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Measuring the effect of social media on student academic performance using a social media influence factor model

• Published: 18 July 2022
• Volume 28 , pages 1165–1188, ( 2023 )

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• Mohammed Nurudeen   ORCID: orcid.org/0000-0001-6711-6735 1 ,
• Siddique Abdul-Samad 2 ,
• Emmanuel Owusu-Oware 1 ,
• Godfred Yaw Koi-Akrofi 1 &
• Hannah Ayaba Tanye 1  

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With the advent of smartphones and fourth generation mobile technologies, the effect of social media on society has stirred up some debate and researchers across various disciplines have drawn different conclusions. Social media provides university students with a convenient platform to create and share educational content. However, social media may have an addicting effect that may lead to poor health, poor concentration in class, poor time management and consequently poor academic performance. Using a random sample of 623 students from the University of Professional Studies Accra, Ghana, this paper presents a social media influence factor (SMIF) model for measuring the effect of social media on student academic performance. The proposed model is examined using linear regression analysis and the results show a statistically significant negative relationship between SMIF variables and student grade point average (GPA). The model accounted for 30.7% of the variability in student GPA and it demonstrated a prediction quality of 55.4% given the data collected.

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The datasets generated during and analyzed during the study are available in figshare repository https://doi.org/10.6084/m9.figshare.14905089.v1

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Acknowledgements

We wish to thank the respondents who took the time to respond to this survey and the University of Professional Studies Accra, Ghana

This research was conducted using the researchers’ annual research allowance which is funding given by the Government of Ghana to all academic staffs in Ghanaian public universities. The research was therefore conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Also, no funding body played any role in the design of the study, collection, analysis, and interpretation of data and in writing the manuscript.

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Nurudeen, M., Abdul-Samad, S., Owusu-Oware, E. et al. Measuring the effect of social media on student academic performance using a social media influence factor model. Educ Inf Technol 28 , 1165–1188 (2023). https://doi.org/10.1007/s10639-022-11196-0

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• Published: 22 October 2022

Comparative sensitivity of social media data and their acceptable use in research

• Libby Hemphill   ORCID: orcid.org/0000-0002-3793-7281 1 , 2 ,
• Angela Schöpke-Gonzalez   ORCID: orcid.org/0000-0001-7912-1371 1 &
• Anmol Panda 1  

Scientific Data volume  9 , Article number:  643 ( 2022 ) Cite this article

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• Research data

Social media data offer a rich resource for researchers interested in public health, labor economics, politics, social behaviors, and other topics. However, scale and anonymity mean that researchers often cannot directly get permission from users to collect and analyze their social media data. This article applies the basic ethical principle of respect for persons to consider individuals’ perceptions of acceptable uses of data. We compare individuals’ perceptions of acceptable uses of other types of sensitive data, such as health records and individual identifiers, with their perceptions of acceptable uses of social media data. Our survey of 1018 people shows that individuals think of their social media data as moderately sensitive and agree that it should be protected. Respondents are generally okay with researchers using their data in social research but prefer that researchers clearly articulate benefits and seek explicit consent before conducting research. We argue that researchers must ensure that their research provides social benefits worthy of individual risks and that they must address those risks throughout the research process.

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Introduction.

Researchers have used social media data in myriad ways and through different means. For instance, Twitter, Facebook, Instagram, Reddit, and Wikipedia are the top five platforms used by social media researchers for gathering data 1 . Prior work also enumerates different ways researchers procure social media data, including web scraping using Python or R, services such as NVivo, Discovertext, NodeXL, TAGS, IFTTT, Social Feed Manager, Zapier, Hydrator, WebRecorder.io, platform APIs, and even screenshots 1 , 2 .

Tools for providing access to social media data are evolving. For instance, in early 2022, Twitter released its no-code API, making it possible for researchers to access Twitter data without needing to have programming expertise. Twitter data is now more accessible for a wider range of researchers to study topics like social capital 3 , political agendas 4 , labor economics 5 , and public health 6 . Likewise, the launch of the Twitter Academic API has significantly expanded the scope of Twitter data that researchers can utilize, while also affording access to the full archive of tweets previously available only through the Enterprise version.

Increased data availability makes ethical questions about social media data use for research all the more pressing. For example, should researchers be collecting social media data? If so, when and how ought researchers use it? Legally, social media users grant broad permissions for their data to be used when they agree to platforms’ terms of service (TOS). However, users often do not actually read or understand TOS 7 , may not think of their data as public 8 , and may not realize that researchers are among that public 9 , 10 . Prior work finds that users actually prefer to grant explicit consent to use their data in research despite agreeing to TOS 8 , and their attitudes toward acceptable data uses depend heavily on the research context and goals 11 . How can social media researchers reconcile legal ability to use social media data and its ready availability with individuals’ preferences about their social media data being used for research?

A basic principle in research called respect for persons can bring clarity to how researchers can think about ethical social media data use practices. Respect for persons 12 requires that researchers orient their practices around individuals’ perceptions of acceptable uses of data. Respect for persons is often addressed through informed consent processes that obtain explicit permission from individuals to use their data. Explicit consent serves to inform individuals of the opportunity to have data about them collected for research, and to express their preferences about their data being collected by accepting or declining participation.

Though researchers recognize that informed consent is a consideration, along with balancing risks and benefits and protecting individuals 13 , explicit consent processes with social media users are often infeasible. The scale and anonymity of social media mean that researchers often cannot directly elicit individuals’ perceptions about the acceptable uses of data they generate on social media. One exception is the Documenting the Now Project, which created “Social Humans” labels to attach explicit use permissions to content and analyses from social media 14 . Social Humans labels aim to bridge the gap between the legal permissions users grant when agreeing to platforms’ TOS and content creators’ wishes. However, this method has yet to be widely adopted, meaning even explicit use permissions like Social Humans labels cannot effectively guide researchers about how to ensure that they are respecting individuals’ preferences about use of their data.

