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Although at present there is broad agreement among researchers, health professionals, and policy makers on the need to control and combat health misinformation, the magnitude of this problem is still unknown. Consequently, it is fundamental to discover both the most prevalent health topics and the social media platforms from which these topics are initially framed and subsequently disseminated.
This systematic review aimed to identify the main health misinformation topics and their prevalence on different social media platforms, focusing on methodological quality and the diverse solutions that are being implemented to address this public health concern.
We searched PubMed, MEDLINE, Scopus, and Web of Science for articles published in English before March 2019, with a focus on the study of health misinformation in social media. We defined health misinformation as a health-related claim that is based on anecdotal evidence, false, or misleading owing to the lack of existing scientific knowledge. We included (1) articles that focused on health misinformation in social media, including those in which the authors discussed the consequences or purposes of health misinformation and (2) studies that described empirical findings regarding the measurement of health misinformation on these platforms.
A total of 69 studies were identified as eligible, and they covered a wide range of health topics and social media platforms. The topics were articulated around the following six principal categories: vaccines (32%), drugs or smoking (22%), noncommunicable diseases (19%), pandemics (10%), eating disorders (9%), and medical treatments (7%). Studies were mainly based on the following five methodological approaches: social network analysis (28%), evaluating content (26%), evaluating quality (24%), content/text analysis (16%), and sentiment analysis (6%). Health misinformation was most prevalent in studies related to smoking products and drugs such as opioids and marijuana. Posts with misinformation reached 87% in some studies. Health misinformation about vaccines was also very common (43%), with the human papilloma virus vaccine being the most affected. Health misinformation related to diets or pro–eating disorder arguments were moderate in comparison to the aforementioned topics (36%). Studies focused on diseases (ie, noncommunicable diseases and pandemics) also reported moderate misinformation rates (40%), especially in the case of cancer. Finally, the lowest levels of health misinformation were related to medical treatments (30%).
The prevalence of health misinformation was the highest on Twitter and on issues related to smoking products and drugs. However, misinformation on major public health issues, such as vaccines and diseases, was also high. Our study offers a comprehensive characterization of the dominant health misinformation topics and a comprehensive description of their prevalence on different social media platforms, which can guide future studies and help in the development of evidence-based digital policy action plans.
Over the last two decades, internet users have been increasingly using social media to seek and share health information [
Although the term “health misinformation” is increasingly present in our societies, its definition is becoming increasingly elusive owing to the inherent dynamism of the social media ecosystem and the broad range of health topics [
The fundamental role of health misinformation on social media has been recently highlighted by the COVID-19 pandemic, as well as the need for quality and veracity of health messages in order to manage the present public health crisis and the subsequent infodemic. In fact, at present, the propagation of health misinformation through social media has become a major public health concern [
More specifically, four knowledge gaps have been detected from the field of public health [
In line with the abovementioned gaps, a recent report represents one of the first steps forward in the comparative study of health misinformation on social media [
Taking into account this wide set of considerations, this systematic review aimed to specifically address the knowledge gap. In order to guide future studies in this field of knowledge, our objective was to identify and compare the prevalence of health misinformation topics on social media platforms, with specific attention paid to the methodological quality of the studies and the diverse analytical techniques that are being implemented to address this public health concern.
This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [
Studies were included if (1) the objectives were to address the study of health misinformation on social media, search systematically for health misinformation, and explicitly discuss the impact, consequences, or purposes of misinformation; (2) the results were based on empirical results and the study used quantitative, qualitative, and computational methods; and (3) the research was specifically focused on social media platforms (eg, Twitter, Facebook, Instagram, Flickr, Sina Weibo, VK, YouTube, Reddit, Myspace, Pinterest, and WhatsApp). For comparability, we included studies written in English that were published after 2000 until March 2019.
Articles were excluded if they addressed health information quality in general or if they partially mentioned the existence of health misinformation without providing empirical findings. We did not include studies that dealt with content posted on other social media platforms. During the screening process, papers with a lack of methodological quality were also excluded.
We searched MEDLINE and PREMEDLINE in March 2019 using the PubMed search engine. Based on previous findings [
In total, we collected 5018 research articles. After removing duplicates, we screened 3563 articles and retrieved 226 potentially eligible articles. In the next stage, we independently carried out a full-text selection process for inclusion (k=0.89). Discrepancies were shared and resolved by mutual agreement. Finally, a total of 69 articles were included in this systematic review (
Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow chart.
