research proposal on impact of social media

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Peer-reviewed

Research Article

Social impact in social media: A new method to evaluate the social impact of research

Roles Investigation, Writing – original draft

* E-mail: [email protected]

Affiliation Department of Journalism and Communication Studies, Universitat Autonoma de Barcelona, Barcelona, Spain

Affiliation Department of Psychology and Sociology, Universidad de Zaragoza, Zaragoza, Spain

Roles Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing

Affiliation Department of Sociology, Universitat Autonoma de Barcelona, Barcelona, Spain

Affiliation Department of Sociology, Universitat de Barcelona (UB), Barcelona, Spain

• Cristina M. Pulido, 
• Gisela Redondo-Sama, 
• Teresa Sordé-Martí, 
• Ramon Flecha

• Published: August 29, 2018
• https://doi.org/10.1371/journal.pone.0203117
• Reader Comments

The social impact of research has usually been analysed through the scientific outcomes produced under the auspices of the research. The growth of scholarly content in social media and the use of altmetrics by researchers to track their work facilitate the advancement in evaluating the impact of research. However, there is a gap in the identification of evidence of the social impact in terms of what citizens are sharing on their social media platforms. This article applies a social impact in social media methodology (SISM) to identify quantitative and qualitative evidence of the potential or real social impact of research shared on social media, specifically on Twitter and Facebook. We define the social impact coverage ratio (SICOR) to identify the percentage of tweets and Facebook posts providing information about potential or actual social impact in relation to the total amount of social media data found related to specific research projects. We selected 10 projects in different fields of knowledge to calculate the SICOR, and the results indicate that 0.43% of the tweets and Facebook posts collected provide linkages with information about social impact. However, our analysis indicates that some projects have a high percentage (4.98%) and others have no evidence of social impact shared in social media. Examples of quantitative and qualitative evidence of social impact are provided to illustrate these results. A general finding is that novel evidences of social impact of research can be found in social media, becoming relevant platforms for scientists to spread quantitative and qualitative evidence of social impact in social media to capture the interest of citizens. Thus, social media users are showed to be intermediaries making visible and assessing evidence of social impact.

Citation: Pulido CM, Redondo-Sama G, Sordé-Martí T, Flecha R (2018) Social impact in social media: A new method to evaluate the social impact of research. PLoS ONE 13(8): e0203117. https://doi.org/10.1371/journal.pone.0203117

Editor: Sergi Lozano, Institut Català de Paleoecologia Humana i Evolució Social (IPHES), SPAIN

Received: November 8, 2017; Accepted: August 15, 2018; Published: August 29, 2018

Copyright: © 2018 Pulido et al. 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.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The research leading to these results has received funding from the 7th Framework Programme of the European Commission under the Grant Agreement n° 613202 P.I. Ramon Flecha, https://ec.europa.eu/research/fp7/index_en.cfm . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The social impact of research is at the core of some of the debates influencing how scientists develop their studies and how useful results for citizens and societies may be obtained. Concrete strategies to achieve social impact in particular research projects are related to a broader understanding of the role of science in contemporary society. There is a need to explore dialogues between science and society not only to communicate and disseminate science but also to achieve social improvements generated by science. Thus, the social impact of research emerges as an increasing concern within the scientific community [ 1 ]. As Bornmann [ 2 ] said, the assessment of this type of impact is badly needed and is more difficult than the measurement of scientific impact; for this reason, it is urgent to advance in the methodologies and approaches to measuring the social impact of research.

Several authors have approached the conceptualization of social impact, observing a lack of generally accepted conceptual and instrumental frameworks [ 3 ]. It is common to find a wide range of topics included in the contributions about social impact. In their analysis of the policies affecting land use, Hemling et al. [ 4 ] considered various domains in social impact, for instance, agricultural employment or health risk. Moving to the field of flora and fauna, Wilder and Walpole [ 5 ] studied the social impact of conservation projects, focusing on qualitative stories that provided information about changes in attitudes, behaviour, wellbeing and livelihoods. In an extensive study by Godin and Dore [ 6 ], the authors provided an overview and framework for the assessment of the contribution of science to society. They identified indicators of the impact of science, mentioning some of the most relevant weaknesses and developing a typology of impact that includes eleven dimensions, with one of them being the impact on society. The subdimensions of the impact of science on society focus on individuals (wellbeing and quality of life, social implication and practices) and organizations (speeches, interventions and actions). For the authors, social impact “refers to the impact knowledge has on welfare, and on the behaviours, practices and activities of people and groups” (p. 7).

In addition, the terms “social impact” and “societal impact” are sometimes used interchangeably. For instance, Bornmann [ 2 ] said that due to the difficulty of distinguishing social benefits from the superior term of societal benefits, “in much literature the term ‘social impact’ is used instead of ‘societal impact’”(p. 218). However, in other cases, the distinction is made [ 3 ], as in the present research. Similar to the definition used by the European Commission [ 7 ], social impact is used to refer to economic impact, societal impact, environmental impact and, additionally, human rights impact. Therefore, we use the term social impact as the broader concept that includes social improvements in all the above mentioned areas obtained from the transference of research results and representing positive steps towards the fulfilment of those officially defined social goals, including the UN Sustainable Development Goals, the EU 2020 Agenda, or similar official targets. For instance, the Europe 2020 strategy defines five priority targets with concrete indicators (employment, research and development, climate change and energy, education and poverty and social exclusion) [ 8 ], and we consider the targets addressed by objectives defined in the specific call that funds the research project.

This understanding of the social impact of research is connected to the creation of the Social Impact Open Repository (SIOR), which constitutes the first open repository worldwide that displays, cites and stores the social impact of research results [ 9 ]. The SIOR has linked to ORCID and Wikipedia to allow the synergies of spreading information about the social impact of research through diverse channels and audiences. It is relevant to mention that currently, SIOR includes evidence of real social impact, which implies that the research results have led to actual improvements in society. However, it is common to find evidence of potential social impact in research projects. The potential social impact implies that in the development of the research, there has been some evidence of the effectiveness of the research results in terms of social impact, but the results have not yet been transferred.

Additionally, a common confusion is found among the uses of dissemination, transference (policy impact) and social impact. While dissemination means to disseminate the knowledge created by research to citizens, companies and institutions, transference refers to the use of this knowledge by these different actors (or others), and finally, as already mentioned, social impact refers to the actual improvements resulting from the use of this knowledge in relation to the goals motivating the research project (such as the United Nations Sustainable Development Goals). In the present research [ 3 ], it is argued that “social impact can be understood as the culmination of the prior three stages of the research” (p.3). Therefore, this study builds on previous contributions measuring the dissemination and transference of research and goes beyond to propose a novel methodological approach to track social impact evidences.

In fact, the contribution that we develop in this article is based on the creation of a new method to evaluate the evidence of social impact shared in social media. The evaluation proposed is to measure the social impact coverage ratio (SICOR), focusing on the presence of evidence of social impact shared in social media. Then, the article first presents some of the contributions from the literature review focused on the research on social media as a source for obtaining key data for monitoring or evaluating different research purposes. Second, the SISM (social impact through social media) methodology[ 10 ] developed is introduced in detail. This methodology identifies quantitative and qualitative evidence of the social impact of the research shared on social media, specifically on Twitter and Facebook, and defines the SICOR, the social impact coverage ratio. Next, the results are discussed, and lastly, the main conclusions and further steps are presented.

Literature review

Social media research includes the analysis of citizens’ voices on a wide range of topics [ 11 ]. According to quantitative data from April 2017 published by Statista [ 12 ], Twitter and Facebook are included in the top ten leading social networks worldwide, as ranked by the number of active users. Facebook is at the top of the list, with 1,968 million active users, and Twitter ranks 10 th , with 319 million active users. Between them are the following social networks: WhatsApp, YouTube, Facebook Messenger, WeChat, QQ, Instagram,Qzone and Tumblr. If we look at altmetrics, the tracking of social networks for mentions of research outputs includes Facebook, Twitter, Google+,LinkedIn, Sina Weibo and Pinterest. The social networks common to both sources are Facebook and Twitter. These are also popular platforms that have a relevant coverage of scientific content and easy access to data, and therefore, the research projects selected here for application of the SISM methodology were chosen on these platforms.

Chew and Eysenbach [ 13 ] studied the presence of selected keywords in Twitter related to public health issues, particularly during the 2009 H1N1 pandemic, identifying the potential for health authorities to use social media to respond to the concerns and needs of society. Crooks et al.[ 14 ] investigated Twitter activity in the context of a 5.8 magnitude earthquake in 2011 on the East Coast of the United States, concluding that social media content can be useful for event monitoring and can complement other sources of data to improve the understanding of people’s responses to such events. Conversations among young Canadians posted on Facebook and analysed by Martinello and Donelle [ 15 ] revealed housing and transportation as main environmental concerns, and the project FoodRisc examined the role of social media to illustrate consumers’ quick responses during food crisis situations [ 16 ]. These types of contributions illustrate that social media research implies the understanding of citizens’ concerns in different fields, including in relation to science.

Research on the synergies between science and citizens has increased over the years, according to Fresco [ 17 ], and there is a growing interest among researchers and funding agencies in how to facilitate communication channels to spread scientific results. For instance, in 1998, Lubchenco [ 18 ] advocated for a social contract that “represents a commitment on the part of all scientists to devote their energies and talents to the most pressing problems of the day, in proportion to their importance, in exchange for public funding”(p.491).

In this framework, the recent debates on how to increase the impact of research have acquired relevance in all fields of knowledge, and major developments address the methods for measuring it. As highlighted by Feng Xia et al. [ 19 ], social media constitute an emerging approach to evaluating the impact of scholarly publications, and it is relevant to consider the influence of the journal, discipline, publication year and user type. The authors revealed that people’s concerns differ by discipline and observed more interest in papers related to everyday life, biology, and earth and environmental sciences. In the field of biomedical sciences, Haustein et al. [ 20 ] analysed the dissemination of journal articles on Twitter to explore the correlations between tweets and citations and proposed a framework to evaluate social media-based metrics. In fact, different studies address the relationship between the presence of articles on social networks and citations [ 21 ]. Bornmann [ 22 ] conducted a case study using a sample of 1,082 PLOS journal articles recommended in F1000 to explore the usefulness of altmetrics for measuring the broader impact of research. The author presents evidence about Facebook and Twitter as social networks that may indicate which papers in the biomedical sciences can be of interest to broader audiences, not just to specialists in the area. One aspect of particular interest resulting from this contribution is the potential to use altmetrics to measure the broader impacts of research, including the societal impact. However, most of the studies investigating social or societal impact lack a conceptualization underlying its measurement.

To the best of our knowledge, the assessment of social impact in social media (SISM) has developed according to this gap. At the core of this study, we present and discuss the results obtained through the application of the SICOR (social impact coverage ratio) with examples of evidence of social impact shared in social media, particularly on Twitter and Facebook, and the implications for further research.

Following these previous contributions, our research questions were as follows: Is there evidence of social impact of research shared by citizens in social media? If so, is there quantitative or qualitative evidence? How can social media contribute to identifying the social impact of research?

Methods and data presentation

A group of new methodologies related to the analysis of online data has recently emerged. One of these emerging methodologies is social media analytics [ 23 ], which was initially used most in the marketing research field but also came to be used in other domains due to the multiple possibilities opened up by the availability and richness of the data for different research purposes. Likewise, the concern of how to evaluate the social impact of research as well as the development of methodologies for addressing this concern has occupied central attention. The development of SISM (Social Impact in Social Media) and the application of the SICOR (Social Impact Coverage Ratio) is a contribution to advancement in the evaluation of the social impact of research through the analysis of the social media selected (in this case, Twitter and Facebook). Thus, SISM is novel in both social media analytics and among the methodologies used to evaluate the social impact of research. This development has been made under IMPACT-EV, a research project funded under the Framework Program FP7 of the Directorate-General for Research and Innovation of the European Commission. The main difference from other methodologies for measuring the social impact of research is the disentanglement between dissemination and social impact. While altmetrics is aimed at measuring research results disseminated beyond academic and specialized spheres, SISM contribute to advancing this measurement by shedding light on to what extent evidence of the social impact of research is found in social media data. This involves the need to differentiate between tweets or Facebook posts (Fb/posts) used to disseminate research findings from those used to share the social impact of research. We focus on the latter, investigating whether there is evidence of social impact, including both potential and real social impact. In fact, the question is whether research contributes and/or has the potential to contribute to improve the society or living conditions considering one of these goals defined. What is the evidence? Next, we detail the application of the methodology.