To understand how we can realize respect for persons in the particular context of social media data research, we look to scholarship on users’ perceptions about acceptable uses of other types of sensitive yet widely available data. Prior work finds that social media data users think of their social media data similarly to how they think about widely-recognized sensitive data types 15 , 16 like voter files 17 , cell phone records 18 , and large-scale surveys 19 . Comparing individuals’ acceptable use perceptions for their social media data with these other types of sensitive data opens opportunities for us to learn from existing respect-for-persons best practices aside from explicit consent developed to support sensitive data. Our survey study thus addresses the following research questions:

RQ1: How do participants perceptions of acceptable social media data use compare to other types of sensitive data about them?

RQ2: How do participants’ perceptions of acceptable social media data use relate to the data analyst , their purpose for using the data, and perceptions of sensitivity ?

Answering RQ1 allows us to assess social media data’s relative sensitivity, and to understand whether other sensitive data types are an appropriate context from which to seek wisdom about how researchers can approach social media data use. Answering RQ2 allows us to clarify sensitivity and other variables’ relationships to acceptable use. Together, the answers to these questions provide insights for social media researchers about which best practices to follow when working with social media data to ensure respect-for-persons.

Factors Affecting Perceptions of Acceptable Use

Prior work argues that individuals’ willingness to share their personal data is a function of: data sensitivity, who will use the data (data analyst), what users hope to gain and who who else will benefit from using the data (data use purpose), which data will be used (data type), and personal characteristics of the person sharing the data (data sharer characteristics). The following subsections review each of these factors.

Data sensitivity: risk perception

Data sensitivity describes how risky a person perceives sharing their data to be. Prior work on consumer willingness to share data with marketers 15 , 16 identifies four types of risk that may be particularly important for people’s characterization of data as sensitive: monetary, psychological, physical, and social risk. A person’s perceptions of how sensitive a type of data is–or how much risk they perceive they will incur by sharing that type of data–may inform how willing that person may be to share their data. However, while this correlation is implied by marketing literature, the existence and quality of this relationship have yet to be empirically evaluated. Further, whether a person characterizes data about themselves as sensitive is not a fixed characteristic, but rather can change according to the contexts in which data might be used and the data sharer’s personal characteristics 15 , 16 .

Data analyst: known identities

Knowing who will use their data shapes data sharers’ perceptions of acceptable use. People tend to find use of their social media data by known data analysts more acceptable than unknown data analysts. While social media users try to limit who sees and uses their data to only intended audiences, their data are still often seen by unintended or unknown audiences 10 . When people learn that these unintended audiences - among them researchers - use their data, they tend to find this use less acceptable than if their data is used by intended audiences 20 .

Beyond known versus unknown data analysts, other data analyst identities can further affect sharers’ perceptions of acceptable use. For example, Gilbert and colleagues 11 asked respondents to rate the appropriateness of their personal Facebook data’s use for research according to the discipline using it. They found that respondents were more concerned about studies in Computer Science, Gender Studies, and Psychology using their data than studies in Health Sciences. A related study about UK public health research shows that participants were much more willing to share their personal data for research by the UK’s National Health Service than with a commercial company 21 . Aside from explicitly intended audiences, people are most comfortable with their data being used by health researchers relative to other analysts. Overall, whether a data analyst is known or unknown and which discipline or professional domain they are affiliated with can affect individuals’ perceptions of acceptable use.

Data use purpose: benefit and process

Literature suggests that individuals’ perceptions of acceptable use also vary across data use purposes like health research or marketing. For example, a study about UK public health research found that when participants were told that mandating consent could lead to selection bias and adversely impact public health research, participants were more willing to share their data without explicit consent 21 . For both health data 21 , 22 , 23 and social media data 11 , 24 , how much participants believe that sharing their data will contribute to a purpose that will benefit society affects how acceptable they find the use of their data.

In addition to public benefits, people are more likely to find data use acceptable when it offers personal benefits like discounts or personalized service. Researchers studying public conversations about privacy controversies found that many discussants understand themselves and their data as a “product” that for-profit companies use for the purpose of making money, and in return they receive some digital service–a personal benefit 25 . Discussants found this type of data use purpose acceptable. In another example, a study of journalists’ use of social media data suggests that the more social media users want to feel heard, the more likely they are to find journalists’ use of their data acceptable. The personal benefits of receiving digital services and “feeling heard” mediated individuals’ perceptions of different data use purposes’ acceptability 26 .

Beyond general use purpose (e.g., for public health research, for marketing, for public awareness, etc.), people’s understanding of exactly how their social media data will be used also affects their perceptions of its acceptable use. When participants know which analysis methods and data security measures researchers will use, they feel better about their data being used for research 22 . Fiesler and Proferes 25 found that people who indicated understanding how their social media data would be used, including for research, were less concerned about its use relative to those who were unaware of how their data was later used. In general, when individuals are asked explicitly for their permission and understand what research will be conducted (i.e., data use purpose), they usually agree that their social media data can be used in research 8 , 11 .

Data type: keys to personal identity and social networks

In addition to who will use their data and for what purposes, sharers care about which specific data will be used. Two studies examined U.S. consumers’ willingness to share different types of information with marketers 15 , 16 . Using a nationally representative survey, they found that respondents were just as unwilling to share their credit card number, financial account details, and driver’s license information–unique identifiers or keys that are directly tied to a specific individual–as they were to share their social network profile, profile picture, and information about their friends or family. Respondents also considered their social media profile more sensitive–or risky to share–than basic demographics such as height, place of birth, and their occupation–data that when linked to other data can increase the risk that a specific individual can be identified. When they asked respondents to rate the appropriateness of uses of their personal data for research by type of data, researchers found that respondents were most concerned about researchers using their photos and videos, data about sexual habits, data about preferences and behaviours, and posts about their friends or family members 11 . These studies show that people weigh the riskiness and acceptability of sharing various types of social media data differently.

