Research studies show that social media may be valuable tools in the disease surveillance toolkit used for improving public health professionals’ ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. A social media work group, consisting of surveillance practitioners, academic researchers, and other subject matter experts convened by the International Society for Disease Surveillance, conducted a systematic primary literature review using the PRISMA framework to identify research, published through February 2013, answering either of the following questions:
- Can social media be integrated into disease surveillance practice and outbreak management to support and improve public health?
- Can social media be used to effectively target populations, specifically vulnerable populations, to test an intervention and interact with a community to improve health outcomes?
Examples of social media included are Facebook, MySpace, microblogs (e.g., Twitter), blogs, and discussion forums. For Question 1, 33 manuscripts were identified, starting in 2009 with topics on Influenza-like Illnesses (n = 15), Infectious Diseases (n = 6), Non-infectious Diseases (n = 4), Medication and Vaccines (n = 3), and Other (n = 5). For Question 2, 32 manuscripts were identified, the first in 2000 with topics on Health Risk Behaviors (n = 10), Infectious Diseases (n = 3), Non-infectious Diseases (n = 9), and Other (n = 10).
The literature on the use of social media to support public health practice has identified many gaps and biases in current knowledge. Despite the potential for success identified in exploratory studies, there are limited studies on interventions and little use of social media in practice. However, information gleaned from the articles demonstrates the effectiveness of social media in supporting and improving public health and in identifying target populations for intervention. A primary recommendation resulting from the review is to identify opportunities that enable public health professionals to integrate social media analytics into disease surveillance and outbreak management practice.
Citation: Charles-Smith LE, Reynolds TL, Cameron MA, Conway M, Lau EHY, Olsen JM, et al. (2015) Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review. PLoS ONE10(10): e0139701. https://doi.org/10.1371/journal.pone.0139701
Editor: Lidia Adriana Braunstein, IFIMAR, UNMdP-CONICET, ARGENTINA
Received: May 18, 2015; Accepted: September 15, 2015; Published: October 5, 2015
This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: Project management support for this effort was provided by the International Society for Disease Surveillance. Participation of CDC and LECS was supported in part by Pacific Northwest National Laboratory's Laboratory Directed Research and Development Program.
Competing interests: The authors have declared that no competing interests exist.
Social media communication is an increasingly utilized outlet for people to freely create and post information that is disseminated and consumed worldwide through the Internet. News media, traditional scientific outlets, and social media create a platform for minority viewpoints and personal information, which is not being captured by other sources. Social media can create a sense of anonymity, allowing for unadulterated personal expression when compared to traditional face-to-face meetings, especially among young people and about intimate matters . In this respect, social media provide an additional informal source of data that can be used to identify health information not reported to medical officials or health departments and to reveal viewpoints on health-related topics, especially of a sensitive nature.
In the past 10 years, research articles connecting disease surveillance with Internet use have increased in number, most likely due to the increase in availability of health-related information from various Internet sites. For example, Wikipedia article hits , Google search terms (Google Flu Trends) , and online restaurant reservation availability (OpenTable)  were modeled against the number of patients with influenza-like illness (ILI) reported by the Centers for Disease Control and Prevention (CDC). Several literature reviews have looked at the potential of this type of research to benefit human health.
Moorhead et al. conducted a review of research studies to identify potential uses, benefits, and limitations of social media to engage the general public, patients, and health professionals in health communication . Although articles identified benefit from using social media in health communications, the authors note a lack of research focused on the evaluation of short- and long-term impacts on health communication practices. Bernardo et al. provided a scoping review of the use of search queries and social media in disease surveillance . First reported in 2006, the reviewed literature highlighted accuracy, speed, and cost performance that was comparable to existing disease surveillance systems and recommended the use of social media programs to support those systems.
Velasco et al. defined their literature review to contain only peer-reviewed articles on event-based disease surveillance  in which they identified and described 12 existing systems. Walters et al. described numerous systems implemented and dedicated to biosurveillance, defined as “the discipline in which diverse data streams such as these are characterized in real or near-real time to provide early warning and situational awareness of events affecting human, plant, and animal health,” many of which center around human disease outbreaks . The paper points out that including emerging media, such as blogs and Short Message Service (SMS), into these systems along with standardized metrics to evaluate the performance of different surveillance systems is crucial to the advancement of these early warning systems.