Data collection

To develop this study, the first step was to select research projects with social media data to be analysed. The selection of research projects for application of the SISM methodology was performed according to three criteria.

Criteria 1. Selection of success projects in FP7. The projects were success stories of the 7 th Framework Programme (FP7) highlighted by the European Commission [ 24 ] in the fields of knowledge of medicine, public health, biology and genomics. The FP7 published calls for project proposals from 2007 to 2013. This implies that most of the projects funded in the last period of the FP7 (2012 and 2013) are finalized or in the last phase of implementation.

Criteria 2. Period of implementation. We selected projects in the 2012–2013 period because they combine recent research results with higher possibilities of having Twitter and Facebook accounts compared with projects of previous years, as the presence of social accounts in research increased over this period.

Criteria 3. Twitter and Facebook accounts. It was crucial that the selected projects had active Twitter and Facebook accounts.

Table 1 summarizes the criteria and the final number of projects identified. As shown, 10 projects met the defined criteria. Projects in medical research and public health had higher presence.

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https://doi.org/10.1371/journal.pone.0203117.t001

After the selection of projects, we defined the timeframe of social media data extraction on Twitter and Facebook from the starting date of the project until the day of the search, as presented in Table 2 .

https://doi.org/10.1371/journal.pone.0203117.t002

The second step was to define the search strategies for extracting social media data related to the research projects selected. In this line, we defined three search strategies.

Strategy 1. To extract messages published on the Twitter account and the Facebook page of the selected projects. We listed the Twitter accounts and Facebook pages related to each project in order to look at the available information. In this case, it is important to clarify that the tweets published under the corresponding Twitter project account are original tweets or retweets made from this account. It is relevant to mention that in one case, the Twitter account and Facebook page were linked to the website of the research group leading the project. In this case, we selected tweets and Facebook posts related to the project. For instance, in the case of the Twitter account, the research group created a specific hashtag to publish messages related to the project; therefore, we selected only the tweets published under this hashtag. In the analysis, we prioritized the analysis of the tweets and Facebook posts that received some type of interaction (likes, retweets or shares) because such interaction is a proxy for citizens’ interest. In doing so, we used the R program and NVivoto extract the data and proceed with the analysis. Once we obtained the data from Twitter and Facebook, we were able to have an overview of the information to be further analysed, as shown in Table 3 .

https://doi.org/10.1371/journal.pone.0203117.t003

We focused the second and third strategies on Twitter data. In both strategies, we extracted Twitter data directly from the Twitter Advanced Search tool, as the API connected to NVivo and the R program covers only a specific period of time limited to 7/9 days. Therefore, the use of the Twitter Advanced Search tool made it possible to obtain historic data without a period limitation. We downloaded the results in PDF and then uploaded them to NVivo.

Strategy 2. To use the project acronym combined with other keywords, such as FP7 or EU. This strategy made it possible to obtain tweets mentioning the project. Table 4 presents the number of tweets obtained with this strategy.

https://doi.org/10.1371/journal.pone.0203117.t004

Strategy 3. To use searchable research results of projects to obtain Twitter data. We defined a list of research results, one for each project, and converted them into keywords. We selected one searchable keyword for each project from its website or other relevant sources, for instance, the brief presentations prepared by the European Commission and published in CORDIS. Once we had the searchable research results, we used the Twitter Advanced Search tool to obtain tweets, as presented in Table 5 .

https://doi.org/10.1371/journal.pone.0203117.t005

The sum of the data obtained from these three strategies allowed us to obtain a total of 3,425 tweets and 1,925 posts on public Facebook pages. Table 6 presents a summary of the results.

https://doi.org/10.1371/journal.pone.0203117.t006

We imported the data obtained from the three search strategies into NVivo to analyse. Next, we select tweets and Facebook posts providing linkages with quantitative or qualitative evidence of social impact, and we complied with the terms of service for the social media from which the data were collected. By quantitative and qualitative evidence, we mean data or information that shows how the implementation of research results has led to improvements towards the fulfilment of the objectives defined in the EU2020 strategy of the European Commission or other official targets. For instance, in the case of quantitative evidence, we searched tweets and Facebook posts providing linkages with quantitative information about improvements obtained through the implementation of the research results of the project. In relation to qualitative evidence, for example, we searched for testimonies that show a positive evaluation of the improvement due to the implementation of research results. In relation to this step, it is important to highlight that social media users are intermediaries making visible evidence of social impact. Users often share evidence, sometimes sharing a link to an external resource (e.g., a video, an official report, a scientific article, news published on media). We identified evidence of social impact in these sources.

Data analysis

γ i is the total number of messages obtained about project i with evidence of social impact on social media platforms (Twitter, Facebook, Instagram, etc.);

T i is the total number of messages from project i on social media platforms (Twitter, Facebook, Instagram, etc.); and

n is the number of projects selected.

Analytical categories and codebook

The researchers who carried out the analysis of the social media data collected are specialists in the social impact of research and research on social media. Before conducting the full analysis, two aspects were guaranteed. First, how to identify evidence of social impact relating to the targets defined by the EU2020 strategy or to specific goals defined by the call addressed was clarified. Second, we held a pilot to test the methodology with one research project that we know has led to considerable social impact, which allowed us to clarify whether or not it was possible to detect evidence of social impact shared in social media. Once the pilot showed positive results, the next step was to extend the analysis to another set of projects and finally to the whole sample. The construction of the analytical categories was defined a priori, revised accordingly and lastly applied to the full sample.

Different observations should be made. First, in this previous analysis, we found that the tweets and Facebook users play a key role as “intermediaries,” serving as bridges between the larger public and the evidence of social impact. Social media users usually share a quote or paragraph introducing evidence of social impact and/or link to an external resource, for instance, a video, official report, scientific article, news story published on media, etc., where evidence of the social impact is available. This fact has implications for our study, as our unit of analysis is all the information included in the tweets or Facebook posts. This means that our analysis reaches the external resources linked to find evidence of social impact, and for this reason, we defined tweets or Facebook posts providing linkages with information about social impact.

Second, the other important aspect is the analysis of the users’ profile descriptions, which requires much more development in future research given the existing limitations. For instance, some profiles are users’ restricted due to privacy reasons, so the information is not available; other accounts have only the name of the user with no description of their profile available. Therefore, we gave priority to the identification of evidence of social impact including whether a post obtained interaction (retweets, likes or shares) or was published on accounts other than that of the research project itself. In the case of the profile analysis, we added only an exploratory preliminary result because this requires further development. Considering all these previous details, the codebook (see Table 7 ) that we present as follows is a result of this previous research.

https://doi.org/10.1371/journal.pone.0203117.t007

How to analyse Twitter and Facebook data

To illustrate how we analysed data from Twitter and Facebook, we provide one example of each type of evidence of social impact defined, considering both real and potential social impact, with the type of interaction obtained and the profiles of those who have interacted.

QUANESISM. Tweet by ZeroHunger Challenge @ZeroHunger published on 3 May 2016. Text: How re-using food waste for animal feed cuts carbon emissions.-NOSHAN project hubs.ly/H02SmrP0. 7 retweets and 5 likes.

The unit of analysis is all the content of the tweet, including the external link. If we limited our analysis to the tweet itself, it would not be evidence. Examining the external link is necessary to find whether there is evidence of social impact. The aim of this project was to investigate the process and technologies needed to use food waste for feed production at low cost, with low energy consumption and with a maximal evaluation of the starting wastes. This tweet provides a link to news published in the PHYS.org portal [ 25 ], which specializes in science news. The news story includes an interview with the main researcher that provides the following quotation with quantitative evidence:

'Our results demonstrated that with a NOSHAN 10 percent mix diet, for every kilogram of broiler chicken feed, carbon dioxide emissions were reduced by 0.3 kg compared to a non-food waste diet,' explains Montse Jorba, NOSHAN project coordinator. 'If 1 percent of total chicken broiler feed in Europe was switched to the 10 percent NOSHAN mix diet, the total amount of CO2 emissions avoided would be 0.62 million tons each year.'[ 25 ]

This quantitative evidence “a NOSHAN 10 percent mix diet, for every kilogram of broiler chicken feed, carbon dioxide emissions carbon dioxide emissions were reduced by 0.3 kg to a non-food waste diet” is linked directly with the Europe 2020 target of Climate Change & Energy, specifically with the target of reducing greenhouse gas emissions by 20% compared to the levels in 1990 [ 8 ]. The illustrative extrapolation the coordinator mentioned in the news is also an example of quantitative evidence, although is an extrapolation based on the specific research result.

This tweet was captured by the Acronym search strategy. It is a message tweeted by an account that is not related to the research project. The twitter account is that of the Zero Hunger Challenge movement, which supports the goals of the UN. The interaction obtained is 7 retweets and 5 likes. Regarding the profiles of those who retweeted and clicked “like”, there were activists, a journalist, an eco-friendly citizen, a global news service, restricted profiles (no information is available on those who have retweeted) and one account with no information in its profile.

The following example illustrates the analysis of QUALESISM: Tweet by @eurofitFP7 published on4 October 2016. Text: See our great new EuroFIT video on youtube! https://t.co/TocQwMiW3c 9 retweets and 5 likes.

The aim of this project is to improve health through the implementation of two novel technologies to achieve a healthier lifestyle. The tweet provides a link to a video on YouTube on the project’s results. In this video, we found qualitative evidence from people who tested the EuroFit programme; there are quotes from men who said that they have experienced improved health results using this method and that they are more aware of how to manage their health:

One end-user said: I have really amazing results from the start, because I managed to change a lot of things in my life. And other one: I was more conscious of what I ate, I was more conscious of taking more steps throughout the day and also standing up a little more. [ 26 ]

The research applies the well researched scientific evidence to the management of health issues in daily life. The video presents the research but also includes a section where end-users talk about the health improvements they experienced. The quotes extracted are some examples of the testimonies collected. All agree that they have improved their health and learned healthy habits for their daily lives. These are examples of qualitative evidence linked with the target of the call HEALTH.2013.3.3–1—Social innovation for health promotion [ 27 ] that has the objectives of reducing sedentary habits in the population and promoting healthy habits. This research contributes to this target, as we see in the video testimonies. Regarding the interaction obtained, this tweet achieved 9 retweets and 5 likes. In this case, the profiles of the interacting citizens show involvement in sport issues, including sport trainers, sport enthusiasts and some researchers.

To summarize the analysis, in Table 8 below, we provide a summary with examples illustrating the evidence found.

https://doi.org/10.1371/journal.pone.0203117.t008

Quantitative evidence of social impact in social media

There is a greater presence of tweets/Fb posts with quantitative evidence (14) than with qualitative evidence (9) in the total number of tweets/Fb posts identified with evidence of social impact. Most of the tweets/Fb posts with quantitative evidence of social impact are from scientific articles published in peer-reviewed international journals and show potential social impact. In Table 8 , we introduce 3 examples of this type of tweets/Fb posts with quantitative evidence:

The first tweet with quantitative social impact selected is from project 7. The aim of this project was to provide high-quality scientific evidence for preventing vitamin D deficiency in European citizens. The tweet highlighted the main contribution of the published study, that is, “Weekly consumption of 7 vitamin D-enhanced eggs has an important impact on winter vitamin D status in adults” [ 28 ]. The quantitative evidence shared in social media was extracted from a news publication in a blog on health news. This blog collects scientific articles of research results. In this case, the blog disseminated the research result focused on how vitamin D-enhanced eggs improve vitamin D deficiency in wintertime, with the published results obtained by the research team of the project selected. The quantitative evidence illustrates that the group of adults who consumed vitamin D-enhanced eggs did not suffer from vitamin D deficiency, as opposed to the control group, which showed a significant decrease in vitamin D over the winter. The specific evidence is the following extracted from the article [ 28 ]:

With the use of a within-group analysis, it was shown that, although serum 25(OH) D in the control group significantly decreased over winter (mean ± SD: -6.4 ± 6.7 nmol/L; P = 0.001), there was no change in the 2 groups who consumed vitamin D-enhanced eggs (P>0.1 for both. (p. 629)

This evidence contributes to achievement of the target defined in the call addressed that is KBBE.2013.2.2–03—Food-based solutions for the eradication of vitamin D deficiency and health promotion throughout the life cycle [ 29 ]. The quantitative evidence shows how the consumption of vitamin D-enhanced eggs reduces vitamin D deficiency.