Individual data sharer characteristics

Research reports that perceptions of acceptable data use also vary based on a data sharer’s personal characteristics. For example, studies find that how much people trust institutions–a type of data analyst–affects whether individuals are okay with those institutions using their social media data 8 , 11 , 27 . For both health data 21 , 22 , 23 and social media data 11 , 24 , how much people trust researchers in general also affects how acceptable they find their data’s use. Studies find mixed results concerning the effects on demographic characteristics on perceptions of acceptable use. For example, Fiesler and Proferes 8 found that demographic characteristics have no statistically significant effect on survey respondents’ attitudes toward their Twitter data being used in various types of research. In contrast, Gilbert, Vitak, and Shilton 11 showed that gender, age, education level, and frequency of social media use have significant effects on individuals’ attitudes toward their Facebook data being used in various type of research. A comparative study of sensitivity perceptions in Brazil and the United States found that perceptions do vary based on an individual’s country of residence (i.e., Brazil or the US) and age affect their willingness to share personal data 16 . Others also reported significant effects for sex and education level on willingness to share data in their US-based survey 15 . Finally, literature shows that pre-existing attitudes toward privacy 8 may affect perceptions of acceptable use. These data sharer characteristics can mediate the effects of the data analyst, data use purpose, and data type on individuals’ perceptions of acceptable use.

Summary of factors affecting perceptions of acceptable use

Overall, social media users’ perceptions of of whether using their data is acceptable may be mediated by how sensitive they perceive their data to be, the data analyst, data use purpose, data type, and their personal characteristics. This web of factors shapes the challenges that researchers face in balancing respect for persons with research needs. Our study draws from existing scholarship’s insights on sensitive data and acceptable use to learn specifically about how people feel about their social media data’s use relative to other data types that people have indicated is sensitive to them like health records 21 and location 28 .

To understand participants’ perceptions about acceptable social media data use, we surveyed 1018 people through Qualtrics panels and Mechanical Turk 29 . Table  6 offers summary descriptive statistics about our sample. We used statistical analyses to identify patterns in survey responses, specifically regarding (a) the sensitivity of individuals’ online identifiers relative to other personal identifiers, and (b) whether or not participants perceive a particular data analyst using a specific type of data for a purpose is acceptable or not. Section 5 provides additional details about our survey design, variables, and analysis techniques. We address each research question in its own section below. In each case, we have provided the regression models of best fit.

We calculated cumulative link mixed model (CLMM) using the ordinal package in R 30 to compare the relative sensitivity of a key to one’s social media data - one’s screen name - with other types of similar keys to potentially sensitive data about individuals (e.g., driver’s license number or social security number). We also compare the sensitivity of one’s screen name with data that in aggregate could be linked to a specific individual’s identity (e.g., race, weight). Our results, presented in Table  1 and Fig.  1 , show that a screen name is more sensitive than demographic details such as race (OR = 0.36, p  < 0.001), religion (OR = 0.50 p  < 0.001), and weight (OR = 0.47 p  < 0.001), but less sensitive than identifiers such as a driver’s license number (OR = 4.67, p  < 0.001) or data from one’s medical history (OR = 4.46, p  < 0.001). Figure  2 also shows that individuals exhibited more variation in the sensitivity of their online screen name than other types of data such as fingerprints or medical history that may also provide access to data about their behaviors.

Violin plots showing the distribution, median, and quartiles for sensitivity of various types of data where 10 = ‘very sensitive’.

Odds ratio plot where DV = “is ok” on each data analyst, data type, purpose combination.

Table  1 also indicates that respondents who were men and those with higher trust in institutions were less likely to find their online screen name sensitive. Respondents from Qualtrics who had higher digital privacy concerns, more privacy behaviors, were neither Black nor White, were straight, older, more educated, and/or had a higher income were more likely to find their online screen name sensitive.

RQ2: How do participants’ perceptions of acceptable use relate to data type, data analyst, data use purpose, and sensitivity?

We calculated a mixed effects logistic regression (MELR) using the glmer function from lme4 31 to understand whether respondents indicated that a particular combination of data analyst, data type, and purpose was acceptable. In this model, the dependent variable was whether respondents answered “yes” to a specific question, and the questions were the only fixed effect. We included random effects for respondent and source (Qualtrics or Mechanical Turk). The coefficient for source’s random effects was nearly zero, indicating that between-subject differences based on source could be almost entirely explained by the other variables we assessed in our model. We also included demographic controls and scales for our questions about trust, digital privacy concerns, and privacy behaviors. According to ANOVA analyses, the model of best fit did not include sensitivity as a predictor.

The results (see Table  2 indicate respondents found only one combination clearly unacceptable (i.e., significantly lower odds ratio):

Is it ok for journalists to use posts you’ve deleted from social media in a story about natural disasters?

Overall, respondents said that it was more acceptable to use their social media data than other types of data (see Table  3 ).

However, respondents generally found academic researchers using social media data about them acceptable except for two scenarios (no significant difference between acceptable and not acceptable):

images you’ve uploaded to social media to train facial recognition software?

metadata from your photos in social media to create a public map of peony gardens in your area?

Respondents with higher scores on the digital privacy concern scale were less likely to agree that it their data’s use is acceptable (OR = 0.892). Those who had higher institutional trust scores (OR = 1.041) and privacy behavior scores (OR = 1.112) were more likely to indicate that using their data was acceptable.

Among our demographic controls, only straight (OR = 0.265) and older respondents (OR = 0.618) were less likely to answer that it was acceptable for their data to be used. Men were significantly more likely than women and other gender identities (OR = 1.980) to say it was acceptable for their data to be used. Individuals of races other than White and Black (OR = 2.116) were also more likely to find their data’s use acceptable. We observed no significant effects for income level.

We also fit a model where we collapsed our data type and purpose variables into categories. According to ANOVA analyses, we found that the best model included data type, use purpose, data analyst, behavioral scales, and demographics but not sensitivity as predictors of respondents finding their social media data’s use by researchers acceptable for social/behavioral research. In this model, presented in Table  3 and Fig.  2 , we use academic researchers , social media data , and social research as the reference categories for data analyst, data type, and purpose respectively. We included a random effect for respondent and another for source (Qualtrics or Mechanical Turk). The variance between sources was nearly zero, indicating that between-source differences could be almost entirely explained by the other variables we assessed in our model. Table  3 shows that respondents were more accepting of their data being used for social research than to generate interventions or research about the natural world.

We saw similar patterns among the control variables (trust, digital privacy, privacy behavior, and demographics) in both of our models. Individuals with concerns about digital privacy (e.g., they hesitate to provide information when its requested) were less likely to be accepting of their data being used. Individuals who generally trust institutions and governments accepted their data being used by these entities. Individuals who engaged in more privacy-protecting behaviors (e.g., removing cookies from their web browser, watching for ways to control what emails they receive) were more likely to agree that their data could be used. We find that information about how sensitive someone considers their online screen name does not provide statistically significant information about how acceptable they think a combination of data, user, and purpose are.