As members of the International Society for Disease Surveillance (ISDS), we established a social media working group (henceforth called the workgroup) to develop research, technology, and operational innovations in electronic public health surveillance. We proposed to evaluate the use of social media to enable public health professionals to realize positive, valuable, and timely community health outcomes at the local, state, regional, national, and global levels. To address these goals, we followed the PRISMA process  by systematically compiling and analyzing literature that demonstrates innovation in electronic public health surveillance through the use of social media.
By focusing on how research on social media data (further defined below) can be used for actionable disease surveillance, we are able to bring to light the best ways of using these tools to target vulnerable populations and improve public health in the broad spectrum from identifying and monitoring disease outbreaks to addressing traditionally intractable health concerns, such as adolescent drug and alcohol use.
This systematic review builds upon the preferred reporting items outlined in the PRISMA Statement in effort to properly assess the quality and quantity of health-related research using social media analytics for active surveillance, S1 Checklist. A social media application was defined for this review as, “an Internet-based application where people can communicate and share resources and information, and where users can activate and set their own profiles, have the ability to develop and update them constantly, and have the opportunity to make such profiles totally or partially public and linked with other profiles in a network.” Examples of social media included in this review are Facebook, MySpace, microblogs (e.g., Twitter), blogs, and discussion forums. Articles using data sources, such as Internet searches, ProMed-mail, and citizen-generated data were not included. In March 2013, a query of scientific literature databases (PubMed, Embase, Scopus, and Ichushi-Web) was conducted for all literature published through February 2013 to determine potential publications for review by the workgroup (Table 1).
Searches were further refined to include only human subjects and to exclude review (i.e., meta-analysis or other systematic reviews) and editorial articles. Articles published in Italian, German, Dutch, English, Spanish, and Japanese were included in the search check box because of multilingualism within the workgroup. In addition to these searches, other articles reviewed for potential inclusion were the ISDS research committee monthly literature review collection (http://www.syndromic.org/cop/research) and references from relevant articles, systematic reviews, and meta-analyses found through initial literature searches. The online bibliographic service Zotero (https://www.zotero.org/) was used for citation management.
The workgroup was formed from members of the ISDS with diverse background specialties, (e.g., public health physician, doctor of veterinary medicine, data scientist, public health professor, biomedical informatics) and countries of residence (e.g., USA, Australia, China, Japan). Within the group, a pair of members evaluated each collected abstract in detail for possible inclusion in the systematic review. Each member recorded the following information from each potential publication: author(s), date of publication, publication type (e.g., journal, conference proceedings, white or gray literature), data source type (e.g., social networking sites, microblogs, or open source databases). Requirements were that each study must be published as original researchand must analyze social media. The initial review was done for all documents containing an abstract, including peer-reviewed conference proceedings or white papers. An article was excluded if the full text was not available, if only methods were described (i.e., building an application programming interface, but no results), or if it did not directly address one of the two following research questions:
- Q1. Can social media be integrated into disease surveillance practice and outbreak management to support and improve public health?
- Q2. Can social media be used to effectively target populations, specifically vulnerable populations, to test an intervention and interact with a community to improve health outcomes?
Any differences of opinion about whether to include a paper were resolved through discussion until the workgroup achieved a consensus on inclusion or exclusion.
For each article fitting the review inclusion criteria, one workgroup member was assigned to extract and record specific details from the full-text article. This information included background (e.g., study objective, sample population and size, and the location, setting, time, and duration of the study), methods (e.g., study design, keywords, classification methods), outcomes measured (e.g., population, disease studied, intervention or exploratory (i.e., whether the study evaluates the impact of or observes the use of social media, respectively), hypothesis, outcomes related to either research question), results, and conclusions. To assess involvement of a public health jurisdiction in the study or intervention, a reviewer searched the acknowledgements for funding agency and methods for direct public health involvement. In addition, the reviewers included any information they believed might have introduced bias into the study. Note that to date, this study protocol has not been registered.