The second example of this table corresponds to the example of quantitative evidence of social impact provided in the previous section.

The third example is a Facebook post from project 3 that is also tweeted. Therefore, this evidence was published in both social media sources analysed. The aim of this project was to measure a range of chemical and physical environmental hazards in food, consumer products, water, air, noise, and the built environment in the pre- and postnatal early-life periods. This Facebook post and tweet links directly to a scientific article [ 30 ] that shows the precision of the spectroscopic platform:

Using 1H NMR spectroscopy we characterized short-term variability in urinary metabolites measured from 20 children aged 8–9 years old. Daily spot morning, night-time and pooled (50:50 morning and night-time) urine samples across six days (18 samples per child) were analysed, and 44 metabolites quantified. Intraclass correlation coefficients (ICC) and mixed effect models were applied to assess the reproducibility and biological variance of metabolic phenotypes. Excellent analytical reproducibility and precision was demonstrated for the 1H NMR spectroscopic platform (median CV 7.2%) . (p.1)

This evidence is linked to the target defined in the call “ENV.2012.6.4–3—Integrating environmental and health data to advance knowledge of the role of environment in human health and well-being in support of a European exposome initiative” [ 31 ]. The evidence provided shows how the project’s results have contributed to building technology for improving the data collection to advance in the knowledge of the role of the environment in human health, especially in early life. The interaction obtained is one retweet from a citizen from Nigeria interested in health issues, according to the information available in his profile.

Qualitative evidence of social impact in social media

We found qualitative evidence of the social impact of different projects, as shown in Table 9 . Similarly to the quantitative evidence, the qualitative cases also demonstrate potential social impact. The three examples provided have in common that they are tweets or Facebook posts that link to videos where the end users of the research project explain their improvements once they have implemented the research results.

https://doi.org/10.1371/journal.pone.0203117.t009

The first tweet with qualitative evidence selected is from project 4. The aim of this project is to produce a system that helps in the prevention of obesity and eating disorders, targeting young people and adults [ 32 ]. The twitter account that published this tweet is that of the Future and Emerging Technologies Programme of the European Commission, and a link to a Euronews video is provided. This video shows how the patients using the technology developed in the research achieved control of their eating disorders, through the testimonies of patients commenting on the positive results they have obtained. These testimonies are included in the news article that complements the video. An example of these testimonies is as follows:

Pierre Vial has lost 43 kilos over the past nine and a half months. He and other patients at the eating disorder clinic explain the effects obesity and anorexia have had on their lives. Another patient, Karin Borell, still has some months to go at the clinic but, after decades of battling anorexia, is beginning to be able to visualise life without the illness: “On a good day I see myself living a normal life without an eating disorder, without problems with food. That’s really all I wish right now”.[ 32 ]

This qualitative evidence shows how the research results contribute to the achievement of the target goals of the call addressed:“ICT-2013.5.1—Personalised health, active ageing, and independent living”. [ 33 ] In this case, the results are robust, particularly for people suffering chronic diseases and desiring to improve their health; people who have applied the research findings are improving their eating disorders and better managing their health. The value of this evidence is the inclusion of the patients’ voices stating the impact of the research results on their health.

The second example is a Facebook post from project 9, which provides a link to a Euronews video. The aim of this project is to bring some tools from the lab to the farm in order to guarantee a better management of the farm and animal welfare. In this video [ 34 ], there are quotes from farmers using the new system developed through the research results of the project. These quotes show how use of the new system is improving the management of the farm and the health of the animals; some examples are provided:

Cameras and microphones help me detect in real time when the animals are stressed for whatever reason,” explained farmer Twan Colberts. “So I can find solutions faster and in more efficient ways, without me being constantly here, checking each animal.”

This evidence shows how the research results contribute to addressing the objectives specified in the call “KBBE.2012.1.1–02—Animal and farm-centric approach to precision livestock farming in Europe” [ 29 ], particularly, to improve the precision of livestock farming in Europe. The interaction obtained is composed of6 likes and 1 share. The profiles are diverse, but some of them do not disclose personal information; others have not added a profile description, and only their name and photo are available.

Interrater reliability (kappa)

The analysis of tweets and Facebook posts providing linkages with information about social impact was conducted following a content analysis method in which reliability was based on a peer review process. This sample is composed of 3,425 tweets and 1,925 Fb/posts. Each tweet and Facebook post was analysed to identify whether or not it contains evidence of social impact. Each researcher has the codebook a priori. We used interrater reliability in examining the agreement between the two raters on the assignment of the categories defined through Cohen’s kappa. We used SPSS to calculate this coefficient. We exported an excel sheet with the sample coded by the two researchers being 1 (is evidence of social impact, either potential or real) and 0 (is not evidence of social impact) to SPSS. The cases where agreement was not achieved were not considered as containing evidence of social impact. The result obtained is 0.979; considering the interpretation of this number according to Landis & Koch [ 35 ], our level of agreement is almost perfect, and thus, our analysis is reliable. To sum up the data analysis, the description of the steps followed is explained:

Step 1. Data analysis I. We included all data collected in an excel sheet to proceed with the analysis. Prior to the analysis, researchers read the codebook to keep in mind the information that should be identified.

Step 2. Each researcher involved reviewed case by case the tweets and Facebook posts to identify whether they provide links with evidence of social impact or not. If the researcher considers there to be evidence of social impact, he or she introduces the value of 1into the column, and if not, the value of 0.

Step 3. Once all the researchers have finished this step, the next step is to export the excel sheet to SPSS to extract the kappa coefficient.

Step 4. Data Analysis II. The following step was to analyse case by case the tweets and Facebook posts identified as providing linkages with information of social impact and classify them as quantitative or qualitative evidence of social impact.

Step 5. The interaction received was analysed because this determines to which extent this evidence of social impact has captured the attention of citizens (in the form of how many likes, shares, or retweets the post has).

Step 6. Finally, if available, the profile descriptions of the citizens interacting through retweeting or sharing the Facebook post were considered.

Step 7. SICOR was calculated. It could be applied to the complete sample (all data projects) or to each project, as we will see in the next section.

The total number of tweets and Fb/posts collected from the 10 projects is 5,350. After the content analysis, we identified 23 tweets and Facebook posts providing linkages to information about social impact. To respond to the research question, which considered whether there is evidence of social impact shared by citizens in social media, the answer was affirmative, although the coverage ratio is low. Both Twitter and Facebook users retweeted or shared evidence of social impact, and therefore, these two social media networks are valid sources for expanding knowledge on the assessment of social impact. Table 10 shows the social impact coverage ratio in relation to the total number of messages analysed.

https://doi.org/10.1371/journal.pone.0203117.t010

The analysis of each of the projects selected revealed some results to consider. Of the 10 projects, 7 had evidence, but those projects did not necessarily have more Tweets and Facebook posts. In fact, some projects with fewer than 70 tweets and 50 Facebook posts have more evidence of social impact than other projects with more than 400 tweets and 400 Facebook posts. This result indicates that the number of tweets and Facebook posts does not determine the existence of evidence of social impact in social media. For example, project 2 has 403 tweets and 423 Facebooks posts, but it has no evidence of social impact on social media. In contrast, project 9 has 62 tweets, 43 Facebook posts, and 2 pieces of evidence of social impact in social media, as shown in Table 11 .

https://doi.org/10.1371/journal.pone.0203117.t011

The ratio of tweets/Fb posts to evidence is 0.43%, and it differs depending on the project, as shown below in Table 12 . There is one project (P7) with a ratio of 4.98%, which is a social impact coverage ratio higher than that of the other projects. Next, a group of projects (P3, P9, P10) has a social impact coverage ratio between 1.41% and 2,99%.The next slot has three projects (P1, P4, P5), with a ratio between 0.13% and 0.46%. Finally, there are three projects (P2, P6, P8) without any tweets/Fb posts evidence of social impact.

https://doi.org/10.1371/journal.pone.0203117.t012

Considering the three strategies for obtaining data, each is related differently to the evidence of social impact. In terms of the social impact coverage ratio, as shown in Table 13 , the most successful strategy is number 3 (searchable research results), as it has a relation of 17.86%, which is much higher than the ratios for the other 2 strategies. The second strategy (acronym search) is more effective than the first (profile accounts),with 1.77% for the former as opposed to 0.27% for the latter.

https://doi.org/10.1371/journal.pone.0203117.t013

Once tweets and Facebook posts providing linkages with information about social impact(ESISM)were identified, we classified them in terms of quantitative (QUANESISM) or qualitative evidence (QUALESISM)to determine which type of evidence was shared in social media. Table 14 indicates the amount of quantitative and qualitative evidence identified for each search strategy.

https://doi.org/10.1371/journal.pone.0203117.t014

First, the results obtained indicated that the SISM methodology aids in calculating the social impact coverage ratio of the research projects selected and evaluating whether the social impact of the corresponding research is shared by citizens in social media. The social impact coverage ratio applied to the sample selected is low, but when we analyse the SICOR of each project separately, we can observe that some projects have a higher social impact coverage ratio than others. Complementary to altmetrics measuring the extent to which research results reach out society, the SICOR considers the question whether this process includes evidence of potential or real social impact. In this sense, the overall methodology of SISM contributes to advancement in the evaluation of the social impact of research by providing a more precise approach to what we are evaluating.

This contribution complements current evaluation methodologies of social impact that consider which improvements are shared by citizens in social media. Exploring the results in more depth, it is relevant to highlight that of the ten projects selected, there is one research project with a social impact coverage ratio higher than those of the others, which include projects without any tweets or Facebook posts with evidence of social impact. This project has a higher ratio of evidence than the others because evidence of its social impact is shared more than is that of other projects. This also means that the researchers produced evidence of social impact and shared it during the project. Another relevant result is that the quantity of tweets and Fb/posts collected did not determine the number of tweets and Fb/posts found with evidence of social impact. Moreover, the analysis of the research projects selected showed that there are projects with less social media interaction but with more tweets and Fb/posts containing evidence of social media impact. Thus, the number of tweets and Fb/posts with evidence of social impact is not determined by the number of publication messages collected; it is determined by the type of messages published and shared, that is, whether they contain evidence of social impact or not.

The second main finding is related to the effectiveness of the search strategies defined. Related to the strategies carried out under this methodology, one of the results found is that the most effective search strategy is the searchable research results, which reveals a higher percentage of evidence of social impact than the own account and acronym search strategies. However, the use of these three search strategies is highly recommended because the combination of all of them makes it possible to identify more tweets and Facebook posts with evidence of social impact.

Another result is related to the type of evidence of social impact found. There is both quantitative and qualitative evidence. Both types are useful for understanding the type of social impact achieved by the corresponding research project. In this sense, quantitative evidence allows us to understand the improvements obtained by the implementation of the research results and capture their impact. In contrast, qualitative evidence allows us to deeply understand how the resultant improvements obtained from the implementation of the research results are evaluated by the end users by capturing their corresponding direct quotes. The social impact includes the identification of both real and potential social impact.