In the first phase, the data were extracted by VSL and then checked by VSL and JAG. In order to evaluate the quality of the selected studies and given the wide variety of methodologies and approaches found in the articles, we composed an extraction form based on previous work [
Furthermore, in order to be able to compare the methods used in the selected studies, the studies were classified into several categories. The studies classified as “content/text analysis” used methods related to textual and content analysis, emphasizing the word/topic frequency, linguistic inquiry word count, n-grams, etc. The second category “evaluating content” grouped together studies whose methods were focused on the evaluation of content and information. In general, these studies analyzed different dimensions of the information published on social media. The third category “evaluating quality” included studies that analyzed the quality of the information offered in a global way. This category considered other dimensions in addition to content, such as readability, accuracy, usefulness, and sources of information. The fourth category “sentiment analysis” included studies whose methods were focused on sentiment analysis techniques (ie, methods measuring the reactions and the general tone of the conversation on social media). Finally, the “social network analysis” category included those studies whose methods were based on social network analysis techniques. These studies focused on measuring how misinformation spreads on social media, the relationship between the quality of information and its popularity on these social platforms, the relationship between users and opinions, echochambers effects, and opinion formation.
Of the 226 studies available for full-text review, 157 were excluded for various reasons, including research topics that were not focused on health misinformation (n=133). We also excluded articles whose research was based on websites rather than social media platforms (n=16), studies that did not assess the quality of health information (n=6) or evaluated institutional communication (n=5), nonempirical studies (n=2), and research protocols (n=1). In addition, two papers were excluded because of a lack of quality requirements (Q score <50%). Finally, the protocol of this review was registered at the International Prospective Register of Systematic Reviews (PROSPERO CRD42019136694).
Ultimately, 69 studies were identified as eligible, and they covered a wide range of health topics and social media platforms, with the most common data source being Twitter (29/69, 43%), followed by YouTube (25/69, 37%) and Facebook (6/69, 9%). The less common sources were Instagram, MySpace, Pinterest, Tumblr, WhatsApp, and VK or a combination of these. Overall, 90% (61/69) of the studies were published in health science journals, and only 7% (5/69) of the studies were published in communication journals. The vast majority of articles analyzed posts written exclusively in one language (63/69, 91%). Only a small percentage assessed posts in more than one language (6/69, 10%).
Summary of the prevalence of misinformation by topic and social media platform.
Authors | Year | Topic | Social media platform | Prevalence of health misinformation posts |
Abukaraky et al [ |
2018 | Treatments | YouTube | 30% |
Ahmed et al [ |
2019 | Pandemics | N/Aa | |
Al Khaja et al [ |
2018 | Drugs | 27% | |
Allem et al [ |
2017 | Drugs | 59% | |
Allem et al [ |
2017 | Drugs | N/A | |
Arseniev-Koehler et al [ |
2016 | EDsb | 36% | |
Basch et al [ |
2017 | Vaccines | YouTube | 65% |
Becker et al [ |
2016 | Vaccines | 1% | |
Biggs et al [ |
2013 | NCDsc | YouTube | 39% |
Blankenship et al [ |
2018 | Vaccines | 24% | |
Bora et al [ |
2018 | Pandemics | YouTube | 23% |
Branley et al [ |
2017 | EDs | Twitter and Tumblr | 25% |
Briones et al [ |
2012 | Vaccines | YouTube | 51% |
Broniatowski et al [ |
2018 | Vaccines | 35% | |
Buchanan et al [ |
2014 | Vaccines | 43% | |
Butler et al [ |
2013 | Treatments | YouTube | N/A |
Cavazos-Rehg et al [ |
2018 | Drugs | 75% | |
Chary et al [ |