We identified 1,405 English language studies published through February 2013 in peer-reviewed journals, conference proceedings, and white/gray literature through Embase, PubMed, and Scopus database searches, as well as 8 articles through the Japanese database, Ichushi-Web (Fig 1). An additional 181 studies were identified from citation lists of relevant reviews or editorials and the workgroup’s private collections. After removing duplicates, 1,499 studies remained for the abstract screening step. We excluded 1,205 of these studies because they were reviews, letters, commentaries, or did not address either of the research questions. An additional 8 studies were excluded because the full-text publications were not available. These excluded study abstracts or presentations reported promising preliminary research addressing active disease surveillance. Topics ranged from targeting sexually transmitted diseases in traditionally hard-to-reach populations [10–13] to detecting unusual events, anomalies, and social disruption for early warning systems .
The abstract screening process resulted in 286 studies identified for detailed review of full-text articles. After this review, we further excluded studies that did not meet our definition of social media (e.g., Internet search, ProMED-mail) or discussed methods exclusively. We identified a total of 60 studies that met our eligibility criteria and addressed at least one of the two research questions. This process took over a year to complete because the authors donated their free time to review and analyze the literature.
Question 1 –Intervention into Surveillance Practice and Outbreak Management.
Of the 33 studies concerning disease surveillance and outbreak management, 48% (n = 16) were conducted in North America [15–30], 24% (n = 8) in Europe [31–37], 15% (n = 5) in Asia [38–42], 9% (n = 3) in an unspecified location [43–45], 3% (n = 1) in South America , and 3% (n = 1) on a global scale . Twitter was used as the primary data source in 81% (n = 27) of the studies although Facebook, various blogs, and health-related discussion forums were also investigated [15–17,19–23,26,27,29–40,43–47]. The studies examined data from January 2006  to January 2012  with the majority focused on 2009. The collection period spanned the 2009 H1N1 influenza pandemic, and 45% (n = 15) of the papers focused on influenza monitoring [15,16,19,27,31–33,38–40,43–45,47]. Comparison to CDC reports were most commonly used to evaluate the effectiveness of the various surveillance techniques presented. Most Twitter-based studies identified study populations through automatic means, i.e., Twitter keyword searches such as "influenza," "H1N1," and "swine flu” to target influenza-related tweets. The articles reported that study sizes were measured either by the number of tweets, ranging from 150 thousand  to 2 billion , or the number of unique social media users, ranging from 118 users  to 24.5 million . Most studies were published in English (2 in Japanese), and all were exploratory in nature.
Question 2 –Targeted Vulnerable Populations.
Thirty-two studies were identified as targeting a vulnerable population to improve health outcomes. These studies emphasized interaction with users rather than automatic algorithms and therefore typically contained focused populations and smaller datasets. The study sizes ranged from 19 post-partum women  to 155,508 Twitter users from 9 distinct areas . All of the studies included were published in English and 66% (n = 21) were conducted in North America [20,21,25,26,49–65], 12% (n = 4) in Australia [48,66–68], 9% (n = 3) in Asia [41,69,70], and 6% (n = 2) in Europe [1,71]. Most of the studies were classified as exploratory, although 24% (n = 8) of studies did include some type of intervention [1,55,56,58,60,64,69,71]. Populations studied were generally more focused than Question 1 studies, e.g., pregnant smokers in Australia. The study populations dated from January 2000  to February 2012 , although many do not disclose study periods. Interestingly, the studies addressing Question 2 first appeared in 2000, but published literature on Question 1 does not appear until 2010. Also, there is a spike in addressing both questions during 2011 (Fig 2).
Bias Across and Within Studies
The spectrum of studies selected for review were subject to publication bias because only primary literature was included and, therefore, other non-published information collected by state or federal health agencies was not incorporated. The choice of data search engines may have excluded valid studies that may not have been published in journals exposed through this process. In addition, there may be more recent articles published since our collection end date of March 2013.
Within the 60 studies reviewed, no important bias was identified by the authors and workgroup reviewers in 43% (n = 26) of the studies [1,19–21,23,24,29,31,34–36,38,39,41,43–45,49,53,57,61–63,66,70,72]; 56% (n = 34) displayed some degree of bias risk. The types of bias can be broken down into six different categories. Selection bias (n = 17) was the most prevalent as data was often collected out of convenience [25,51,52,60], at focused locations [15,46], or within specific social groups  and, therefore, was often not representative of the total population [15,26,28,30,46–48,51,54,58,59,65,71,73]. There were 14 articles displaying a faulty study design due to the choice of time period [16,17,27,33,37,40,42,47], data source [18,42,60], study scope [37,42], reporting of results [37,54], or lack of result measure [50,51]. Temporal relationship and directionality bias within 8 studies caused issues in the ability to extrapolate data [16,65,67] or infer directionality or causality [28,32,55,69]. A few studies had sample sizes that were too small to draw conclusions, i.e., sample size bias [17,30,55,64,67]. Other biases present in the reviewed articles were reliability bias [22,28,54,68] and selective interpretation bias [32,40].