Conclusions

After discussing the main results obtained, we conclude with the following points. Our study indicates that there is incipient evidence of social impact, both potential and real, in social media. This demonstrates that researchers from different fields, in the present case involved in medical research, public health, animal welfare and genomics, are sharing the improvements generated by their research and opening up new venues for citizens to interact with their work. This would imply that scientists are promoting not only the dissemination of their research results but also the evidence on how their results may lead to the improvement of societies. Considering the increasing relevance and presence of the dissemination of research, the results indicate that scientists still need to include in their dissemination and communication strategies the aim of sharing the social impact of their results. This implies the publication of concrete qualitative or quantitative evidence of the social impact obtained. Because of the inclusion of this strategy, citizens will pay more attention to the content published in social media because they are interested in knowing how science can contribute to improving their living conditions and in accessing crucial information. Sharing social impact in social media facilitates access to citizens of different ages, genders, cultural backgrounds and education levels. However, what is most relevant for our argument here is how citizens should also be able to participate in the evaluation of the social impact of research, with social media a great source to reinforce this democratization process. This contributes not only to greatly improving the social impact assessment, as in addition to experts, policy makers and scientific publications, citizens through social media contribute to making this assessment much more accurate. Thus, citizens’ contribution to the dissemination of evidence of the social impact of research yields access to more diverse sectors of society and information that might be unknown by the research or political community. Two future steps are opened here. On the one hand, it is necessary to further examine the profiles of users who interact with this evidence of social impact considering the limitations of the privacy and availability of profile information. A second future task is to advance in the articulation of the role played by citizens’ participation in social impact assessment, as citizens can contribute to current worldwide efforts by shedding new light on this process of social impact assessment and contributing to making science more relevant and useful for the most urgent and poignant social needs.

Supporting information

S1 file. interrater reliability (kappa) result..

This file contains the SPSS file with the result of the calculation of Cohen’s Kappa regards the interrater reliability. The word document exported with the obtained result is also included.

https://doi.org/10.1371/journal.pone.0203117.s001

S2 File. Data collected and SICOR calculation.

This excel contains four sheets, the first one titled “data collected” contains the number of tweets and Facebook posts collected through the three defined search strategies; the second sheet titled “sample” contains the sample classified by project indicating the ID of the message or code assigned, the type of message (tweet or Facebook post) and the codification done by researchers being 1 (is evidence of social impact, either potential or real) and 0 (is not evidence of social impact); the third sheet titled “evidence found” contains the number of type of evidences of social impact founded by project (ESISM-QUANESIM or ESISM-QUALESIM), search strategy and type of message (tweet or Facebook posts); and the last sheet titled “SICOR” contains the Social Impact Coverage Ratio calculation by projects in one table and type of search strategy done in another one.

https://doi.org/10.1371/journal.pone.0203117.s002

Acknowledgments

The research leading to these results received funding from the 7 th Framework Programme of the European Commission under Grant Agreement n° 613202. The extraction of available data using the list of searchable keywords on Twitter and Facebook followed the ethical guidelines for social media research supported by the Economic and Social Research Council (UK) [ 36 ] and the University of Aberdeen [ 37 ]. Furthermore, the research results have already been published and made public, and hence, there are no ethical issues.

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Potential risks of content, features, and functions: The science of how social media affects youth

Almost a year after APA issued its health advisory on social media use in adolescence , society continues to wrestle with ways to maximize the benefits of these platforms while protecting youth from the potential harms associated with them. 1

By early 2024, few meaningful changes to social media platforms had been enacted by industry, and no federal policies had been adopted. There remains a need for social media companies to make fundamental changes to their platforms.

Psychological science continues to reveal benefits from social media use , as well as risks and opportunities that certain content, features, and functions present to young social media users. The science discussed below highlights the need to enact new, responsible safety standards to mitigate harm. 2

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Related content

• APA report calls on social media companies to take responsibility to protect youth
• How much is too much social media use?

Elaboration of science on social media content, features, and functions

Platforms built for adults are not inherently suitable for youth. i Youth require special protection due to areas of competence or vulnerability as they progress through the childhood, teenage, and late adolescent years. ii This is especially true for youth experiencing psychological, physical, intellectual, mental health, or other developmental challenges; chronological age is not directly associated with social media readiness . iii

Hypersensitivity to social feedback

Brain development starting at ages 10–13 (i.e., the outset of puberty) until approximately the mid-twenties is linked with hypersensitivity to social feedback/stimuli. iv In other words, youth become especially invested in behaviors that will help them get personalized feedback, praise, or attention from peers.

• AI-recommended content has the potential to be especially influential and hard to resist within this age range. v It is critical that AI-recommended content be designed to prioritize youth safety and welfare over engagement. This suggests potentially restricting the use of personalized recommendations using youth data, design features that may prioritize content evoking extreme emotions, or content that may depict illegal or harmful behavior.
• Likes and follower counts activate neural regions that trigger repetitive behavior, and thus may exert greater influence on youths’ attitudes and behavior than among adults. vi Youth are especially sensitive to both positive social feedback and rejection from others. Using these metrics to maintain platform engagement capitalizes on youths’ vulnerabilities and likely leads to problematic use.
• The use of youth data for tailored ad content similarly is influential for youth who are biologically predisposed toward peer influence at this stage and sensitive to personalized content. vii

Need for relationship skill building

Adolescence is a critical period for the development of more complex relationship skills, characterized by the ability to form emotionally intimate relationships. viii The adolescent years should provide opportunities to practice these skills through one-on-one or small group interactions.

• The focus on metrics of followers, likes, and views focuses adolescents’ attention on unilateral, depersonalized interactions and may discourage them from building healthier and psychologically beneficial relationship skills. ix

Susceptibility to harmful content

Adolescence is a period of heightened susceptibility to peer influence, impressionability, and sensitivity to social rejection. x Harmful content, including cyberhate, the depiction of illegal behavior, and encouragement to engage in self-harm (e.g., cutting or eating-disordered behavior) is associated with increased mental health difficulties among both the targets and witnesses of such content. xi

• The absence of clear and transparent processes for addressing reports of harmful content makes it harder for youth to feel protected or able to get help in the face of harmful content.

Underdeveloped impulse control

Youths’ developing cortical system (particularly in the brain’s inhibitory control network) makes them less capable of resisting impulses or stopping themselves from behavior that may lead to temporary benefit despite negative longer-term consequences. xii This can lead to adolescents making decisions based on short-term gain, lower appreciation of long-term risks, and interference with focus on tasks that require concentration.

• Infinite scroll is particularly risky for youth since their ability to monitor and stop engagement on social media is more limited than among adults. xiii This contributes to youths’ difficulty disengaging from social media and may contribute to high rates of youth reporting symptoms of clinical dependency on social media. xiv
• The lack of time limits on social media use similarly is challenging for youth, particularly during the school day or at times when they should be doing homework. xv
• Push notifications capitalize on youths’ sensitivity to distraction. Task-shifting is a higher order cognitive ability not fully developed until early adulthood and may interfere with youths’ focus during class time and when they should be doing homework. xvi
• The use and retention of youths’ data without appropriate parental consent, and/or child assent in developmentally appropriate language, capitalizes on youths’ relatively poor appreciation for long-term consequences of their actions, permanence of online content, or their ability to weigh the risks of their engagement on social media. xvii

Reliance on sleep for healthy brain development

Other than the first year of life, puberty is the most important period of brain growth and reorganization in our lifetimes. xviii Sleep is essential for healthy brain development and mental health in adolescence. xix Sleep delay or disruptions have significant negative effects on youths’ attention, behavior, mood, safety, and academic performance.

• A lack of limits on the time of day when youth can use social media has been cited as the predominant reason why adolescents are getting less than the recommended amount of sleep, with significant implications for brain and mental health. xx

Vulnerability to malicious actors

Youth are easily deceived by predators and other malicious actors who may attempt to interact with them on social media channels. xxi

• Connection and direct messaging with adult strangers places youth at risk of identity theft and potentially dangerous interactions, including sexploitation.

Need for parental/caregiver partnership

Research indicates that youth benefit from parental support to guide them toward safe decisions and to help them understand and appropriately respond to complex social interactions. xxii Granting parents oversight of youths’ accounts should be offered in balance with adolescents’ needs for autonomy, privacy, and independence. However, it should be easier for parents to partner with youth online in a manner that fits their family’s needs.

• The absence of transparent and easy-to-use parental/caregiver tools increases parents’ or guardians’ difficulty in supporting youths’ experience on social media. xxiii

Health advisory on social media use in adolescence

Related topics

• Social media and the internet
• Mental health

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A path forward based on science

Change is needed soon. Solutions should reflect a greater understanding of the science in at least three ways.

First, youth vary considerably in how they use social media. Some uses may promote healthy development and others may create harm. As noted in the APA health advisory , using social media is not inherently beneficial or harmful to young people. The effects of social media depend not only on what teens can do and see online, but teens’ pre-existing strengths or vulnerabilities, and the contexts in which they grow up.

Second, science has highlighted biological and psychological abilities/vulnerabilities that interact with the content, functions, and features built into social media platforms, and it is these aspects of youths’ social media experience that must be addressed to attenuate risks. xxiv Social media use, functionality, and permissions/consenting should be tailored to youths’ developmental capabilities. Design features created for adults may not be appropriate for children.

Third, youth are adept at working around age restrictions. Substantial data reveal a remarkable number of children aged 12 years and younger routinely using social media, indicating that current policies and practices to restrict use to older youth are not working. xxv

Policies will not protect youth unless technology companies are required to reduce the risks embedded within the platforms themselves.

As policymakers at every level assess their approach to this complex issue, it is important to note the limitations of frequently proposed policies, which are often misreported and fall far short of comprehensive safety solutions that will achieve meaningful change.

Restricting downloads

Restricting application downloads at the device level does not fully restrict youths’ access and will not meaningfully improve the safety of social media platforms. Allowing platforms to delegate responsibility to app stores does not address the vulnerabilities and harms built into the platforms.

Requiring age restrictions

Focusing only on age restrictions does not improve the platforms or address the biological and psychological vulnerabilities that persist past age 18. While age restriction proposals could offer some benefits if effectively and equitably implemented, they do not represent comprehensive improvements to social media platforms, for at least four reasons:

• Creating a bright line age limit ignores individual differences in adolescents’ maturity and competency
• These proposals fail to mitigate the harms for those above the age limit and can lead to a perception that social media is safe for adolescents above the threshold age, though neurological changes continue until age 25
• Completely limiting access to social media may disadvantage those who are experiencing psychological benefits from social media platforms, such as community support and access to science-based resources, which particularly impact those in marginalized populations
• The process of age-verification requires more thoughtful consideration to ensure that the storage of official identification documents does not systematically exclude subsets of youth, create risks for leaks, or circumvent the ability of young people to maintain anonymity on social platforms.

Use of parental controls

Granting parents and caregivers greater access to their children’s social media accounts will not address risks embedded within platforms themselves. More robust and easy-to-use parental controls would help some younger age groups, but as a sole strategy, this approach ignores the complexities of adolescent development, the importance of childhood autonomy and privacy, and disparities in time or resources available for monitoring across communities. xxvi

[Related: Keeping teens safe on social media: What parents should know to protect their kids ]

Some parents might be technologically ill-equipped, lack the time or documentation to complete requirements, or simply be unavailable to complete these requirements. Disenfranchising some young people from these platforms creates inequities. xxvii

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1 These recommendations enact policies and resolutions approved by the APA Council of Representatives including the APA Resolution on Child and Adolescent Mental and Behavioral Health and the APA Resolution on Dismantling Systemic Racism in contexts including social media. These are not professional practice guidelines but are intended to provide information based on psychological science.

2 This report seeks to elaborate on extant psychological science findings, which may be particularly relevant in the creation of policy solutions that protect young people, and to inform the development of social media safety standards.