2017 | Drugs | 0% | |
Chew et al [ |
2010 | Pandemics | 4% | |
Covolo et al [ |
2017 | Vaccines | YouTube | 23% |
Dunn et al [ |
2015 | Vaccines | 25% | |
Dunn et al [ |
2017 | Vaccines | N/A | |
Ekram et al [ |
2018 | Vaccines | YouTube | 57% |
Erdem et al [ |
2018 | Treatments | YouTube | 0% |
Faasse et al [ |
2016 | Vaccines | N/A | |
Fullwood et al [ |
2016 | Drugs | YouTube | 34% |
Garg et al [ |
2015 | Vaccines | YouTube | 11% |
Gimenez-Perez et al [ |
2018 | NCDs | YouTube | 50% |
Goobie et al [ |
2019 | NCDs | YouTube | N/A |
Guidry et al [ |
2017 | Pandemics | Twitter and Instagram | N/A |
Guidry et al [ |
2016 | Drugs | 97% | |
Guidry et al [ |
2015 | Vaccines | 74% | |
Hanson et al [ |
2013 | Drugs | 0% | |
Harris et al [ |
2018 | EDs | N/A | |
Haymes et al [ |
2016 | NCDs | YouTube | 47% |
Helmi et al [ |
2018 | NCDs | Different sources | N/A |
Kang et al [ |
2017 | Vaccines | 42% | |
Katsuki et al [ |
2015 | Drugs | 6% | |
Keelan et al [ |
2010 | Vaccines | MySpace | 43% |
Keim-Malpass et al [ |
2017 | Vaccines | 43% | |
Kim et al [ |
2017 | NCDs | YouTube | 22% |
Krauss et al [ |
2017 | Drugs | 50% | |
Krauss et al [ |
2015 | Drugs | 87% | |
Kumar et al [ |
2014 | NCDs | YouTube | 33% |
Laestadius et al [ |
2016 | Drugs | N/A | |
Leong et al [ |
2018 | NCDs | YouTube | 33% |
Lewis et al [ |
2015 | Treatments | YouTube | N/A |
Loeb et al [ |
2018 | NCDs | YouTube | 77% |
Love et al [ |
2013 | Vaccines | 13% | |
Martinez et al [ |
2018 | Drugs | 67% | |
Massey et al [ |
2016 | Vaccines | 25% | |
McNeil et al [ |
2012 | NCDs | 41% | |
Menon et al [ |
2017 | Treatments | YouTube | 2% |
Merianos et al [ |
2016 | Drugs | YouTube | 65% |
Meylakhs et al [ |
2014 | NCDs | VK | N/A |
Morin et al [ |
2018 | Pandemics | N/A | |
Mueller et al [ |
2019 | NCDs | YouTube | 66% |
Porat et al [ |
2019 | Pandemics | 0% | |
Radzikowski et al [ |
2016 | Vaccines | N/A | |
Schmidt et al [ |
2018 | Vaccines | 4% | |
Seltzer et al [ |
2017 | Pandemics | 60% | |
Seymour et al [ |
2015 | NCDs | N/A | |
Syed-Abdul et al [ |
2013 | EDs | YouTube | 29% |
Teufel et al [ |
2013 | EDs | 22% | |
Tiggermann et al [ |
2018 | EDs | 29% | |
Tuells et al [ |
2015 | Vaccines | YouTube | 12% |
van der Tempel et al [ |
2016 | Drugs | N/A | |
Waszak et al [ |
2018 | NCDs | 40% | |
Yang et al [ |
2018 | Drugs | YouTube | 98% |
aN/A: not applicable.
bEDs: eating disorders.
cNCDs: noncommunicable diseases.
Overall, health misinformation was most prevalent in studies related to smoking products, such as hookah and water pipes [
Prevalence of health misinformation grouped by different topics and social media type.
Regarding the methods used in the different studies, there were some differences between the diverse social media platforms. We classified the studies based on the methods applied into the following five categories: social network analysis (19/69), evaluating content (18/69), evaluating quality (16/69), content/text analysis (12/69), and sentiment analysis (4/69).
Prevalence of health misinformation grouped by methods and social media type.
Overall, 32% (22/69) of the studies focused on vaccines or vaccination decision-making–related topics. Additionally, 14% (10/69) of the selected articles focused on social media discussion regarding the potential side effects of vaccination [
Most authors studied differences in language use, the effect of a heterogeneous community structure in the propagation of health misinformation, and the role played by fake profiles or bots in the spread of poor quality, doubtful, or ambiguous health content. In line with these concerns, authors pointed out the need to further study the circumstances surrounding those who adopt these arguments [
Several studies (16/69, 22%) covered misuse and misinformation about e-cigarettes, marijuana, opioid consumption, and prescription drug abuse. Studies covering the promotion of e-cigarette use and other forms of smoking, such as hookah (ie, water pipes or narghiles) represented 7% (5/69) of the articles analyzed. The rest (16%, 11/69) were focused on the analysis of drug misinformation.
