transformation [ 19 , 44 ].

It is also worth highlighting the importance of the theory of change underlying the different interventions. Where authors of the systematic reviews draw on theory, there is space to discuss/explain findings. We note a distinction between theoretical and atheoretical systematic review discussion sections. Atheoretical reviews tend to present acontextual findings (for instance, one study found very positive results for one intervention, and this gets highlighted in the abstract) whilst theoretically informed reviews attempt to contextualise and explain patterns within the included studies. Theory-informed systematic reviews seem more likely to offer more profound and useful insights (see [ 19 , 35 , 40 , 43 , 45 ]). We find that the most insightful systematic reviews of MFIs engage in theoretical generalisation — they attempt to go beyond the data of individual studies and discuss the wider implications of the findings of the studies within their reviews drawing on implementation theory. At the same time, they highlight the active role of context and the wider relational and system-wide issues linked to implementation. It is these types of investigations that can help providers further develop evidence-based practice.

This overview has identified a small, but insightful set of papers that interrogate and help theorise why, how, for whom, and in which circumstances it might be the case that MFIs are superior (see [ 19 , 35 , 40 ] once more). At the level of this overview — and in most of the systematic reviews included — it appears to be the case that MFIs struggle with the question of attribution. In addition, there are other important elements that are often unmeasured, or unreported (e.g. costs of the intervention — see [ 40 ]). Finally, the stronger systematic reviews [ 19 , 35 , 40 , 43 , 45 ] engage with systems issues, human agency and context [ 18 ] in a way that was not evident in the systematic reviews identified in the previous reviews [ 8 , 9 ]. The earlier reviews lacked any theory of change that might explain why MFIs might be more effective than single ones — whereas now some systematic reviews do this, which enables them to conclude that sometimes single interventions can still be more effective.

As Nilsen et al. ([ 6 ] p. 7) note ‘Study findings concerning the effectiveness of various approaches are continuously synthesized and assembled in systematic reviews’. We may have gone as far as we can in understanding the implementation of evidence through systematic reviews of single and multi-faceted interventions and the next step would be to conduct more research exploring the complex and situated nature of evidence used in clinical practice and by particular professional groups. This would further build on the nuanced discussion and conclusion sections in a subset of the papers we reviewed. This might also support the field to move away from isolating individual implementation strategies [ 6 ] to explore the complex processes involving a range of actors with differing capacities [ 51 ] working in diverse organisational cultures. Taxonomies of implementation strategies do not fully account for the complex process of implementation, which involves a range of different actors with different capacities and skills across multiple system levels. There is plenty of work to build on, particularly in the social sciences, which currently sits at the margins of debates about evidence implementation (see for example, Normalisation Process Theory [ 52 ]).

There are several changes that we have identified in this overview of systematic reviews in comparison to the review we published in 2011 [ 8 ]. A consistent and welcome finding is that the overall quality of the systematic reviews themselves appears to have improved between the two reviews, although this is not reflected upon in the papers. This is exhibited through better, clearer reporting mechanisms in relation to the mechanics of the reviews, alongside a greater attention to, and deeper description of, how potential biases in included papers are discussed. Additionally, there is an increased, but still limited, inclusion of original studies conducted in low- and middle-income countries as opposed to just high-income countries. Importantly, we found that many of these systematic reviews are attuned to, and comment upon the contextual distinctions of pursuing evidence-informed interventions in health care settings in different economic settings. Furthermore, systematic reviews included in this updated article cover a wider set of clinical specialities (both within and beyond hospital settings) and have a focus on a wider set of healthcare professions — discussing both similarities, differences and inter-professional challenges faced therein, compared to the earlier reviews. These wider ranges of studies highlight that a particular intervention or group of interventions may work well for one professional group but be ineffective for another. This diversity of study settings allows us to consider the important role context (in its many forms) plays on implementing evidence into practice. Examining the complex and varied context of health care will help us address what Nilsen et al. ([ 6 ] p. 1) described as, ‘society’s health problems [that] require research-based knowledge acted on by healthcare practitioners together with implementation of political measures from governmental agencies’. This will help us shift implementation science to move, ‘beyond a success or failure perspective towards improved analysis of variables that could explain the impact of the implementation process’ ([ 6 ] p. 2).

This review brings together 32 papers considering individual and multi-faceted interventions designed to support the use of evidence in clinical practice. The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. Combined with the two previous reviews, 86 systematic reviews of strategies to increase the implementation of research into clinical practice have been conducted. As a whole, this substantial body of knowledge struggles to tell us more about the use of individual and MFIs than: ‘it depends’. To really move forwards in addressing the gap between research evidence and practice, we may need to shift the emphasis away from isolating individual and multi-faceted interventions to better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice. This will involve drawing on a wider range of perspectives, especially from the social, economic, political and behavioural sciences in primary studies and diversifying the types of synthesis undertaken to include approaches such as realist synthesis which facilitate exploration of the context in which strategies are employed. Harvey et al. [ 53 ] suggest that when context is likely to be critical to implementation success there are a range of primary research approaches (participatory research, realist evaluation, developmental evaluation, ethnography, quality/ rapid cycle improvement) that are likely to be appropriate and insightful. While these approaches often form part of implementation studies in the form of process evaluations, they are usually relatively small scale in relation to implementation research as a whole. As a result, the findings often do not make it into the subsequent systematic reviews. This review provides further evidence that we need to bring qualitative approaches in from the periphery to play a central role in many implementation studies and subsequent evidence syntheses. It would be helpful for systematic reviews, at the very least, to include more detail about the interventions and their implementation in terms of how and why they worked.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Before and after study

Controlled clinical trial

Effective Practice and Organisation of Care

High-income countries

Information and Communications Technology

Interrupted time series

Knowledge translation

Low- and middle-income countries

Randomised controlled trial

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Metz A, Jensen T, Farley A, Boaz A, et al. Is implementation research out of step with implementation practice? Pathways to effective implementation support over the last decade. Implement Res Pract. 2022;3:1–11. https://doi.org/10.1177/26334895221105585 .