Public Health Involvement
There was a small number of local (3%, n = 2) and state (15%, n = 9) governmental public health agencies involved in the studies reviewed for actionable health and disease surveillance (Table 2). These supportive agencies reside in England (London) and the United States of America (USA) (California, Louisiana, Michigan, and Washington State). Public health involvement was mainly in monetary support on a national level (n = 30) from Brazil, Canada, Germany, Japan, Netherlands, Switzerland, Taiwan, and USA. One research paper, funded by the University of Maryland, USA, described the implementation of social media communication by a local Taiwan government for disaster management, which showed promise over current national awareness and response protocols . Other universities showing interest in support of social media research were located in Australia, Germany, Italy, Japan, United Kingdom (UK), and USA. The private funding agencies that supported reviewed literature are found in the UK and USA. Only 3 papers contained a co-author who was affiliated with a public health agency, i.e., Public Health Agency of Canada , Governmental Institute of Public Health of Lower Saxony in Germany , and National Cancer Institute in USA .
For both research questions, this table records the number of articles in which private organizations, universities, and governments (local, state and national) contributed as funding agencies and/or organizations with direct involvement in each study or intervention reviewed. Note that some articles contained more than one funding agency.
Question 1: Can social media be integrated into disease surveillance practice and outbreak management to support and improve public health?
The key to our systematic literature review of Question 1 was to identify if, when, and how social media have been applied for disease surveillance and outbreak management to support and improve public health. Within the 33 manuscripts identified as addressing this question, we found an overwhelming number focused on influenza-like illnesses (45%, n = 15). For the remaining articles, we classified the instances into Infectious Diseases (n = 6), Non-infectious Diseases (n = 4), Medication and Vaccines (n = 3), and Other (n = 5) to understand the extent and focus of current research. All of these studies were exploratory research and did not contain any type of intervention analysis.
Influenza-like illness (ILI) was the first disease to be modeled using social media data in our review (Table 3). We identified 15 original, exploratory studies on ILI targeting social media users (e.g., Twitter and other blogs) from the USA, UK, and Japan between 2008 and 2012. From simple text searches, (e.g., flu or influenza [32,40,45]) to more specific influenza subtypes (e.g., H1N1, Swine Flu [16,44,47]) and symptomatic disease sets [15,17,19,31,32,38,39], all of the studies claimed to be able to use the social media data in real-time disease surveillance. A study by Sadilek, Kautz, and Silenzio (2012), applied their technique to identify the health of any person through geo-tagged Twitter microblogs in an effort to predict disease transmission . In general, correlation between social media data and national health statistics, e.g., from the CDC, ranged from 0.55  to 0.95  and was shown to predict outbreaks before the standard outbreak surveillance method favored by each country [19,31,32,38,39].
We identified 6 studies that used different social media programs to determine if the timeliness and sensitivity of detection for other infectious disease outbreaks (e.g., dengue fever, cholera, human immunodeficiency virus (HIV), and Escherichia coli) could be improved (Table 4). In a study by Chunara, Andrews, and Brownstein (2013), the volume of cholera-related Twitter posts and HealthMap news media reports were compared to official Haiti cholera case reports during the first 100 days of the 2010 outbreak . The changes in social media and news data trends were detected up to 2 weeks earlier than official case data, which they believe could have had direct implications on the disease outbreak and control measures taken . After analyzing 7 million tweets on medical conditions during the 2011 Enterohaemorrhagic E. coli (EHEC) outbreak in Germany, Diaz-Aviles et al. (2012) found over 450,000 posts related to the outbreak and determined that this information would have detected the outbreak 1 day earlier than other warning systems . Gomide et al. (2011) showed a correlation between Twitter posts in Brazil and dengue outbreaks (e.g., reported dengue cases correlated with the word “dengue” (0.78) and personal experience with dengue (0.96)) . However, they reported that only 40% of tweets included location, which limited spatial analysis . Although the breadth of studies is limited and most often retrospective, detection of outbreaks through social media tracking appears to provide a timeliness advantage in a variety of infectious disease outbreak settings.