According to topic, regarding drug and opioid use, studies investigated the dissemination of misinformation through social media platforms [
Unlike drug studies, most of the papers analyzed how e-cigarettes and hookah [
We found that 10% (7/69) of the studies used methods focused on evaluating the content of the posts. These studies aimed to explore the misperceptions of drug abuse or alternative forms of tobacco consumption. Along these lines, another study (1/69, 1%) focused on evaluating the quality of content. The authors evaluated the truthfulness of claims about drugs. In particular, we found that 7% (5/69) of the studies used social network analysis techniques. These studies analyzed the popularity of messages based on whether they promoted illegal access to drugs online and the interaction of users with this content. Other studies (3/69, 3%) used content analysis techniques. These studies evaluated the prevalence of misinformation on platforms and geographically, as a kind of “toxicosurveillance” system [
A relevant proportion (13/69, 19%) of studies assessed noncommunicable diseases, such as cancer, diabetes, and epilepsy. Most of the studies focused on the objective evaluation of information quality on YouTube [
Some studies evaluated the potential of this platform as a source of information specially for health students or self-directed education among the general public. Unfortunately, the general tone of research findings was that YouTube is not an advisable source for health professionals or health information seekers. Regarding diabetes, the probability of finding misleading videos was high [
Results indicated that 10% (7/69) of the studies covered misinformation related to pandemics and communicable diseases such as H1N1 [
We found that 14% (10/69) of the studies on this topic evaluated the quality of the information. To achieve this, most of the studies used external instruments such as DISCERN and AAD7 Self-Care Behaviors. Overall, 9% (6/69) of the papers evaluated the content of the information. These studies were focused on analysis of the issues of misinformation. Another 4% (3/69) used social media analysis to observe the propagation of misinformation. Finally, 3% (2/69) used textual analysis as the main method. These studies focused on the study of the prevalence of health misinformation.
These studies identified social media as a public forum for free discussion and indicated that this freedom might lead to rumors on anecdotal evidence and misunderstandings regarding pandemics. Consequently, although social media was described as a forum for sharing health-related knowledge, these tools are also recognized by researchers and health professionals as a source of misinformation that needs to be controlled by health experts [
Studies focusing on diet and eating disorders represented 9% (6/69) of the included studies. This set of studies identified pro–eating disorder groups and discourses within social media [
Overall, 4% (3/69) of the studies used social network analysis techniques. The authors focused on analyzing the existing connections between individuals in the pro–eating disorder community and their engagement, or comparing the cohesion of these communities with other communities, such as the fitness community, that promote healthier habits. Moreover, 3% (2/69) of the studies evaluated the quality of the content and particularly focused on informative analysis of the videos, that is, the content was classified as informative when it described the health consequences of anorexia or proana if, on the contrary, anorexia was presented as a fashion or a source of beauty. Furthermore, only one study used content analysis techniques. The authors classified the posts according to the following categories: proana, antiana, and prorecovery. Pro–eating disorder pages tended to identify themselves with body-associated pictures owing to the importance they attributed to motivational aspects of pro–eating disorder communities [
Regarding eating disorders on social media, paying attention to community structure is important according to authors. Although it is widely acknowledged that communities can be positive by providing social support, such as recovery and well-being, certain groups on social media may also reaffirm the pro–eating disorder identity [
Finally, we found that 7% (5/69) of the studies assessed the quality of health information regarding different medical treatments or therapies recommended through social media [
As in the case of noncommunicable diseases, professionals scanned social networks, especially YouTube, and evaluated the quality of online health content as an adequate instrument for self-care or for health student training. There were specific cases where information was particularly limited in quality and quantity, such as dental implants and first-aid information on burns [
A full description of the objectives and main conclusions of the reviewed articles is presented in
This work represents, to our knowledge, the first effort aimed at finding objective and comparable measures to quantify the extent of health misinformation in the social media ecosystem. Our study offers an initial characterization of dominant health misinformation topics and specifically assesses their prevalence on different social platforms. Therefore, our systematic review provides new insights on the following unanswered question that has been recurrently highlighted in studies of health misinformation on social media: How prevalent is health misinformation for different topics on different social platform types (ie, microblogging, media sharing, and social networks)?
We found that health misinformation on social media is generally linked to the following six topical domains: (1) vaccines, (2) diets and eating disorders, (3) drugs and new tobacco products, (4) pandemics and communicable diseases, (5) noncommunicable diseases, and (6) medical treatments and health interventions.