Recommendations from APA’s health advisory on social media use in adolescence

• Youth using social media should be encouraged to use functions that create opportunities for social support, online companionship, and emotional intimacy that can promote healthy socialization.
• Social media use, functionality, and permissions/consenting should be tailored to youths’ developmental capabilities; designs created for adults may not be appropriate for children.
• In early adolescence (i.e., typically 10–14 years), adult monitoring (i.e., ongoing review, discussion, and coaching around social media content) is advised for most youths’ social media use; autonomy may increase gradually as kids age and if they gain digital literacy skills. However, monitoring should be balanced with youths’ appropriate needs for privacy.
• To reduce the risks of psychological harm, adolescents’ exposure to content on social media that depicts illegal or psychologically maladaptive behavior, including content that instructs or encourages youth to engage in health-risk behaviors, such as self-harm (e.g., cutting, suicide), harm to others, or those that encourage eating-disordered behavior (e.g., restrictive eating, purging, excessive exercise) should be minimized, reported, and removed; moreover, technology should not drive users to this content.
• To minimize psychological harm, adolescents’ exposure to “cyberhate” including online discrimination, prejudice, hate, or cyberbullying especially directed toward a marginalized group (e.g., racial, ethnic, gender, sexual, religious, ability status), or toward an individual because of their identity or allyship with a marginalized group should be minimized.
• Adolescents should be routinely screened for signs of “problematic social media use” that can impair their ability to engage in daily roles and routines, and may present risk for more serious psychological harms over time.
• The use of social media should be limited so as to not interfere with adolescents’ sleep and physical activity.
• Adolescents should limit use of social media for social comparison, particularly around beauty- or appearance-related content.
• Adolescents’ social media use should be preceded by training in social media literacy to ensure that users have developed psychologically-informed competencies and skills that will maximize the chances for balanced, safe, and meaningful social media use.
• Substantial resources should be provided for continued scientific examination of the positive and negative effects of social media on adolescent development.

Acknowledgments

We wish to acknowledge the outstanding contributions to this report made by the following individuals:

Expert advisory panel

Mary Ann McCabe, PhD, ABPP, member-at-large, Board of Directors, American Psychological Association; associate clinical professor of pediatrics, The George Washington University School of Medicine and Health Sciences

Mitchell J. Prinstein, PhD, ABPP, chief science officer, American Psychological Association; John Van Seters Distinguished Professor of Psychology and Neuroscience, University of North Carolina at Chapel Hill

Mary K. Alvord, PhD, founder, Alvord, Baker & Associates; board president, Resilience Across Borders; adjunct associate professor of psychiatry and behavioral sciences, The George Washington University School of Medicine and Health Sciences

Dawn T. Bounds, PhD, PMHNP-BC, FAAN, assistant professor, Sue & Bill Gross School of Nursing, University of California, Irvine

Linda Charmaraman, PhD, senior research scientist, Wellesley Centers for Women, Wellesley College

Sophia Choukas-Bradley, PhD, assistant professor, Department of Psychology, University of Pittsburgh

Dorothy L. Espelage, PhD, William C. Friday Distinguished Professor of Education, University of North Carolina at Chapel Hill

Joshua A. Goodman, PhD, assistant professor, Department of Psychology, Southern Oregon University

Jessica L. Hamilton, PhD, assistant professor, Department of Psychology, Rutgers University

Brendesha M. Tynes, PhD, Dean’s Professor of Educational Equity, University of Southern California

L. Monique Ward, PhD, professor, Department of Psychology (Developmental), University of Michigan

Lucía Magis-Weinberg, MD, PhD, assistant professor, Department of Psychology, University of Washington

We also wish to acknowledge the contributions to this report made by Katherine B. McGuire, chief advocacy officer, and Corbin Evans, JD, senior director of congressional and federal relations, American Psychological Association.

Selected references

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Research/Study Research/Study

Right-wing media spread misinformation about Harris campaign tax proposal that would impact only the wealthiest Americans

Harris proposed an unrealized capital gains tax provision that would impact only certain American households worth more than $100 million

Written by Pete Tsipis & Reed McMaster

Published 08/28/24 3:44 PM EDT

During the Democratic National Convention, Vice President Kamala Harris’ presidential campaign told multiple outlets that she would support a provision — which was included in the Biden-Harris administration’s federal budget proposal — to tax unrealized capital gains of the ultrawealthy. The policy would affect only those with $100 million or more in tradable assets. 

In response, right-wing media repeatedly and falsely claimed that the proposal would actually impact everyday Americans, retirees, and independent businesses and that it could severely damage the economy.

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The harris campaign has signaled support for a tax on the ultra-wealthy, right-wing media are pushing misinformation about the policy and who it would affect, right-wing media demonized harris’ tax policies by calling them “wealth confiscation,” “communism,” and “full-scale socialism” and fearmongering that they would lead to economic turmoil like a stock market crash.

• The Biden administration proposed, and Harris said she would support, at least a 25% income tax on certain people with over $100 million in assets that include unrealized capital gains. Harris reiterated her support for a provision included in the administration's proposed budget for the 2025 fiscal year, which would apply only to wealth held by high-net-worth households. Unrealized capital gains are increases in the value of an asset that has not yet been sold, like a company, stock, or property. Currently, unrealized gains are not taxed. [Axios, 8/23/24 ; Investopedia, 6/9/24 ]
• The proposed tax on unrealized gains would apply only to those who have 80% of their $100 million-plus wealth in tradable assets (i.e. stocks). Axios’ Dan Primack wrote, “Within that $100 million club, you'd only pay taxes on unrealized capital gains if at least 80% of your wealth is in tradeable assets (i.e., not shares of private startups or real estate). One caveat for this illiquid group is that there would be a deferred tax of up to 10% on unrealized capital gains upon exit.” [Axios, 8/23/24 ]  
• Daily Wire co-founder Ben Shapiro claimed that a 25% tax on unrealized capital gains would bankrupt “half the companies in existence.” Shapiro called the policy proposal “psychotic” and “insane,” but he failed to mention that the policy would apply only to those worth more than $100 million with 80% in tradable assets. [The Daily Wire, The Ben Shapiro Show , 8/21/24 ; Media Matters, 8/21/24 ]
• Fox host Sean Hannity falsely claimed on his radio show that anyone with a 401(k) would have to pay new taxes on unrealized capital gains. He argued, “if you think inflation is bad, that's inflation in perpetuity. ‘Oh, but we're not gonna tax people that make under $400,000 a year.’ Well, apparently, if you have a 401(k) or retirement account of any kind, guess what? You're gonna pay taxes on unrealized capital gains. … She wants to raise the capital gains tax to an enormous rate.” (Someone making $400,000 is earning 250 times less than the tax threshold for this unrealized capital gains provision.) [Premiere Radio Networks, The Sean Hannity Show , 8/23/24 ]
• Fox Business host Dagen McDowell said the proposed tax on unrealized gains would involve “punishing business creation” and falsely claimed it would “starve startups” even though it wouldn’t apply to shares of privately owned startups. She added, “They don’t like anything they are not in control of. They are politicians, and so if they can’t control you, they will destroy you.” [Fox Business, The Big Money Show , 8/22/24 ]
• Fox host Jesse Watters suggested the unrealized capital gains tax would affect all stock portfolios, when it would actually impact only the portfolios of hundred-millionaires. He stated, “Kamala did kind of play footsie with a new policy today. She said she wants to raise taxes. She said she wants to raise capital gains taxes. And then she says she wants a wealth tax. She wants to tax unrealized gains, which means if your portfolio goes up, and you don’t sell, you just hold it, she wants to tax that.” [Fox News, Jesse Watters Primetime , 8/20/24 ]
• Fox Business host Maria Bartiromo claimed taxing unrealized capital gains at 25% would trickle down and impact the middle class. She stated, “You’ve got to zero-in on some of these taxes that Kamala Harris is backing. Like 44.6% capital gains tax. I mean, what is that going to do to the economy and the markets, you know? Or a 25% tax on unrealized gains. And of course, taking the corporate tax rate up to 28%. You know, she talks about cutting taxes for the middle class, but all of this trickles down. And when you’re talking about a 25% tax on unrealized gains, people need to understand what that means. That's not just about the stock market.” [Fox Business, Mornings with Maria , 8/23/24 ]
• Hannity falsely claimed that any American with a retirement plan would have to pay new taxes on unrealized capital gains. He stated, “Look at the tax policy tonight. … If you have a 401(k), if you have a retirement plan of any kind, and you have unrealized capital gains, they will tax those, and they’re not going to ask if you’re making $400,000 a year. Now, reality check, most Americans that work for a living, that don’t make anywhere near $400,000 a year, that means they’re going to get taxed.” Hannity seems to be confusing or conflating standard taxes with Harris' new proposal, which would affect only extremely wealthy households. [Fox News, Hannity , 8/22/24 ; Investopedia, 8/20/24 ]
• Fox host Greg Gutfeld repeatedly called the taxation of unrealized gains code for “theft” and fearmongered about the policy being turned on poor people, even though the policy is targeted at the rich. He ranted, “So a tax on unrealized gains is taxing money you don't have. And it goes right to the government. So to sell a corrupt, immoral idea, you've got to call it something else, so you call it — instead of ‘theft,’ it’s ‘unrealized gains.’ … So an unrealized gain is the house that your parents live in. … If it does gain in value, 25 to 50% of that increase goes to the IRS every year.” He continued. “They’re gonna say, ‘But we're only going to do this to really rich people,’ but as you know sooner or later, we run out of really rich people, then it’s rich people, then it’s not so rich people, then it’s poor people.” [Fox News, The Five , 8/21/24 ]
• Newsmax host Bob Sellers claimed that the tax proposal would only initially target the ultra-rich before being used to go after real estate. He claimed, “Now, it isn't necessarily going to affect you or me. They're talking about the ultimate – the ultra-rich. Yeah. That's what they're saying now. But once they get that concept through, they'll start looking more and more. And say, ‘Well, you know, you haven't sold your house. It's worth an awful lot. There's a lot of wealth in there.’ And I'm not saying that's what they're going to do. But once you take that step, it's a slippery slope.” [Newsmax, The Record with Greta Van Susteren , 8/22/24 ]  
• Fox Business host Stuart Varney called Harris’ tax proposals “full-scale socialism.” He stated, “I say she has been listening to Bernie Sanders, who last night delivered a full-throated roar for socialism. All of the above that you just mentioned, which is now Kamala Harris’ policy, that’s exactly what Bernie Sanders wants. You’ve got a drift here to full-scale socialism.” [Fox Business, Mornings with Maria , 8/21/24 ]
• Hannity attacked Kamala’s tax policies, arguing they’re basically “wealth confiscation.” He claimed, “She wants to tax small business. She wants to tax corporations. She wants to put a tax pretty much on anything. She wants a wealth tax. She is open to a 70 to 80% top marginal tax rate. She wants to tax capital gains. She wants to tax unrealized capital gains. She wants to raise the estate tax. Well, basically, it's wealth confiscation, but she's gonna need every penny of it, and it still won't be enough if she wants her $93 trillion Green New Deal ever passed.” [Premiere Radio Networks, The Sean Hannity Show , 8/19/24 ]
• Turning Point USA founder Charlie Kirk called the proposal “communist” and said the tax on capital gains “is a recipe for a stock market crash.” He stated, “This is communist. … Taxing unrealized gains would force investors to sell off assets to cover their tax bills, hurting long term investments and economic growth. This is a recipe for a stock market crash.” [Rumble, The Charlie Kirk Show , 8/21/24 ] 
• Former presidential candidate Vivek Ramaswamy claimed the policy is part of “a formula for Great Depression.” He stated, “Lay out Kamala in her own words, including in her presidential campaign, what she stood for, single payer health care all the way to taxes on unrealized capital gains, it’s a formula for Great Depression. Yes, remind the American voters of that.” [Fox News, Hannity , 8/19/24 ]

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Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice

John a. naslund.

a Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA

Ameya Bondre

b CareNX Innovations, Mumbai, India

John Torous

c Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA

Kelly A. Aschbrenner

d Department of Psychiatry, Geisel School of Medicine at Dartmouth, Lebanon, NH

Social media platforms are popular venues for sharing personal experiences, seeking information, and offering peer-to-peer support among individuals living with mental illness. With significant shortfalls in the availability, quality, and reach of evidence-based mental health services across the United States and globally, social media platforms may afford new opportunities to bridge this gap. However, caution is warranted, as numerous studies highlight risks of social media use for mental health. In this commentary, we consider the role of social media as a potentially viable intervention platform for offering support to persons with mental disorders, promoting engagement and retention in care, and enhancing existing mental health services. Specifically, we summarize current research on the use of social media among mental health service users, and early efforts using social media for the delivery of evidence-based programs. We also review the risks, potential harms, and necessary safety precautions with using social media for mental health. To conclude, we explore opportunities using data science and machine learning, for example by leveraging social media for detecting mental disorders and developing predictive models aimed at characterizing the aetiology and progression of mental disorders. These various efforts using social media, as summarized in this commentary, hold promise for improving the lives of individuals living with mental disorders.