The 4 studies identified as targeting non-infectious diseases were purely exploratory and focused on alcohol, tobacco, and sexual activity (Table 5). Facebook  and Twitter  were used to identify associations between alcohol references and misuse in college students or alcohol sales, respectively. It was shown that social media references to alcohol correlated with college students’ self-reported alcohol use, including alcohol-related injuries, and the U.S. Census Bureau’s alcohol sales volume. Therefore, social media data can enhance alcohol use surveillance and target specific audiences in need of health support. Another study, directed at college freshmen’s Facebook use, found a positive correlation between displaying sexual references online and reporting the intention to become sexually active, providing a new forum to target prevention or education messages to adolescents . Prier et al. (2011) examined different tools available to most effectively identify public health topics on Twitter . They found that the Latent Dirichlet Allocation (LDA) topic modeling method was successful in identifying broad topics, e.g., physical activity, obesity, substance abuse, and attitudes towards healthcare, whereas a smaller, more focused dataset created by query selection and theme analysis is necessary to detect lower-frequency topics such as tobacco use. Overall, the study showed that social media can be used to promote both positive and negative heath behaviors.
Medication and Vaccines.
Social media discussions can be used to determine attitudes, misinformation, and adverse events related to medications, vaccines, and other drug uses (Table 6). Salathé and Khandelwal (2011) identified an increase in Twitter data between August and November 2009 related to the launch of the 2009 influenza H1N1 vaccine . Tweets among opinionated users most often shared similar positive or negative sentiments towards vaccine use. As a result, simulation studies of disease transmission result in clusters of individuals with negative vaccine sentiments being unvaccinated and, therefore, at a higher risk of infection. This evidence may assist in targeting public health interventions of unvaccinated people at risk of disease. Another study reported that negative sentiment is more contagious than positive and, therefore, an increase in positive attitudes may predict an even greater increase in negative sentiment, which can be useful in modeling the diffusion of health behavior on social networks . Twitter feeds provide a forum for discussions regarding medications and, therefore, can be targeted to improve information dissemination. Bian et al. scanned Twitter feeds for 5 different drugs and found 239 drug users with 27 drug-related adverse event tweets . This study identifies support for pharmacovigilance through social media analysis, especially concerning new drug releases.
Many researchers have evaluated ways to best access and use health information on Twitter for disease surveillance (Table 7). A group in Germany retrospectively reviewed tweets that contained keywords of infectious disease symptoms and found 51% contained headlines that were linked to news websites regarding outbreaks and determined that a potential exists for using Twitter for real-time disease surveillance . Sofean and Smith (2012) designed and evaluated a real-time architecture for collecting and filtering disease-related postings on Twitter and found they could track health status in real time . Other researchers developed methods for pulling social media, including using a Badu search engine  and the Ailment Topic Aspect Model (ATAM) . ATAM introduces prior knowledge into the model from articles on diseases, reports model behavior in new settings, tracks illnesses over time and location, correlates risk factors with ailments, and then analyzes the correlations of symptoms and treatments. The ATAM is able to discover any coherent ailments, symptoms and treatment and does not have to be disease-specific . Using a variety of search engines and new tools, it is possible to detect and track a variety of health ailments using social media.
Question 2: Can social media be used to effectively target populations, specifically vulnerable populations, to test an intervention and interact with a community to improve health outcomes?
For question 2, we identified if, when, and how social media have been used to target populations and transform information gleaned from this data into action. The majority of studies within this group used social media to identify health risk behaviors (n = 10) and evaluate use of virtual communities to aid in risk reduction. For the remaining articles, we classified the instances into Infectious Diseases (n = 3), Non-infectious Diseases (n = 9), and Other (n = 10) to get a better overview where exploratory research (n = 25) and intervention efforts (n = 7) have been focused.
Health Risk Behaviors.