Our results suggest that individuals are generally accepting of academic researchers using social media data and that online screen names are less sensitive than many other types of data, including individual identifiers such as driver’s license numbers and medical histories, but more sensitive than height, weight, race, and religion. Individuals indicated that academic researchers using their data was acceptable in more scenarios than journalists or social media companies doing so. Prior work on internet fandom similarly indicated more trust for researchers than for private companies and journalists 32 .

One implication of our findings is that social media researchers can leverage the expertise and practices of researchers who use other types of sensitive or moderately sensitive data such as health records. However, one potential limitation of our study and Gilbert’s 11 is that our survey instruments clearly communicated the user, data, and purpose. In doing so, the instruments may have been specific enough that users found these scenarios acceptable; had we asked more generally about social media researchers using their data, they may not have been as accepting. We address findings about the comparative sensitivity of social media data and the relationships between sensitivity and acceptable data use below.

Sensitivity comparison

Prior work on social media data use in research 11 , 25 examines social media data on its own rather than in the context of private and/or sensitive personal information. Our survey instrument allowed us to compare individuals’ reports about the sensitivity of different types of data so that we can understand how social media data is similar to (or dissimilar from) other data often used in research. Looking at a specific type of social media data - one’s online screen name or the “key” to accessing one’s social media data, similar to one’s “offline” name–respondents suggest that their online screen name is more sensitive than demographic characteristics (e.g., one’s race) and less sensitive than other personally-identifiable data that can be linked to an individual and her behavior (e.g., one’s driver’s license number). Respondents also found online identifiers less sensitive than mental health and health records. There was also more variation in respondents’ perceptions of their online screen name’s sensitivity relative to other types of data like fingerprints or medical history that may also provide access to data about their behaviors. This variability may indicate uncertainty among individuals or true variation in our sample, and future work could attempt to verify this distribution and its causes.

Acceptable use comparison

To evaluate perceptions of social media data’s acceptable use relative to other widely used sensitive data types, we compared social media data with examples like cell phone data and voter files that carry varying re-identification risks. Compared to these other types of sensitive data, respondents were most comfortable with their social media data being used, and least comfortable with their cell phone location data being used. This finding resonates with prior work on location data arguing that individuals expect privacy even in public 28 , and this expectation extends to automatically collected data like location captured by cell phones and social media. Relative to social media data, respondents were also less likely to agree that it’s acceptable to use their voter file or security camera footage of them. While voter files are widely used in political science research 33 , 34 , researchers have made important efforts to protect data sharers’ privacy expectations including disclosure risk mitigation techniques like aggregation to avoid revealing personally identifiable information.

Sensitivity and acceptable use

We found that sensitivity is not a good predictor of whether individuals thought it was acceptable for researchers to use social media data. This result is somewhat unexpected given prior literature that suggested sensitivity mediates acceptable use 16 . One possible explanation is that ‘online screen name’ is not a useful example of social media data to ask about. It is possible, for example, that respondents may not understand how much data can be accessed when one’s online screen name is known (e.g., one’s tweet history or one’s Reddit comment history, which may reveal other types of sensitive data like religion, mental health status, etc.). Another explanation is that for other types of data, sensitivity may predict acceptable use, but for social media data, acceptable use is a function of the data analyst, data type, and purpose of data use 11 .

Concerning data analyst, type, and use purpose, our findings echo earlier results indicating users accept their social media data being used in research when told about who will use it and for what purpose 8 , 11 . Our respondents were generally more accepting of researchers using their data than social media companies or journalists (see Table  2 ). We expect that this pattern holds because users better are able to imagine benefits from research. Given the increasing distrust of journalists in the United States 35 , 36 , it’s not surprising that our respondents did not welcome journalists using their data. In line with prior research about sensitive data use in health research 27 and marketing 16 , our respondents were more comfortable sharing data with researchers looking to produce social benefit and understanding than with for-profit companies using their data for similar purposes.

Given these findings, rather than looking to similarly sensitive data for guidance on respect-for-persons practices with social media data, our research points us to data whose analysts have clearly communicated their use purposes’ benefits and cultivated trust among prospective sharers. In fact, for both our survey and Gilbert, et al .‘s 11 , articulating data use scenarios explicitly may drive much of the acceptance individuals expressed.

Acceptable use and personal characteristics

In our results, men were more likely to report that researchers could use their data without explicit permission, and that most uses of their data were acceptable. Related prior research on individuals’ willingness to share data with marketers found that sensitivity was a function of perceived privacy controls and cultural context such as masculinity values and long-term orientation 16 . The importance of masculinity values (e.g., “ It is more important for men to have a professional career than for women.”) in predicting sensitivity may explain why we observed differences between men and other gender identities. Women and members of gender identity minorities face greater risks when engaging in social media; 37 , 38 those risks may lead them to be more conservative in their data sharing beliefs. As Mikal and colleagues 39 point out, we must carefully consider who may opt-out of using social media publicly whenever we think of social media data.

We also found that older adults and straight respondents oppose the use of their data without explicit permission when asked about research generally. In another study, Dym and Fiesler 32 found in their survey about data from online fandom communities–majority LGBTQ spaces–and their use in research that less than ten percent of their respondents used their real names in online fandom. It is possible that LGBTQ users employ privacy-protecting strategies like pseudonyms to disconnect their online screen names from their offline identities, rendering their screen names less sensitive. It is also possible that members of marginalized demographic groups may be more willing to share their data because they seek inclusion or because they see efforts to avoid surveillance as futile. Benjamin 40 provides a thorough discussion of the differential impacts of surveillance among racial groups, for instance.