Introduction

Social media has become a prominent fixture in the lives of many individuals facing the challenges of mental illness. Social media refers broadly to web and mobile platforms that allow individuals to connect with others within a virtual network (such as Facebook, Twitter, Instagram, Snapchat, or LinkedIn), where they can share, co-create, or exchange various forms of digital content, including information, messages, photos, or videos ( Ahmed, Ahmad, Ahmad, & Zakaria, 2019 ). Studies have reported that individuals living with a range of mental disorders, including depression, psychotic disorders, or other severe mental illnesses, use social media platforms at comparable rates as the general population, with use ranging from about 70% among middle-age and older individuals, to upwards of 97% among younger individuals ( Aschbrenner, Naslund, Grinley, et al., 2018 ; M. L. Birnbaum, Rizvi, Correll, Kane, & Confino, 2017 ; Brunette et al., 2019 ; Naslund, Aschbrenner, & Bartels, 2016 ). Other exploratory studies have found that many of these individuals with mental illness appear to turn to social media to share their personal experiences, seek information about their mental health and treatment options, and give and receive support from others facing similar mental health challenges ( Bucci, Schwannauer, & Berry, 2019 ; Naslund, Aschbrenner, Marsch, & Bartels, 2016b ).

Across the United States and globally, very few people living with mental illness have access to adequate mental health services ( Patel et al., 2018 ). The wide reach and near ubiquitous use of social media platforms may afford novel opportunities to address these shortfalls in existing mental health care, by enhancing the quality, availability, and reach of services. Recent studies have explored patterns of social media use, impact of social media use on mental health and wellbeing, and the potential to leverage the popularity and interactive features of social media to enhance the delivery of interventions. However, there remains uncertainty regarding the risks and potential harms of social media for mental health ( Orben & Przybylski, 2019 ), and how best to weigh these concerns against potential benefits.

In this commentary, we summarized current research on the use of social media among individuals with mental illness, with consideration of the impact of social media on mental wellbeing, as well as early efforts using social media for delivery of evidence-based programs for addressing mental health problems. We searched for recent peer reviewed publications in Medline and Google Scholar using the search terms “mental health” or “mental illness” and “social media”, and searched the reference lists of recent reviews and other relevant studies. We reviewed the risks, potential harms, and necessary safety precautions with using social media for mental health. Overall, our goal was to consider the role of social media as a potentially viable intervention platform for offering support to persons with mental disorders, promoting engagement and retention in care, and enhancing existing mental health services, while balancing the need for safety. Given this broad objective, we did not perform a systematic search of the literature and we did not apply specific inclusion criteria based on study design or type of mental disorder.

Social Media Use and Mental Health

In 2020, there are an estimated 3.8 billion social media users worldwide, representing half the global population ( We Are Social, 2020 ). Recent studies have shown that individuals with mental disorders are increasingly gaining access to and using mobile devices, such as smartphones ( Firth et al., 2015 ; Glick, Druss, Pina, Lally, & Conde, 2016 ; Torous, Chan, et al., 2014 ; Torous, Friedman, & Keshavan, 2014 ). Similarly, there is mounting evidence showing high rates of social media use among individuals with mental disorders, including studies looking at engagement with these popular platforms across diverse settings and disorder types. Initial studies from 2015 found that nearly half of a sample of psychiatric patients were social media users, with greater use among younger individuals ( Trefflich, Kalckreuth, Mergl, & Rummel-Kluge, 2015 ), while 47% of inpatients and outpatients with schizophrenia reported using social media, of which 79% reported at least once-a-week usage of social media websites ( Miller, Stewart, Schrimsher, Peeples, & Buckley, 2015 ). Rates of social media use among psychiatric populations have increased in recent years, as reflected in a study with data from 2017 showing comparable rates of social media use (approximately 70%) among individuals with serious mental illness in treatment as compared to low-income groups from the general population ( Brunette et al., 2019 ).

Similarly, among individuals with serious mental illness receiving community-based mental health services, a recent study found equivalent rates of social media use as the general population, even exceeding 70% of participants ( Naslund, Aschbrenner, & Bartels, 2016 ). Comparable findings were demonstrated among middle-age and older individuals with mental illness accessing services at peer support agencies, where 72% of respondents reported using social media ( Aschbrenner, Naslund, Grinley, et al., 2018 ). Similar results, with 68% of those with first episode psychosis using social media daily were reported in another study ( Abdel-Baki, Lal, D.-Charron, Stip, & Kara, 2017 ).

Individuals who self-identified as having a schizophrenia spectrum disorder responded to a survey shared through the National Alliance of Mental Illness (NAMI), and reported that visiting social media sites was one of their most common activities when using digital devices, taking up roughly 2 hours each day ( Gay, Torous, Joseph, Pandya, & Duckworth, 2016 ). For adolescents and young adults ages 12 to 21 with psychotic disorders and mood disorders, over 97% reported using social media, with average use exceeding 2.5 hours per day ( M. L. Birnbaum et al., 2017 ). Similarly, in a sample of adolescents ages 13-18 recruited from community mental health centers, 98% reported using social media, with YouTube as the most popular platform, followed by Instagram and Snapchat ( Aschbrenner et al., 2019 ).

Research has also explored the motivations for using social media as well as the perceived benefits of interacting on these platforms among individuals with mental illness. In the sections that follow (see Table 1 for a summary), we consider three potentially unique features of interacting and connecting with others on social media that may offer benefits for individuals living with mental illness. These include: 1) Facilitate social interaction; 2) Access to a peer support network; and 3) Promote engagement and retention in services.

Summary of potential benefits and challenges with social media for mental health

Facilitate Social Interaction

Social media platforms offer near continuous opportunities to connect and interact with others, regardless of time of day or geographic location. This on demand ease of communication may be especially important for facilitating social interaction among individuals with mental disorders experiencing difficulties interacting in face-to-face settings. For example, impaired social functioning is a common deficit in schizophrenia spectrum disorders, and social media may facilitate communication and interacting with others for these individuals ( Torous & Keshavan, 2016 ). This was suggested in one study where participants with schizophrenia indicated that social media helped them to interact and socialize more easily ( Miller et al., 2015 ). Like other online communication, the ability to connect with others anonymously may be an important feature of social media, especially for individuals living with highly stigmatizing health conditions ( Berger, Wagner, & Baker, 2005 ), such as serious mental disorders ( Highton-Williamson, Priebe, & Giacco, 2015 ).

Studies have found that individuals with serious mental disorders ( Spinzy, Nitzan, Becker, Bloch, & Fennig, 2012 ) as well as young adults with mental illness ( Gowen, Deschaine, Gruttadara, & Markey, 2012 ) appear to form online relationships and connect with others on social media as often as social media users from the general population. This is an important observation because individuals living with serious mental disorders typically have few social contacts in the offline world, and also experience high rates of loneliness ( Badcock et al., 2015 ; Giacco, Palumbo, Strappelli, Catapano, & Priebe, 2016 ). Among individuals receiving publicly funded mental health services who use social media, nearly half (47%) reported using these platforms at least weekly to feel less alone ( Brusilovskiy, Townley, Snethen, & Salzer, 2016 ). In another study of young adults with serious mental illness, most indicated that they used social media to help feel less isolated ( Gowen et al., 2012 ). Interestingly, more frequent use of social media among a sample of individuals with serious mental illness was associated with greater community participation, measured as participation in shopping, work, religious activities or visiting friends and family, as well as greater civic engagement, reflected as voting in local elections ( Brusilovskiy et al., 2016 ).

Emerging research also shows that young people with moderate to severe depressive symptoms appear to prefer communicating on social media rather than in-person ( Rideout & Fox, 2018 ), while other studies have found that some individuals may prefer to seek help for mental health concerns online rather than through in-person encounters ( Batterham & Calear, 2017 ). In a qualitative study, participants with schizophrenia described greater anonymity, the ability to discover that other people have experienced similar health challenges, and reducing fears through greater access to information as important motivations for using the Internet to seek mental health information ( Schrank, Sibitz, Unger, & Amering, 2010 ). Because social media does not require the immediate responses necessary in face-to-face communication, it may overcome deficits with social interaction due to psychotic symptoms that typically adversely affect face-to-face conversations ( Docherty et al., 1996 ). Online social interactions may not require the use of non-verbal cues, particularly in the initial stages of interaction ( Kiesler, Siegel, & McGuire, 1984 ), with interactions being more fluid, and within the control of users, thereby overcoming possible social anxieties linked to in-person interaction ( Indian & Grieve, 2014 ). Furthermore, many individuals with serious mental disorders can experience symptoms including passive social withdrawal, blunted affect and attentional impairment, as well as active social avoidance due to hallucinations or other concerns ( Hansen, Torgalsbøen, Melle, & Bell, 2009 ); thus, potentially reinforcing the relative advantage, as perceived by users, of using social media over in person conversations.

Access to a Peer Support Network

There is growing recognition about the role that social media channels could play in enabling peer support ( Bucci et al., 2019 ; Naslund, Aschbrenner, et al., 2016b ), referred to as a system of mutual giving and receiving where individuals who have endured the difficulties of mental illness can offer hope, friendship, and support to others facing similar challenges ( Davidson, Chinman, Sells, & Rowe, 2006 ; Mead, Hilton, & Curtis, 2001 ). Initial studies exploring use of online self-help forums among individuals with serious mental illnesses have found that individuals with schizophrenia appeared to use these forums for self-disclosure, and sharing personal experiences, in addition to providing or requesting information, describing symptoms, or discussing medication ( Haker, Lauber, & Rössler, 2005 ), while users with bipolar disorder reported using these forums to ask for help from others about their illness ( Vayreda & Antaki, 2009 ). More recently, in a review of online social networking in people with psychosis, Highton-Williamson et al (2015) highlight that an important purpose of such online connections was to establish new friendships, pursue romantic relationships, maintain existing relationships or reconnect with people, and seek online peer support from others with lived experience ( Highton-Williamson et al., 2015 ).

Online peer support among individuals with mental illness has been further elaborated in various studies. In a content analysis of comments posted to YouTube by individuals who self-identified as having a serious mental illness, there appeared to be opportunities to feel less alone, provide hope, find support and learn through mutual reciprocity, and share coping strategies for day-to-day challenges of living with a mental illness ( Naslund, Grande, Aschbrenner, & Elwyn, 2014 ). In another study, Chang (2009) delineated various communication patterns in an online psychosis peer-support group ( Chang, 2009 ). Specifically, different forms of support emerged, including ‘informational support’ about medication use or contacting mental health providers, ‘esteem support’ involving positive comments for encouragement, ‘network support’ for sharing similar experiences, and ‘emotional support’ to express understanding of a peer’s situation and offer hope or confidence ( Chang, 2009 ). Bauer et al. (2013) reported that the main interest in online self-help forums for patients with bipolar disorder was to share emotions with others, allow exchange of information, and benefit by being part of an online social group ( Bauer, Bauer, Spiessl, & Kagerbauer, 2013 ).

For individuals who openly discuss mental health problems on Twitter, a study by Berry et al. (2017) found that this served as an important opportunity to seek support and to hear about the experiences of others ( Berry et al., 2017 ). In a survey of social media users with mental illness, respondents reported that sharing personal experiences about living with mental illness and opportunities to learn about strategies for coping with mental illness from others were important reasons for using social media ( Naslund et al., 2017 ). A computational study of mental health awareness campaigns on Twitter provides further support with inspirational posts and tips being the most shared ( Saha et al., 2019 ). Taken together, these studies offer insights about the potential for social media to facilitate access to an informal peer support network, though more research is necessary to examine how these online interactions may impact intentions to seek care, illness self-management, and clinically meaningful outcomes in offline contexts.