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Acknowledgements

The authors would like to thank Professor Kathryn Oliver for her support in the planning the review, Professor Steve Hanney for reading and commenting on the final manuscript and the staff at LSHTM library for their support in planning and conducting the literature search.

This study was supported by LSHTM’s Research England QR strategic priorities funding allocation and the National Institute for Health and Care Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King’s College Hospital NHS Foundation Trust. Grant number NIHR200152. The views expressed are those of the author(s) and not necessarily those of the NIHR, the Department of Health and Social Care or Research England.

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AB led the conceptual development and structure of the manuscript. EP conducted the searches and data extraction. All authors contributed to screening and quality appraisal. EP and AF wrote the first draft of the methods section. AB, JB and AF performed result synthesis and contributed to the analyses. AB wrote the first draft of the manuscript and incorporated feedback and revisions from all other authors. All authors revised and approved the final manuscript.

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Boaz, A., Baeza, J., Fraser, A. et al. ‘It depends’: what 86 systematic reviews tell us about what strategies to use to support the use of research in clinical practice. Implementation Sci 19 , 15 (2024). https://doi.org/10.1186/s13012-024-01337-z

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DOI : https://doi.org/10.1186/s13012-024-01337-z

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research paper chapter 1 about social media

School of Journalism and Mass Communication

Visual media symposium 2024, state of visual evidence symposium.

We invite experts in the field of visual communication to discuss the current challenges and opportunities that synthetic media pose for the contemporary media environment, and how we can utilize visuals as data to answer social scientific questions.

Date: Monday, April 8, 3-6:30 p.m. (central time USA)

Conference Mode: Zoom  

Register here

Opening Remarks: 3-3:15 p.m.

Melissa Tully, Sang Jung Kim, Alex Scott and Bingbing Zhang

Keynote 1: 3:15-4:15 p.m.

Speaker: Bryce Dietrich  

Moderator: Sang Jung Kim

Topic: Video-as-data; Seeing Racial Avoidance on Virtual Streets

Speaker Bio: Dietrich is an Associate Professor of Political Science at Purdue University. He is also a Research Scholar at the Center for C-SPAN Scholarship & Engagement. Previously, he was an Assistant Professor of Social Science Informatics at the University of Iowa and a postdoctoral research fellow at Harvard's Kennedy School and Northeastern University.

Dietrich's research uses novel quantitative, automated, and machine learning methods to analyze non-traditional data sources such as audio (or speech) data and video data. He uses these to understand the causes and consequences of non-verbal political behavior, such as vocal inflections and walking trajectories, especially in relation to descriptive representation and implicit gender/racial bias. Underlying this research is a love for high-performance computing and a genuine desire to make "big data" more accessible, while his substantive interests are firmly grounded in American political behavior at both the mass- and elite-level.

Keynote 2: 4:15-5:15 p.m

Speaker: Cindy Shen  

Moderator: Bingbing Zhang

Topic: Perception, mechanism, and intervention of visual misinformation 

Speaker Bio: Cuihua (Cindy) Shen is a professor of communication at UC Davis and the co-director of the Computational Communication Research lab. Her recent research focuses on computational social science and multimodal (mis)information in AI-mediated environments. She is the past chair of the Computational Methods Division of the International Communication Association, and the founding associate editor of the journal Computational Communication Research , as well as the associate editor of Journal of Computer-Mediated Communication . Her research has been funded by the National Science Foundation and Facebook. She is a recipient of numerous top paper awards from ICA as well as a Fulbright US Scholar Award. 

Keynote 3: 5:15-6:15 p.m.

Q & A with  T. J. Thomson  

Moderator: Alex Scott

Topic: Impact of AI generated images & visual misinformation 

Speaker Bio:   A majority of Thomson's research centers on the visual aspects of news and journalism and on the concerns and processes relevant to those who make, edit, and present visual news. He has broader interests in digital media, journalism studies, and visual culture and often focuses on under-represented identities, attributes, and environments in his research. Thomson is committed to not only studying visual communication phenomena but also working to increase the visibility, innovation, and quality of how research findings are presented, accessed, and understood.

Thomson has obtained more than $1.32 million in external research funding from a number of organizations, including the Australian Academy of the Humanities, the Australian Research Council, the Office of the Queensland Chief Scientist, the University of Nottingham Ningbo China, and the International Visual Literacy Association. He has also been awarded research fellowships in China and Germany.

Closing Remarks: 6:15-6:30 p.m.

Sang Jung Kim, Alex Scott and Bingbing Zhang

Symposium Co-Sponsors

The School of Journalism and Mass Communication and Visual Media Lab would like to thank the symposium co-sponsors for their support of this event:

  • College of Liberal Arts and Sciences
  • Department of Communication Studies
  • Department of Cinematic Arts
  • SPARTA Lab - Department of Computer Science
  • Department of Political Science
  • Public Policy Center
  • The Iowa Initiative for Artificial Intelligence

NOTICE: The University of Iowa Center for Advancement is an operational name for the State University of Iowa Foundation, an independent, Iowa nonprofit corporation organized as a 501(c)(3) tax-exempt, publicly supported charitable entity working to advance the University of Iowa. Please review its full disclosure statement.

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