Realizing respect for persons in social media research

Our results have two implications for realizing respect for persons in social media research. First, whenever possible, researchers should elicit informed consent from social media users to use their data in research. However, if researchers use data only from those individuals who have provided explicit permission and who are generally accepting of their data being used in research, their data will likely skew male, younger, more educated, and less straight. While bias in data cannot be eliminated and is not inherently bad, demographically-biased social media data limit their utility for population-level studies. Because individuals are generally more comfortable with population-level research than with individual-level research 8 , 20 , this tension is especially important for researchers to consider. Given the burden of obtaining informed consent and the biases it introduces, when it is not possible to obtain, researchers should work to anonymize data as much as possible to reduce the risks of reidentification. Existing work such as Williams, Burnap and Sloan 20 , the AOIR Ethics Guidelines 41 , and the STEP framework 42 provide useful tools for thinking through research processes, when and how to get consent, and how to mitigate risks to individuals.

Second, as Kass et al . 27 suggest, educating the public about why research is important and why it requires their data is vital to ensuring individuals’ comfort with their data being used. As earlier studies 11 , 24 and now our work show, how much data sharers trust researchers affects their perceptions of acceptable use. People who place more trust in institutions were more likely to accept their data being used. We can learn from health research that has been able to explain to individuals how and why their medical records are necessary for understanding diseases such as cancer. People now tend to find their data’s use for health research more acceptable than for other uses 11 , 21 .

Researchers who leverage social media data can engage in similar outreach and engagement efforts to understand individuals’ hesitations and preferences. We do not, however, suggest that researchers try to cajole or coerce potential participants. Instead, social media researchers must work to ensure that their research does actually provide social benefits worthy of individual risks, and that they are consequently able to articulate the significance of their work so that individuals can decide whether that benefit is worth their risks. As Sloan and colleagues 43 argue, the principles outlined in the Belmont Report apply even to social media research, and researchers have a responsibility to ensure that individuals understand their own data, how it could be used, and the risks associated with use.

In one example, Xafis 44 demonstrated how they helped individuals understand how their data could be used and potentially competing interests between researchers and individuals represented in data. Their research showed that individuals could understand data linkage processes and the potential trade-offs quickly. Because it is not feasible to educate each potential participant that might share their social media data, the burden of education lies on researchers collectively. Researchers need to articulate the social benefits of their work so that individuals understand why disclosure and reputation risks are worth taking; if the benefits do not outweigh the risks, researchers need to be willing to abandon or avoid particular projects.

Contextual integrity in social media research

Our work shows that beyond considering individuals’ preferences for their data’s use under a respect for persons framework, social media researchers must also attend to the contexts in which user data are generated and used. Our results confirm that contexts in which user data are used impact individuals’ perceptions of acceptable use. Specifically, they indicate relatively less comfort with their data being used for natural research or to develop interventions than with their use in social research. These findings resonate with earlier work in online fan communities, where fans expressed concerns about the contexts in which their data could be used (e.g., articles about fandom) because their identities carry different risks (e.g., fandom vs physical communities for LGBTQ folks) 32 . It also echoes prior work about Facebook data, specifically, where users expressed comfort with their data being used to improve services but not to evaluate mental health 11 . To address concerns about mismatch between data generation and use contexts, Nissenbaum 45 offers the principle of “contextual integrity” to refer to the challenges inherent in using data generated in one context (e.g., an online discussion) in another (e.g., a research study).

Legal constructs that impact social media data use

Recent developments in regulations, such as the EU General Data Protection Regulation (GDPR) 46 , the Digital Services Act (DSA) 47 , and proposed bills in the United States encode ethical principles in governments’ policies. For instance, GDPR specifically requires that researchers provide privacy notices and consent documents. Similarly, the DSA demands that social media companies attend to the risks related to data’s collection and use. Researchers’ obligations under GDPR, DSA, and their analogues in other jurisdictions are not yet settled, but it is clear that researchers will have regulatory requirements to meet 48 . As these regulations begin to take effect, our work based on a respect-for-persons framework suggests that future policy efforts would benefit from soliciting public input on appropriate uses of data to balance users’ expectations and preferences with researchers’ interests and goals. A one-size-fits-all approach to data reuse policy could potentially stifle research that users found acceptable and from which society could benefit.

We began this article with an example of increased accessibility of Twitter data and raised questions for social media researchers such as: should they use social media data in their research? If so, when and how ? As with other large-scale data, it is not always possible to ask all prospective study participants to explicitly consent to use their social media data in research. However, through surveying individuals, our study offers a benchmark of individuals’ perceptions of their social media data’s sensitivity and acceptable use from which social media data researchers can cultivate general respect-for-persons practices. We show that people generally find their social media data moderately sensitive relative to other widely used data types. People generally find it acceptable for researchers to use their social media data but prefer that researchers clearly articulate the benefits of their work. Individuals are most concerned about who, why , and which data will be used. When these factors are clearly communicated, individuals are more likely to find their data’s use for research acceptable.

Given our findings that individuals find their social media data moderately sensitive, our study invites social media researchers to learn from well-established best practices for using sensitive data and increasing public awareness about the benefits of research with social media data. Researchers must be clear with themselves and with the public about why social media data is necessary for their work and what benefits that work provides for society, especially for the individuals who carry risks by being included in the data. By using contemporary strategies to balance social media data’s use risks to individuals with the benefits to society, social media researchers can more effectively realize respect for persons, ensuring greater public support for significant advances in research.

To understand participants’ perceptions about acceptable social media data use, we surveyed 1018 people through Qualtrics panels and Mechanical Turk 29 . Qualtrics recruitment targeted U.S. adults who posted publicly to social media at least once per week. We also used quotas for racial identity to ensure our sample was at least 10% African American individuals and at maximum was 80% white-only. We did not use targeting in recruiting MTurk participants.

We used statistical analyses to identify patterns in survey responses, specifically regarding (a) the sensitivity of individuals’ online identifiers relative to other personal identifiers, and (b) whether or not participants perceive a particular data analyst using a specific type of data for a purpose as acceptable or not.

Survey population and sample size

We used the easypower 49 package to estimate the appropriate size of our survey sample using a significance criterion of α  = 0.05 and power = 0.80 and determined we needed at least 426 respondents to detect significant differences in the effect of the interaction of analyst, data type, and purpose. We contracted with Qualtrics to solicit responses from 586 survey panelists. Using a Qualtrics panel enabled us to set minimum quotas for our independent variables (e.g., non-white respondents) to ensure variability. We then used the same instrument to survey 432 crowdworkers through Amazon’s Mechanical Turk. We recruited panels through both Qualtrics and Mechanical Turk to determine whether recruitment platform influenced findings. In our analyses, we include random effects for recruitment source to detect differences between the samples and their impacts on our observations 50 .