With regard to vaccines, we found some interesting results throughout the different studies. Although antivaccine ideas have been traditionally linked to emotional discourse against the rationality of the scientific and expert community, we curiously observed that in certain online discussions, antivaccine groups tend to incorporate scientific language in their own discourse with logically structured statements and/or with less usage of emotional expressions [
As observed in our review, health topics were omnipresent over all social media platforms included in our study; however, the health misinformation prevalence for each topic varied depending on platform characteristics. Therefore, the potential effect on population health was ambivalent, that is, we found both positive and negative effects depending on the topic and on the group of health information seekers. For instance, content related to eating disorders was frequently hidden or not so evident to the general public, since pro–eating disorder communities use their own codes to reach specific audiences (eg, younger groups) [
Throughout our review, we found different types of misinformation claims depending on the topic. Concerning vaccines, misinformation was often framed with a scientific appearance against scientific evidence [
In this study, we focused on analysis of the results obtained and the conclusions of the authors. Some of our findings are in line with those obtained in recent works [
Finally, we should mention that the current results are limited to the availability and quality of social media data. Although the digitalization of social life offers researchers an unprecedented amount of health and social information that can be used to understand human behaviors and health outcomes, accessing this online data is becoming increasingly difficult, and some measures have to be taken to mitigate bias [
The present study has some limitations. First, the conceptual definition of health misinformation is one limitation. In any case, taking into account that we were facing a new field of study, we considered a broad definition in order to be more inclusive and operative in the selection of studies. Therefore, we included as many papers as possible for the review in order to perform an analysis of the largest number of possible topics. Second, from a methodological perspective, our findings are limited to research published in English language journals and do not cover all the social media platforms that exist. Besides, we discovered some technical limitations when conducting this systematic review. Owing to the newness of this research topic, our study revealed difficulties in comparing different research studies characterized by specific theoretical approaches, working definitions, methodologies, data collection processes, and analytical techniques. Some studies selected involved observational designs (using survey methods and textual analysis), whereas others were based on the application of automatic or semiautomatic computational procedures with the aim of classifying and analyzing health misinformation on social media. Finally, taking into account the particular features of each type of social media (ie, microblogging service, video sharing service, or social network) and the progressive barriers in accessing social media data, we need to consider the information and selection bias when studying health misinformation on these platforms. According to these biases, we should ponder which users are behind these tools and how we can extrapolate specific findings (ie, applied to certain groups and social media platforms) to a broader social context.
Despite the limitations described above, it is necessary to mention the strengths of our work. First, we believe that this study represents one of the first steps in advancing research involving health misinformation on social media. Unlike previous work, we offer some measures that can serve as guidance and a comparative baseline for subsequent studies. In addition, our study highlights the need to redirect future research toward social media platforms, which, perhaps due to the difficulties of automatic data collection, are currently being neglected by researchers. Our study also highlights the need for both researchers and health professionals to explore the possibility of using these digital tools for health promotion and the need for them to progressively colonize the social media ecosystem with the ultimate goal of combating the waves of health misinformation that recurrently flood our societies.
Health misinformation was most common on Twitter and on issues related to smoking products and drugs. Although we should be aware of the difficulties inherent in the dynamic magnitude of online opinion flows, our systematic review offers a comprehensive comparative framework that identifies subsequent action areas in the study of health misinformation on social media. Despite the abovementioned limitations, our research presents some advances when compared with previous studies. Our study provides (1) an overview of the prevalence of health misinformation identified on different social media platforms; (2) a methodological characterization of studies focused on health misinformation; and (3) a comprehensive description of the current research lines and knowledge gaps in this research field.
According to the studies reviewed, the greatest challenge lies in the difficulty of characterizing and evaluating the quality of the information on social media. Knowing the prevalence of health misinformation and the methods used for its study, as well as the present knowledge gaps in this field will help us to guide future studies and, specifically, to develop evidence-based digital policy action plans aimed at combating this public health problem through different social media platforms.
Search terms and results from the search query.
Data extraction sheet.
Summary of quality scores.
Summary table with objectives and conclusions about misinformation prevalence in social media.
application programming interface
human papilloma virus
We would like to acknowledge the support of the University Research Institute on Social Sciences (INDESS, University of Cadiz) and the Ramon & Cajal Program. JAG was subsidized by the Ramon & Cajal Program operated by the Ministry of Economy and Business (RYC-2016-19353) and the European Social Fund.
None declared.