Social media, especially Facebook [25,49,68] and MySpace [56,57], have been used to target adolescents displaying health risk behaviors associated with substance abuse and sexual activities (Table 8). Specialized chat rooms, websites, and Twitter have been targeted for adult health risk behavior with tobacco use [26,48,60], substance abuse , and sexual activities . The specific populations, located in the USA and Australia, include college students [49,56,68], post-partum women , men who have sex with men (MSM) , and low-income youth . These studies show that social media can be effective at identifying adolescent populations displaying substance abuse, especially alcohol [25,49,68], in addition to sexual behavior , and that social media can improve community health outcomes in at-risk adolescents  and MSM . Interestingly, tobacco-related subjects posed an issue for researchers who tried to use topic modeling in Twitter  and found that the use of a virtual community bulletin board to reduce smoking behavior was ineffective . As proposed by Prier et al. (2011), the use of low-frequency topics, such as tobacco use, may require human intervention for selection of query terms and relevant subsequent analysis to properly address health concerns .
Two of the 3 social media studies focusing on infectious diseases (67%), investigated the use of social media to reach target populations for protection against sexually transmitted infections (STI) (Table 9). For example, Sullivan et al. (2011) identified factors behind the underrepresentation of black and Hispanic MSM in online research studies (ORS) despite this group experiencing the largest increase in HIV case reports . Targeted banner advertisements were posted in MySpace, displaying an ethnicity-matched model. This approach increased the odds of click-through of the ORS (adjusted odds ratios 1.7–1.8), but with limited effect on reducing dropouts. In the 2009 H1N1 pandemic, Szomszor, Kostkova, and de Quincey found that health communication via official Twitter feeds and trusted news organizations (e.g., BBC) was most effective in reaching the public; however, timeliness of health information may not directly translate to site popularity among these trusted sources . In addition, they found 40% of appreciable health-related information identified on the Internet containing poor scientific merit was directly linked to spam. Overall, the studies showed potential in reaching populations concerning socially stigmatized or sensitive health conditions, but time and effort are needed to build up a trusted channel for information dissemination.
Social media could potentially be used to target populations with illnesses of high prevalence and public health impact (e.g, depression, cancer, obesity, diabetes, and asthma) with an intervention to improve health outcomes. In a 16-week study of 32 women with breast cancer, an intervention using an electronic support group reported a significant decrease in depression symptoms and reaction to pain, and a trend towards increasing posttraumatic growth, zest for life, and deepening of spiritual lives . There were some dropouts in participation, which was attributed to different personalities’ response to the electronic support group. Similarly, researchers set up a chat room to provide an educational tool for adolescents with Type 1 diabetes and found that it significantly increased compliance and decreased HbA(1c) concentrations (from 8.9% to 7.8%) over a period of 3 months . Mobile support programs used to increase dietary self-monitoring and improve weight loss resulted in body weight changes; however, a similar study using Twitter did not find any differences . Therefore, the types of social media and the populations who will use and benefit from this type of information are key factors in how they impact health.
Multiple studies attempted to determine whether the potential exists for social media to reach vulnerable populations (Table 10). For mental health, a study of college freshmen showed that 46% of female and 21% of male students referenced stress, depression, or stress-related conditions, e.g., weight issues or drinking alcohol, on Facebook, and those who referred to stress were significantly more likely to mention weight concerns or depression . These researchers concluded that Facebook may provide a mode of distribution of targeted stress reduction information. Similarly, researchers in Australia found that 44% of students reported the need for mental health support; within this group, 50% of them already use the Internet and 47% said they would use online social networks for mental health problems . Social media could be used to identify those with non-infectious diseases and provide education and support to improve public health.
Social media was used to identify “other” target populations, e.g., low-income groups  and older people in need of physical activity , to assess vaccination sentiments  and misuse of antibiotics  (Table 11). Salathé et al. (2011) found a strong correlation (r = 0.78) between vaccination sentiments on Twitter and vaccination rates reported by the CDC across U.S. Department of Health and Human Service regions . Clusters of unprotected individuals with negative vaccination sentiments can be identified and targeted for tailored interventions. Scanfeld, Scanfeld, and Larson (2010) identified individuals from Twitter who may have misused antibiotics for treating viral infections who could be targeted for health-related education . Dissemination of valid health information among the identified groups may promote behavioral change towards a healthier lifestyle.