Instrument design and variables

Our survey instrument measured relationships between acceptable use and relevant constructs like data sensitivity, and personal characteristics like trust in institutions. Table  4 summarizes the various measures we included in our instrument. The following subsections explain how we developed each measure.

Acceptable use

We developed a measure of acceptable use perceptions motivated by existing work that uses scenarios. However, existing acceptable use scenarios did not explicitly vary three constructs that other literature proposes affect acceptable use perceptions (“data analyst”, “data type”, and “data use purpose”). Therefore, we developed our own set of scenarios to evaluate the effects of these three constructs (see Table  5 for a summary of how we operationalized these constructs). Specifically, we varied scenarios according to three data analysts : academic researchers, social media companies 21 , and journalists 26 . We varied data types to include social media content 15 , cell phone location 51 , 52 , voter file 53 , survey 33 , security camera 54 , and grocery store purchase data 55 because they are used widely in contemporary research. We included three data use purposes : social/behavioral research, research about the natural world, and interventions designed to change individual behavior 11 . Scenarios describing these purposes include real-world research use such as vegetation phenology 56 , emotional contagion 57 , and consumer spending 58 . They also mirror vignettes used in prior work on attitudes toward Facebook and Twitter users’ data 8 , 11 and individuals’ location data 28 . We requested binary responses rather than scale-based responses to our questions about whether data use was acceptable in a scenario because scales are more difficult for respondents to interpret and answer 59 , 60 .

Data sensitivity

In studying the relative sensitivity of respondents’ data, we include items from Milne, et al .‘s 15 study of information sensitivity and willingness to provide it to various institutions for different purposes. Their study evaluated respondents’ relationships to a specific kind of social media data: the online screen name . Evaluating respondents’ relationships to their online screen name does not encompass all types of social media data. However, this particular social media data type, like a driver’s license number, can be a key to accessing other data about a person. Online screen names thus offer a point of comparison with similarly sensitive data types. We mirror earlier surveys’ 15 focus on the online screen name to facilitate comparisons to earlier work and other types of sensitive data. We compare the sensitivity of the online screen name to other data that can uniquely identify a specific person (e.g., driver’s license number) and to data that through linkage or in aggregate can be used to identify a specific person (e.g., race, religion, income, etc.) 61 . Sensitivity acts as a dependent variable in analyses responding to RQ1, and independent variable in analyses responding to RQ2.

Independent variables

To understand whether perceptions of acceptable use vary by mediating factors, we included independent variables motivated by existing literature. Based on existing research, we expected participants’ perspectives to vary based on personal characteristics like their trust in institutions generally 62 , their existing privacy practices and concerns 63 , their demographics 62 , 64 , and their social media use 65 . Assessing these independent variables’ effects also allows us to compare our findings with existing research on personal data sharing and sensitivity 8 , 11 , 15 , 27 .

We used generalized linear mixed models (GLMM)–specifically a cumulative link mixed model (CLMM) for ordinal outcome variables and a mixed effects logistic regression (MELR) for binary outcome variables–to analyze our survey’s results. Through the ability to include random effects, GLMMs enabled us to understand whether individual variation among participants, in addition to personal characteristics like demographics and institutional trust that we measured, impacted participants’ responses. We included a random effect for response platform (Qualtrics or MTurk) and for individual respondent. We estimated models for different combinations of independent variables, their interactions, and control variables. We include the models of best fit, determined by ANOVA, in the main text below.

Limitations

Our survey method likely underestimates individuals’ agreement with various data uses because they may not be familiar with ways that data are used or the methods employed in analysis. For instance, prior studies of patient records 23 and commercial access to health data 66 found that users increased their willingness to share health data after they understood the potential for public benefit and data security measures.

We adopted the variable ‘online screen name’ from prior surveys. Had we included the specific data types we asked about in scenarios (e.g., social media posts) in the sensitivity questions, we may have gotten different results. We chose ‘online screen name’ because it enabled comparison to prior studies 15 , 16 , and because it is analogous to other linkable personal identifiers such as driver’s license number. Future work could examine the sensitivity of specific types of social media data.

Data availability

The survey data 29 that support the findings of this study are available in Deep Blue Data with the identifier 10.7302/6vjf-av59. The University of Michigan IRB HSBS has reviewed this study and determined that it is exempt from ongoing IRB review per federal exemption category: EXEMPTION 2(i) and/or 2(ii) at 45 CFR 46.104(d) (IRB: HUM00204213).

Code availability

Code 67 used to perform the data preparation and analysis for this paper is available on GitHub at https://github.com/casmlab/personal-data-survey and through Zenodo with the identifier https://doi.org/10.5281/zenodo.6807258 .

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Acknowledgements

We are thankful for our colleagues’ help and feedback on this project: Nazanin Andalibi, Ricardo Punzalan, and Kat Roemmich helped draft the survey questions. The Statistical Design Group directed by Brady West and James Wagner consulted on the structure and statistical design of our survey instrument. Lizhou (Leo) Fan provided comments on an earlier draft. Andrew Schrock provided editing services. Staff at Consulting for Statistics, Computing, and Analytics Research (CSCAR) at the University of Michigan advised on statistical analysis. This material is based upon work supported by the National Science Foundation under Grant No. 1839868. Additional funding was provided by the Propelling Original Data Science (PODS) grant program from the Michigan Institute for Data Science.

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Libby Hemphill: Conceptualization, Methodology, Validation, Formal Analysis, Resources, Data Curation, Writing - Original Draft, Writing - Review and Editing, Supervision, Project administration, Funding acquisition; Angela Schöpke-Gonzalez: Validation, Writing - Original Draft, Writing - Review and Editing; Anmol Panda: Conceptualization, Methodology, Writing - Review and Editing.