Promote Engagement and Retention in Services

Many individuals living with mental disorders have expressed interest in using social media platforms for seeking mental health information ( Lal, Nguyen, & Theriault, 2018 ), connecting with mental health providers ( M. L. Birnbaum et al., 2017 ), and accessing evidence-based mental health services delivered over social media specifically for coping with mental health symptoms or for promoting overall health and wellbeing ( Naslund et al., 2017 ). With the widespread use of social media among individuals living with mental illness combined with the potential to facilitate social interaction and connect with supportive peers, as summarized above, it may be possible to leverage the popular features of social media to enhance existing mental health programs and services. A recent review by Biagianti et al (2018) found that peer-to-peer support appeared to offer feasible and acceptable ways to augment digital mental health interventions for individuals with psychotic disorders by specifically improving engagement, compliance, and adherence to the interventions, and may also improve perceived social support ( Biagianti, Quraishi, & Schlosser, 2018 ).

Among digital programs that have incorporated peer-to-peer social networking consistent with popular features on social media platforms, a pilot study of the HORYZONS online psychosocial intervention demonstrated significant reductions in depression among patients with first episode psychosis ( Alvarez-Jimenez et al., 2013 ). Importantly, the majority of participants (95%) in this study engaged with the peer-to-peer networking feature of the program, with many reporting increases in perceived social connectedness and empowerment in their recovery process ( Alvarez-Jimenez et al., 2013 ). This moderated online social therapy program is now being evaluated as part of a large randomized controlled trial for maintaining treatment effects from first episode psychosis services ( Alvarez-Jimenez et al., 2019 ).

Other early efforts have demonstrated that use of digital environments with the interactive peer-to-peer features of social media can enhance social functioning and wellbeing in young people at high risk of psychosis ( Alvarez-Jimenez et al., 2018 ). There has also been a recent emergence of several mobile apps to support symptom monitoring and relapse prevention in psychotic disorders. Among these apps, the development of PRIME (Personalized Real-time Intervention for Motivational Enhancement) has involved working closely with young people with schizophrenia to ensure that the design of the app has the look and feel of mainstream social media platforms, as opposed to existing clinical tools ( Schlosser et al., 2016 ). This unique approach to the design of the app is aimed at promoting engagement, and ensuring that the app can effectively improve motivation and functioning through goal setting and promoting better quality of life of users with schizophrenia ( Schlosser et al., 2018 ).

Social media platforms could also be used to promote engagement and participation in in-person services delivered through community mental health settings. For example, the peer-based lifestyle intervention called PeerFIT targets weight loss and improved fitness among individuals living with serious mental illness through a combination of in-person lifestyle classes, exercise groups, and use of digital technologies ( Aschbrenner, Naslund, Shevenell, Kinney, & Bartels, 2016 ; Aschbrenner, Naslund, Shevenell, Mueser, & Bartels, 2016 ). The intervention holds tremendous promise as lack of support is one of the largest barriers toward exercise in patients with serious mental illness ( Firth et al., 2016 ) and it is now possible to use social media to counter such. Specifically, in PeerFIT, a private Facebook group is closely integrated into the program to offer a closed platform where participants can connect with the lifestyle coaches, access intervention content, and support or encourage each other as they work towards their lifestyle goals ( Aschbrenner, Naslund, & Bartels, 2016 ; Naslund, Aschbrenner, Marsch, & Bartels, 2016a ). To date, this program has demonstrate preliminary effectiveness for meaningfully reducing cardiovascular risk factors that contribute to early mortality in this patient group ( Aschbrenner, Naslund, Shevenell, Kinney, et al., 2016 ), while the Facebook component appears to have increased engagement in the program, while allowing participants who were unable to attend in-person sessions due to other health concerns or competing demands to remain connected with the program ( Naslund, Aschbrenner, Marsch, McHugo, & Bartels, 2018 ). This lifestyle intervention is currently being evaluated in a randomized controlled trial enrolling young adults with serious mental illness from a variety of real world community mental health services settings ( Aschbrenner, Naslund, Gorin, et al., 2018 ).

These examples highlight the promise of incorporating the features of popular social media into existing programs, which may offer opportunities to safely promote engagement and program retention, while achieving improved clinical outcomes. This is an emerging area of research, as evidenced by several important effectiveness trials underway ( Alvarez-Jimenez et al., 2019 ; Aschbrenner, Naslund, Gorin, et al., 2018 ), including efforts to leverage online social networking to support family caregivers of individuals receiving first episode psychosis services ( Gleeson et al., 2017 ).

Challenges with Social Media for Mental Health

The science on the role of social media for engaging persons with mental disorders needs a cautionary note on the effects of social media usage on mental health and well being, particularly in adolescents and young adults. While the risks and harms of social media are frequently covered in the popular press and mainstream news reports, careful consideration of the research in this area is necessary. In a review of 43 studies in young people, many benefits of social media were cited, including increased self-esteem, and opportunities for self-disclosure ( Best, Manktelow, & Taylor, 2014 ). Yet, reported negative effects were an increased exposure to harm, social isolation, depressive symptoms and bullying ( Best et al., 2014 ). In the sections that follow (see Table 1 for a summary), we consider three major categories of risk related to use of social media and mental health. These include: 1) Impact on symptoms; 2) Facing hostile interactions; and 3) Consequences for daily life.

Impact on Symptoms

Studies consistently highlight that use of social media, especially heavy use and prolonged time spent on social media platforms, appears to contribute to increased risk for a variety of mental health symptoms and poor wellbeing, especially among young people ( Andreassen et al., 2016 ; Kross et al., 2013 ; Woods & Scott, 2016 ). This may partly be driven by the detrimental effects of screen time on mental health, including increased severity of anxiety and depressive symptoms, which have been well documented ( Stiglic & Viner, 2019 ). Recent studies have reported negative effects of social media use on mental health of young people, including social comparison pressure with others and greater feeling of social isolation after being rejected by others on social media ( Rideout & Fox, 2018 ). In a study of young adults, it was found that negative comparisons with others on Facebook contributed to risk of rumination and subsequent increases in depression symptoms ( Feinstein et al., 2013 ). Still, the cross sectional nature of many screen time and mental health studies makes it challenging to reach causal inferences ( Orben & Przybylski, 2019 ).

Quantity of social media use is also an important factor, as highlighted in a survey of young adults ages 19 to 32, where more frequent visits to social media platforms each week were correlated with greater depressive symptoms ( Lin et al., 2016 ). More time spent using social media is also associated with greater symptoms of anxiety ( Vannucci, Flannery, & Ohannessian, 2017 ). The actual number of platforms accessed also appears to contribute to risk as reflected in another national survey of young adults where use of a large number of social media platforms was associated with negative impact on mental health ( Primack et al., 2017 ). Among survey respondents using between 7 and 11 different social media platforms compared to respondents using only 2 or fewer platforms, there was a 3 times greater odds of having high levels of depressive symptoms and a 3.2 times greater odds of having high levels of anxiety symptoms ( Primack et al., 2017 ).

Many researchers have postulated that worsening mental health attributed to social media use may be because social media replaces face-to-face interactions for young people ( Twenge & Campbell, 2018 ), and may contribute to greater loneliness ( Bucci et al., 2019 ), and negative effects on other aspects of health and wellbeing ( Woods & Scott, 2016 ). One nationally representative survey of US adolescents found that among respondents who reported more time accessing media such as social media platforms or smartphone devices, there was significantly greater depressive symptoms and increased risk of suicide when compared to adolescents who reported spending more time on non-screen activities, such as in-person social interaction or sports and recreation activities ( Twenge, Joiner, Rogers, & Martin, 2018 ). For individuals living with more severe mental illnesses, the effects of social media on psychiatric symptoms have received less attention. One study found that participation in chat rooms may contribute to worsening symptoms in young people with psychotic disorders ( Mittal, Tessner, & Walker, 2007 ), while another study of patients with psychosis found that social media use appeared to predict low mood ( Berry, Emsley, Lobban, & Bucci, 2018 ). These studies highlight a clear relationship between social media use and mental health that may not be present in general population studies ( Orben & Przybylski, 2019 ), and emphasize the need to explore how social media may contribute to symptom severity and whether protective factors may be identified to mitigate these risks.

Facing Hostile Interactions

Popular social media platforms can create potential situations where individuals may be victimized by negative comments or posts. Cyberbullying represents a form of online aggression directed towards specific individuals, such as peers or acquaintances, which is perceived to be most harmful when compared to random hostile comments posted online ( Hamm et al., 2015 ). Importantly, cyberbullying on social media consistently shows harmful impact on mental health in the form of increased depressive symptoms as well as worsening of anxiety symptoms, as evidenced in a review of 36 studies among children and young people ( Hamm et al., 2015 ). Furthermore, cyberbullying disproportionately impacts females as reflected in a national survey of adolescents in the United States, where females were twice as likely to be victims of cyberbullying compared to males ( Alhajji, Bass, & Dai, 2019 ). Most studies report cross-sectional associations between cyberbullying and symptoms of depression or anxiety ( Hamm et al., 2015 ), though one longitudinal study in Switzerland found that cyberbullying contributed to significantly greater depression over time ( Machmutow, Perren, Sticca, & Alsaker, 2012 ).

For youth ages 10 to 17 who reported major depressive symptomatology, there was over 3 times greater odds of facing online harassment in the last year compared to youth who reported mild or no depressive symptoms ( Ybarra, 2004 ). Similarly, in a 2018 national survey of young people, respondents ages 14 to 22 with moderate to severe depressive symptoms were more likely to have had negative experiences when using social media, and in particular, were more likely to report having faced hostile comments, or being “trolled”, from others when compared to respondents without depressive symptoms (31% vs. 14%) ( Rideout & Fox, 2018 ). As these studies depict risks for victimization on social media and the correlation with poor mental health, it is possible that individuals living with mental illness may also experience greater hostility online compared to individuals without mental illness. This would be consistent with research showing greater risk of hostility, including increased violence and discrimination, directed towards individuals living with mental illness in in-person contexts, especially targeted at those with severe mental illnesses ( Goodman et al., 1999 ).

A computational study of mental health awareness campaigns on Twitter reported that while stigmatizing content was rare, it was actually the most spread (re-tweeted) demonstrating that harmful content can travel quickly on social media ( Saha et al., 2019 ). Another study was able to map the spread of social media posts about the Blue Whale Challenge, an alleged game promoting suicide, over Twitter, YouTube, Reddit, Tumblr and other forums across 127 countries ( Sumner et al., 2019 ). These findings show that it is critical to monitor the actual content of social media posts, such as determining whether content is hostile or promotes harm to self or others. This is pertinent because existing research looking at duration of exposure cannot account for the impact of specific types of content on mental health and is insufficient to fully understand the effects of using these platforms on mental health.

Consequences for Daily Life

The ways in which individuals use social media can also impact their offline relationships and everyday activities. To date, reports have described risks of social media use pertaining to privacy, confidentiality, and unintended consequences of disclosing personal health information online ( Torous & Keshavan, 2016 ). Additionally, concerns have been raised about poor quality or misleading health information shared on social media, and that social media users may not be aware of misleading information or conflicts of interest especially when the platforms promote popular content regardless of whether it is from a trustworthy source ( Moorhead et al., 2013 ; Ventola, 2014 ). For persons living with mental illness there may be additional risks from using social media. A recent study that specifically explored the perspectives of social media users with serious mental illnesses, including participants with schizophrenia spectrum disorders, bipolar disorder, or major depression, found that over one third of participants expressed concerns about privacy when using social media ( Naslund & Aschbrenner, 2019 ). The reported risks of social media use were directly related to many aspects of everyday life, including concerns about threats to employment, fear of stigma and being judged, impact on personal relationships, and facing hostility or being hurt ( Naslund & Aschbrenner, 2019 ). While few studies have specifically explored the dangers of social media use from the perspectives of individuals living with mental illness, it is important to recognize that use of these platforms may contribute to risks that extend beyond worsening symptoms and that can affect different aspects of daily life.