This systematic primary literature review on the use of social media to support public health practice has identified many evidence gaps and biases in the current knowledge on this topic. There are few studies to date on interventions and a lack of use of social media in practice despite the high potential for success identified in exploratory studies. This mirrors the lack of scientific reports published (n = 16) on performance assessment of disease surveillance methods found by Babaie et al. (2015), regardless of their necessity to public health response . Our findings may suggest that it is particularly challenging to translate research using social media for biosurveillance into practice. This challenge may be amplified by the lack of an ethical framework for the integration of social media into public health surveillance systems . In addition, the focus of many studies, especially on infectious diseases, is done retrospectively, potentially highlighting the ease in prediction post outbreak rather than implementation of social media prospectively. The under-representation of social media analytics in active surveillance may be due to a lack of resources or technical skills necessary for successful execution in the public health domain. Alternatively, public health departments may be using social media as a tool but not publishing their efforts. Due to the number of heterogeneous data sources used in analysis, a comparison and evaluation of techniques was not possible. However, this review demonstrates some evidence that the use of social media data could provide real-time surveillance of health issues, speed up outbreak management, and identify target populations necessary to support and improve public health and intervention outcomes.
Social media can impact the public health surveillance domain, bringing the wider media landscape to the public health community. This impact has been particularly important in the context of public health emergencies, such as after Haiti's post-earthquake cholera outbreak, where the utility of using social media as a data source in rapidly changing and dynamic situations was clearly shown . Pharmacovigilance is another key area where social media have demonstrated value. Traditional methods of reporting adverse drug events rely on gatekeepers (e.g., clinicians and pharmaceutical companies) to alert authorities of these events. Social media, in particular Twitter, have shown significant potential for creating real-time access to firsthand reports of adverse drug events, thereby bypassing the gatekeeper bottleneck [22,76].
Traditionally hard-to-reach groups, e.g., MSM and adolescents, may be more likely to engage with social media rather than with more conventional public health communication channels, creating a new avenue to address sensitive health issues. A significant proportion of the interventions reviewed (40%) concentrated on targeting populations with increased risk of STIs, a topic often avoided in public settings [11,12,56,58]. Mental health intervention studies suggested that young people would be willing to use social media to address mental health issues [51,65,67,70]. In this context, the type of mediations must fit the social media outlet targeted. For example, mental health interventions conducted via Twitter, with a 140 character limit, are likely to be very different from the kinds of interventions conducted through the more discursive communication possible with Internet discussion forums.
Different target groups, e.g., age groups, may prefer different social media outlets. Consequently, knowing the population and how they use social media can be a critical part of successful intervention and surveillance. For example, in the articles reviewed focusing on health risk behaviors, we found that adolescents were targeted using Facebook [25,49,68] and MySpace [10,56,57], while adults were targeted within Twitter and specialized chat rooms and websites [26,48,53,58,60]. However, to our knowledge, there is no directed health-related scientific research addressing which social media outlet should be targeted for specific populations. For health surveillance, the impact of the potential lack of population representativeness in the use of social media to detect and track disease outbreaks has not been adequately researched.
In addition, different topics may require different search techniques to identify targeted populations. In the study by Prier et al. (2011), they were successful at identifying broad topics in social media, e.g., physical activity and obesity, using the LDA method, yet for lower-frequency topics, such as tobacco use, human intervention was required for selection of query terms to create a smaller, more focused data set in which subsequent analysis was possible . Similarly, the type of social media platform used in analyses may change based on the target population and the years being studied. For example, the use of social media has evolved from blogs and discussion forums pre–2005 to social networking platforms as new technologies came to market. In this respect, the field of surveillance would benefit from a study classifying topics of concern and appropriate analysis techniques to achieve the greatest number of results or largest audience for intervention.
Since February 2013, the last search date reported in this review, there has been an increase in the number of articles published per year using a similar initial search query to this review. Estimating an 80% exclusion rate based on the results discussed above, around 90 new articles per year were published in 2013 and 2014. This number is higher than previous years, although many of the new literature focus is on previously described sources (e.g., google flu) and methods being applied to a different disease. Regardless, an upward trend in publications suggests an increase interest in understanding social media’s role in disease surveillance. This literature review demonstrates the effectiveness of social media in supporting and improving public health and identifying target populations for intervention. Coupled with the increased interest in social media analytics, opportunities to integrate this novel data source into disease surveillance and outbreak management should arise for public health professionals.