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Hemphill, L., Schöpke-Gonzalez, A. & Panda, A. Comparative sensitivity of social media data and their acceptable use in research. Sci Data 9 , 643 (2022). https://doi.org/10.1038/s41597-022-01773-w

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Using Social Media Data in Academic Research

The Mixed Blessing of Social Media Data

The phenomenal growth in utilization rates of popular social networking sites such as Facebook, Twitter, and Instagram, offer academic researchers an absolute treasure trove of consumer-generated content for analysis. However, access to that data isn’t without challenges—all providers have unique policies and procedures on data collection, sharing and publication.

The Potential of Social Media Data

Assuming the challenges to access data can be overcome, social media data offers several advantages over data gathered by more traditional research methods :

• Topical information that is potentially available in real time
• Information that is unfiltered by survey tools and other potential biases
• Naturally occurring data collected from an actively engaged population
• Clearly identified, albeit narrow user demographics

Ethical Challenges

Beyond the default privacy settings established by the respective social networking sites (which offer varied customization options to users), critics of social media data mining raise concerns about the issue of consent. Does the fact that the posts are made to public sites with the potential for re-posting constitute a de facto grant of consent? Should consent be sought from each user? Is that even feasible when software programs are scraping tens of thousand of posts per minute?

Is there some middle ground where the anonymity of your username can be seen to allay fears about identity protection? Is your Twitter user name sufficiently anonymous, or should that name be kept anonymous too?

Facebook has recently begun running television commercials advising users not to post vacation photos since their declaration of absence from home is an open invitation to burglars. Given such an apparently cavalier attitude to privacy, should the protections of identity for academic research be adjusted accordingly?

Research Challenges

Given the relatively recent prominence of social networking (Facebook was founded as far back as 2004, Twitter in 2006, and Instagram is a 2010 venture), researchers are still struggling with the extent to which traditional research methodologies should be applied to this data:

• There is increasing evidence of propaganda as each venture has accepted advertising to limit their cash burn and generate some profits for investors, the arrival of new posts, tweets, and photos that the respective company “thought you might find interesting” has opened the floodgates for propaganda.
• The veracity of the information being posted and the identities of the posters continue to be questionable. In December 2014, Instagram purged all the suspected fake and spam accounts from its site. After a loss of 18.8 million accounts (nearly 30% of its total membership), the event became known as the “Instagram Rapture.”
• Perceived anonymity on the Internet has been shown to promote exaggeration and falsehood. Advocates might argue that this is no different than the hyperbole of survey responses where enthusiastic participants give you the answers they think you want, but the sheer volume of social media data has a greater potential to skew the results.

Too Rich to Ignore?

While ethical questions and practical challenges remain, the research potential held within this data is too rich to ignore. It may require the development of new rigorous research methodologies and some adjustment on the part of traditional researchers, but the data will find its way into broader academic research strategies eventually.

Consider that much of the astronomical market valuations of these companies (over $220 billion for Facebook, $35 billion for Instagram, and over $23 billion for Twitter ) are tied-up in the market research potential of their users. As advertisers demand better access to those users, academic researchers should be able to benefit from those improvements.

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Social Media Use and Its Connection to Mental Health: A Systematic Review

Fazida karim.

1 Psychology, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA

2 Business & Management, University Sultan Zainal Abidin, Terengganu, MYS

Azeezat A Oyewande

3 Family Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA

4 Family Medicine, Lagos State Health Service Commission/Alimosho General Hospital, Lagos, NGA

Lamis F Abdalla

5 Internal Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA

Reem Chaudhry Ehsanullah

Safeera khan.

Social media are responsible for aggravating mental health problems. This systematic study summarizes the effects of social network usage on mental health. Fifty papers were shortlisted from google scholar databases, and after the application of various inclusion and exclusion criteria, 16 papers were chosen and all papers were evaluated for quality. Eight papers were cross-sectional studies, three were longitudinal studies, two were qualitative studies, and others were systematic reviews. Findings were classified into two outcomes of mental health: anxiety and depression. Social media activity such as time spent to have a positive effect on the mental health domain. However, due to the cross-sectional design and methodological limitations of sampling, there are considerable differences. The structure of social media influences on mental health needs to be further analyzed through qualitative research and vertical cohort studies.

Introduction and background

Human beings are social creatures that require the companionship of others to make progress in life. Thus, being socially connected with other people can relieve stress, anxiety, and sadness, but lack of social connection can pose serious risks to mental health [ 1 ].

Social media

Social media has recently become part of people's daily activities; many of them spend hours each day on Messenger, Instagram, Facebook, and other popular social media. Thus, many researchers and scholars study the impact of social media and applications on various aspects of people’s lives [ 2 ]. Moreover, the number of social media users worldwide in 2019 is 3.484 billion, up 9% year-on-year [ 3 - 5 ]. A statistic in Figure  1  shows the gender distribution of social media audiences worldwide as of January 2020, sorted by platform. It was found that only 38% of Twitter users were male but 61% were using Snapchat. In contrast, females were more likely to use LinkedIn and Facebook. There is no denying that social media has now become an important part of many people's lives. Social media has many positive and enjoyable benefits, but it can also lead to mental health problems. Previous research found that age did not have an effect but gender did; females were much more likely to experience mental health than males [ 6 , 7 ].

Impact on mental health

Mental health is defined as a state of well-being in which people understand their abilities, solve everyday life problems, work well, and make a significant contribution to the lives of their communities [ 8 ]. There is debated presently going on regarding the benefits and negative impacts of social media on mental health [ 9 , 10 ]. Social networking is a crucial element in protecting our mental health. Both the quantity and quality of social relationships affect mental health, health behavior, physical health, and mortality risk [ 9 ]. The Displaced Behavior Theory may help explain why social media shows a connection with mental health. According to the theory, people who spend more time in sedentary behaviors such as social media use have less time for face-to-face social interaction, both of which have been proven to be protective against mental disorders [ 11 , 12 ]. On the other hand, social theories found how social media use affects mental health by influencing how people view, maintain, and interact with their social network [ 13 ]. A number of studies have been conducted on the impacts of social media, and it has been indicated that the prolonged use of social media platforms such as Facebook may be related to negative signs and symptoms of depression, anxiety, and stress [ 10 - 15 ]. Furthermore, social media can create a lot of pressure to create the stereotype that others want to see and also being as popular as others.