In this commentary we considered ways in which social media may yield benefits for individuals living with mental illness, while contrasting these with the possible harms. Studies reporting on the threats of social media for individuals with mental illness are mostly cross-sectional, making it difficult to draw conclusions about direction of causation. However, the risks are potentially serious. These risks should be carefully considered in discussions pertaining to use of social media and the broader use of digital mental health technologies, as avenues for mental health promotion, or for supporting access to evidence-based programs or mental health services. At this point, it would be premature to view the benefits of social media as outweighing the possible harms, when it is clear from the studies summarized here that social media use can have negative effects on mental health symptoms, can potentially expose individuals to hurtful content and hostile interactions, and can result in serious consequences for daily life, including threats to employment and personal relationships. Despite these risks, it is also necessary to recognize that individuals with mental illness will continue to use social media given the ease of accessing these platforms and the immense popularity of online social networking. With this in mind, it may be ideal to raise awareness about these possible risks so that individuals can implement necessary safeguards, while also highlighting that there could also be benefits. For individuals with mental illness who use social media, being aware of the risks is an essential first step, and then highlighting ways that use of these popular platforms could also contribute to some benefits, ranging from finding meaningful interactions with others, engaging with peer support networks, and accessing information and services.

To capitalize on the widespread use of social media, and to achieve the promise that these platforms may hold for supporting the delivery of targeted mental health interventions, there is need for continued research to better understand how individuals living with mental illness use social media. Such efforts could inform safety measures and also encourage use of social media in ways that maximize potential benefits while minimizing risk of harm. It will be important to recognize how gender and race contribute to differences in use of social media for seeking mental health information or accessing interventions, as well as differences in how social media might impact mental wellbeing. For example, a national survey of 14- to 22-year olds in the United States found that female respondents were more likely to search online for information about depression or anxiety, and to try to connect with other people online who share similar mental health concerns, when compared to male respondents ( Rideout & Fox, 2018 ). In the same survey, there did not appear to be any differences between racial or ethnic groups in social media use for seeking mental health information ( Rideout & Fox, 2018 ). Social media use also appears to have a differential impact on mental health and emotional wellbeing between females and males ( Booker, Kelly, & Sacker, 2018 ), highlighting the need to explore unique experiences between gender groups to inform tailored programs and services. Research shows that lesbian, gay, bisexual or transgender individuals frequently use social media for searching for health information and may be more likely compared to heterosexual individuals to share their own personal health experiences with others online ( Rideout & Fox, 2018 ). Less is known about use of social media for seeking support for mental health concerns among gender minorities, though this is an important area for further investigation as these individuals are more likely to experience mental health problems and more likely to experience online victimization when compared to heterosexual individuals ( Mereish, Sheskier, Hawthorne, & Goldbach, 2019 ).

Similarly, efforts are needed to explore the relationship between social media use and mental health among ethnic and racial minorities. A recent study found that exposure to traumatic online content on social media showing violence or hateful posts directed at racial minorities contributed to increases in psychological distress, PTSD symptoms, and depression among African American and Latinx adolescents in the United States ( Tynes, Willis, Stewart, & Hamilton, 2019 ). These concerns are contrasted by growing interest in the potential for new technologies including social media to expand the reach of services to underrepresented minority groups ( Schueller, Hunter, Figueroa, & Aguilera, 2019 ). Therefore, greater attention is needed to understanding the perspectives of ethnic and racial minorities to inform effective and safe use of social media for mental health promotion efforts.

Research has found that individuals living with mental illness have expressed interest in accessing mental health services through social media platforms. A survey of social media users with mental illness found that most respondents were interested in accessing programs for mental health on social media targeting symptom management, health promotion, and support for communicating with health care providers and interacting with the health system ( Naslund et al., 2017 ). Importantly, individuals with serious mental illness have also emphasized that any mental health intervention on social media would need to be moderated by someone with adequate training and credentials, would need to have ground rules and ways to promote safety and minimize risks, and importantly, would need to be free and easy to access.

An important strength with this commentary is that it combines a range of studies broadly covering the topic of social media and mental health. We have provided a summary of recent evidence in a rapidly advancing field with the goal of presenting unique ways that social media could offer benefits for individuals with mental illness, while also acknowledging the potentially serious risks and the need for further investigation. There are also several limitations with this commentary that warrant consideration. Importantly, as we aimed to address this broad objective, we did not conduct a systematic review of the literature. Therefore, the studies reported here are not exhaustive, and there may be additional relevant studies that were not included. Additionally, we only summarized published studies, and as a result, any reports from the private sector or websites from different organizations using social media or other apps containing social media-like features would have been omitted. Though it is difficult to rigorously summarize work from the private sector, sometimes referred to as “gray literature”, because many of these projects are unpublished and are likely selective in their reporting of findings given the target audience may be shareholders or consumers.

Another notable limitation is that we did not assess risk of bias in the studies summarized in this commentary. We found many studies that highlighted risks associated with social media use for individuals living with mental illness; however, few studies of programs or interventions reported negative findings, suggesting the possibility that negative findings may go unpublished. This concern highlights the need for a future more rigorous review of the literature with careful consideration of bias and an accompanying quality assessment. Most of the studies that we described were from the United States, as well as from other higher income settings such as Australia or the United Kingdom. Despite the global reach of social media platforms, there is a dearth of research on the impact of these platforms on the mental health of individuals in diverse settings, as well as the ways in which social media could support mental health services in lower income countries where there is virtually no access to mental health providers. Future research is necessary to explore the opportunities and risks for social media to support mental health promotion in low-income and middle-income countries, especially as these countries face a disproportionate share of the global burden of mental disorders, yet account for the majority of social media users worldwide ( Naslund et al., 2019 ).

Future Directions for Social Media and Mental Health

As we consider future research directions, the near ubiquitous social media use also yields new opportunities to study the onset and manifestation of mental health symptoms and illness severity earlier than traditional clinical assessments. There is an emerging field of research referred to as ‘digital phenotyping’ aimed at capturing how individuals interact with their digital devices, including social media platforms, in order to study patterns of illness and identify optimal time points for intervention ( Jain, Powers, Hawkins, & Brownstein, 2015 ; Onnela & Rauch, 2016 ). Given that most people access social media via mobile devices, digital phenotyping and social media are closely related ( Torous et al., 2019 ). To date, the emergence of machine learning, a powerful computational method involving statistical and mathematical algorithms ( Shatte, Hutchinson, & Teague, 2019 ), has made it possible to study large quantities of data captured from popular social media platforms such as Twitter or Instagram to illuminate various features of mental health ( Manikonda & De Choudhury, 2017 ; Reece et al., 2017 ). Specifically, conversations on Twitter have been analyzed to characterize the onset of depression ( De Choudhury, Gamon, Counts, & Horvitz, 2013 ) as well as detecting users’ mood and affective states ( De Choudhury, Gamon, & Counts, 2012 ), while photos posted to Instagram can yield insights for predicting depression ( Reece & Danforth, 2017 ). The intersection of social media and digital phenotyping will likely add new levels of context to social media use in the near future.

Several studies have also demonstrated that when compared to a control group, Twitter users with a self-disclosed diagnosis of schizophrenia show unique online communication patterns ( Michael L Birnbaum, Ernala, Rizvi, De Choudhury, & Kane, 2017 ), including more frequent discussion of tobacco use ( Hswen et al., 2017 ), symptoms of depression and anxiety ( Hswen, Naslund, Brownstein, & Hawkins, 2018b ), and suicide ( Hswen, Naslund, Brownstein, & Hawkins, 2018a ). Another study found that online disclosures about mental illness appeared beneficial as reflected by fewer posts about symptoms following self-disclosure (Ernala, Rizvi, Birnbaum, Kane, & De Choudhury, 2017). Each of these examples offers early insights into the potential to leverage widely available online data for better understanding the onset and course of mental illness. It is possible that social media data could be used to supplement additional digital data, such as continuous monitoring using smartphone apps or smart watches, to generate a more comprehensive ‘digital phenotype’ to predict relapse and identify high-risk health behaviors among individuals living with mental illness ( Torous et al., 2019 ).

With research increasingly showing the valuable insights that social media data can yield about mental health states, greater attention to the ethical concerns with using individual data in this way is necessary ( Chancellor, Birnbaum, Caine, Silenzio, & De Choudhury, 2019 ). For instance, data is typically captured from social media platforms without the consent or awareness of users ( Bidargaddi et al., 2017 ), which is especially crucial when the data relates to a socially stigmatizing health condition such as mental illness ( Guntuku, Yaden, Kern, Ungar, & Eichstaedt, 2017 ). Precautions are needed to ensure that data is not made identifiable in ways that were not originally intended by the user who posted the content, as this could place an individual at risk of harm or divulge sensitive health information ( Webb et al., 2017 ; Williams, Burnap, & Sloan, 2017 ). Promising approaches for minimizing these risks include supporting the participation of individuals with expertise in privacy, clinicians, as well as the target individuals with mental illness throughout the collection of data, development of predictive algorithms, and interpretation of findings ( Chancellor et al., 2019 ).

In recognizing that many individuals living with mental illness use social media to search for information about their mental health, it is possible that they may also want to ask their clinicians about what they find online to check if the information is reliable and trustworthy. Alternatively, many individuals may feel embarrassed or reluctant to talk to their clinicians about using social media to find mental health information out of concerns of being judged or dismissed. Therefore, mental health clinicians may be ideally positioned to talk with their patients about using social media, and offer recommendations to promote safe use of these sites, while also respecting their patients’ autonomy and personal motivations for using these popular platforms. Given the gap in clinical knowledge about the impact of social media on mental health, clinicians should be aware of the many potential risks so that they can inform their patients, while remaining open to the possibility that their patients may also experience benefits through use of these platforms. As awareness of these risks grows, it may be possible that new protections will be put in place by industry or through new policies that will make the social media environment safer. It is hard to estimate a number needed to treat or harm today given the nascent state of research, which means the patient and clinician need to weigh the choice on a personal level. Thus offering education and information is an important first step in that process. As patients increasingly show interest in accessing mental health information or services through social media, it will be necessary for health systems to recognize social media as a potential avenue for reaching or offering support to patients. This aligns with growing emphasis on the need for greater integration of digital psychiatry, including apps, smartphones, or wearable devices, into patient care and clinical services through institution-wide initiatives and training clinical providers ( Hilty, Chan, Torous, Luo, & Boland, 2019 ). Within a learning healthcare environment where research and care are tightly intertwined and feedback between both is rapid, the integration of digital technologies into services may create new opportunities for advancing use of social media for mental health.

As highlighted in this commentary, social media has become an important part of the lives of many individuals living with mental disorders. Many of these individuals use social media to share their lived experiences with mental illness, to seek support from others, and to search for information about treatment recommendations, accessing mental health services, and coping with symptoms ( Bucci et al., 2019 ; Highton-Williamson et al., 2015 ; Naslund, Aschbrenner, et al., 2016b ). As the field of digital mental health advances, the wide reach, ease of access, and popularity of social media platforms could be used to allow individuals in need of mental health services or facing challenges of mental illness to access evidence-based treatment and support. To achieve this end and to explore whether social media platforms can advance efforts to close the gap in available mental health services in the United States and globally, it will be essential for researchers to work closely with clinicians and with those affected by mental illness to ensure that possible benefits of using social media are carefully weighed against anticipated risks.

Acknowledgements

Dr. Naslund is supported by a grant from the National Institute of Mental Health (U19MH113211). Dr. Aschbrenner is supported by a grant from the National Institute of Mental Health (1R01MH110965-01).

Publisher's Disclaimer: This Author Accepted Manuscript is a PDF file of a an unedited peer-reviewed manuscript that has been accepted for publication but has not been copyedited or corrected. The official version of record that is published in the journal is kept up to date and so may therefore differ from this version.

Conflict of Interest

The authors have nothing to disclose.

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Project Proposal EFFECT OF SOCIAL MEDIA AND ADVERTISMNETS ON YOUTH

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