The need for a systematic review

Systematic studies can quantitatively and qualitatively identify, aggregate, and evaluate all accessible data to generate a warm and accurate response to the research questions involved [ 4 ]. In addition, many existing systematic studies related to mental health studies have been conducted worldwide. However, only a limited number of studies are integrated with social media and conducted in the context of social science because the available literature heavily focused on medical science [ 6 ]. Because social media is a relatively new phenomenon, the potential links between their use and mental health have not been widely investigated.

This paper attempt to systematically review all the relevant literature with the aim of filling the gap by examining social media impact on mental health, which is sedentary behavior, which, if in excess, raises the risk of health problems [ 7 , 9 , 12 ]. This study is important because it provides information on the extent of the focus of peer review literature, which can assist the researchers in delivering a prospect with the aim of understanding the future attention related to climate change strategies that require scholarly attention. This study is very useful because it provides information on the extent to which peer review literature can assist researchers in presenting prospects with a view to understanding future concerns related to mental health strategies that require scientific attention. The development of the current systematic review is based on the main research question: how does social media affect mental health?

Research strategy

The research was conducted to identify studies analyzing the role of social media on mental health. Google Scholar was used as our main database to find the relevant articles. Keywords that were used for the search were: (1) “social media”, (2) “mental health”, (3) “social media” AND “mental health”, (4) “social networking” AND “mental health”, and (5) “social networking” OR “social media” AND “mental health” (Table  1 ).

Out of the results in Table  1 , a total of 50 articles relevant to the research question were selected. After applying the inclusion and exclusion criteria, duplicate papers were removed, and, finally, a total of 28 articles were selected for review (Figure  2 ).

PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Inclusion and exclusion criteria

Peer-reviewed, full-text research papers from the past five years were included in the review. All selected articles were in English language and any non-peer-reviewed and duplicate papers were excluded from finally selected articles.

Of the 16 selected research papers, there were a research focus on adults, gender, and preadolescents [ 10 - 19 ]. In the design, there were qualitative and quantitative studies [ 15 , 16 ]. There were three systematic reviews and one thematic analysis that explored the better or worse of using social media among adolescents [ 20 - 23 ]. In addition, eight were cross-sectional studies and only three were longitudinal studies [ 24 - 29 ].The meta-analyses included studies published beyond the last five years in this population. Table  2  presents a selection of studies from the review.

IGU, internet gaming disorder; PSMU, problematic social media use

This study has attempted to systematically analyze the existing literature on the effect of social media use on mental health. Although the results of the study were not completely consistent, this review found a general association between social media use and mental health issues. Although there is positive evidence for a link between social media and mental health, the opposite has been reported.

For example, a previous study found no relationship between the amount of time spent on social media and depression or between social media-related activities, such as the number of online friends and the number of “selfies”, and depression [ 29 ]. Similarly, Neira and Barber found that while higher investment in social media (e.g. active social media use) predicted adolescents’ depressive symptoms, no relationship was found between the frequency of social media use and depressed mood [ 28 ].

In the 16 studies, anxiety and depression were the most commonly measured outcome. The prominent risk factors for anxiety and depression emerging from this study comprised time spent, activity, and addiction to social media. In today's world, anxiety is one of the basic mental health problems. People liked and commented on their uploaded photos and videos. In today's age, everyone is immune to the social media context. Some teens experience anxiety from social media related to fear of loss, which causes teens to try to respond and check all their friends' messages and messages on a regular basis.

On the contrary, depression is one of the unintended significances of unnecessary use of social media. In detail, depression is limited not only to Facebooks but also to other social networking sites, which causes psychological problems. A new study found that individuals who are involved in social media, games, texts, mobile phones, etc. are more likely to experience depression.

The previous study found a 70% increase in self-reported depressive symptoms among the group using social media. The other social media influence that causes depression is sexual fun [ 12 ]. The intimacy fun happens when social media promotes putting on a facade that highlights the fun and excitement but does not tell us much about where we are struggling in our daily lives at a deeper level [ 28 ]. Another study revealed that depression and time spent on Facebook by adolescents are positively correlated [ 22 ]. More importantly, symptoms of major depression have been found among the individuals who spent most of their time in online activities and performing image management on social networking sites [ 14 ].

Another study assessed gender differences in associations between social media use and mental health. Females were found to be more addicted to social media as compared with males [ 26 ]. Passive activity in social media use such as reading posts is more strongly associated with depression than doing active use like making posts [ 23 ]. Other important findings of this review suggest that other factors such as interpersonal trust and family functioning may have a greater influence on the symptoms of depression than the frequency of social media use [ 28 , 29 ].

Limitation and suggestion

The limitations and suggestions were identified by the evidence involved in the study and review process. Previously, 7 of the 16 studies were cross-sectional and slightly failed to determine the causal relationship between the variables of interest. Given the evidence from cross-sectional studies, it is not possible to conclude that the use of social networks causes mental health problems. Only three longitudinal studies examined the causal relationship between social media and mental health, which is hard to examine if the mental health problem appeared more pronounced in those who use social media more compared with those who use it less or do not use at all [ 19 , 20 , 24 ]. Next, despite the fact that the proposed relationship between social media and mental health is complex, a few studies investigated mediating factors that may contribute or exacerbate this relationship. Further investigations are required to clarify the underlying factors that help examine why social media has a negative impact on some peoples’ mental health, whereas it has no or positive effect on others’ mental health.

Conclusions

Social media is a new study that is rapidly growing and gaining popularity. Thus, there are many unexplored and unexpected constructive answers associated with it. Lately, studies have found that using social media platforms can have a detrimental effect on the psychological health of its users. However, the extent to which the use of social media impacts the public is yet to be determined. This systematic review has found that social media envy can affect the level of anxiety and depression in individuals. In addition, other potential causes of anxiety and depression have been identified, which require further exploration.

The importance of such findings is to facilitate further research on social media and mental health. In addition, the information obtained from this study can be helpful not only to medical professionals but also to social science research. The findings of this study suggest that potential causal factors from social media can be considered when cooperating with patients who have been diagnosed with anxiety or depression. Also, if the results from this study were used to explore more relationships with another construct, this could potentially enhance the findings to reduce anxiety and depression rates and prevent suicide rates from occurring.

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

The authors have declared that no competing interests exist.