This work was supported by the International Society for Disease Surveillance. The authors thank Christine Noonan at Pacific Northwest National Laboratory (PNNL) for project support, as well as Amy Ising, University of North Carolina at Chapel Hill, William Storm, Ohio Department of Health, and Silvia Valkova, IMS Institute for Healthcare Informatics, for their contributions to the project. Participation of Courtney D. Corley and Lauren E. Charles-Smith was supported in part by the PNNL’s Laboratory Directed Research and Development Program. Pacific Northwest National Laboratory is operated for the U.S. Department of Energy by Battelle under Contract DE-AC05-76RL01830. The views expressed in this article are those of the authors and do not necessarily represent the views of the U.S. Department of Defense, U.S. Department of Veterans Affairs, or Health Services Research and Development Service.
Conceived and designed the experiments: LECS TLR MAC MC EHYL JAP MS LCS KJS CDC. Performed the experiments: LECS TLR MAC MC EHYL JMO JAP MS LCS KJS CDC. Analyzed the data: LECS TLR MAC MC EHYL JMO JAP MS LCS KJS CDC. Contributed reagents/materials/analysis tools: LECS TLR MAC MC EHYL JMO JAP MS LCS KJS CDC. Wrote the paper: LECS TLR MAC MC EHYL JMO JAP MS LCS KJS CDC.
- 1. Iafusco D, Ingenito N, Prisco F. The chatline as a communication and educational tool in adolescents with insulin-dependent diabetes: preliminary observations. Diabetes Care. 2000;23: 1853–1853.
- 2. McIver D, Brownstein J. Wikipedia Usage Estimates Prevalence of Influenza-Like Illness in the United States in Near Real-Time. PLoS Comput Biol. 2014;10: 1–8.
- 3. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009;457: 1012–1014. pmid:19020500
- 4. Nsoesie E, Buckeridge D, Brownstein J. Guess Who’s Not Coming to Dinner? Evaluating Online Restaurant Reservations for Disease Surveillance. J Med Internet Res. 2014;16.
- 5. Moorhead S, Hazlett D, Harrison L, Carroll J, Irwin A, Hoving C. A New Dimension of Health Care: Systematic Review of the Uses, Benefits, and Limitations of Social Media for Health Communication. J Med Internet Res. 2013;15.
- 6. Bernardo TM, Rajic A, Young I, Robiadek K, Pham MT, Funk JA. Scoping Review on Search Queries and Social Media for Disease Surveillance: A Chronology of Innovation. J Med Internet Res. 2013;15: e147. pmid:23896182
- 7. Acera E, Agheneza T, Denecke K, Kirchner G, Eckmanns T. Social Media and Internet-Based Data in Global Systems for Public Health Surveillance: A Systematic Review. Milbank Q. 2014;92: 7–33. pmid:24597553
- 8. Walters R, Harlan P, Nelson N, Hartley D. Data Sources for Biosurveillance. Wiley Handbook of Science and Technology for Homeland Security. John Wiley & Sons, Inc.; 2009. pp. 1–17.
- 9. Moher D, Liberati A, Tetzlaff J, Altman D. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med. 2009;6.
- 10. Moreno MA, Brockman L, Christakis DA. “Oops, I Did It Again”: A Content Analysis of Adolescents’ Displayed Sexual References on Myspace. J Adolesc Health. 2009;44: S22–S23.
- 11. Read R, Ewalds T, Singh A. Web 2.0 and [Nexopia.com]: Direct-To-Teen STI Interaction via a social networking site. 18th International Society for STD Research Conferences. London; 2009.
- 12. Schmidt M, Currie M, Bertram S, Bavinton T, Bowden F. Promoting Chlamydia Testing to Young Women, Their Partners and Their GPs using modern and social media. SEXUAL HEALTH. 2009. pp. 369–369.
- 13. Yamauchi E. MYMsta: Using Mobile Social Networking in HIV Prevention. Sex:: Tech Conference 2010. San Francisco; 2010.
- 14. Vaillant L, Barboza P, Arthur R. Epidemic Intelligence: Assessing event-based tools and users’ perception in the GHSAG community [Internet]. Presentation presented at: IMED; 2011; Vienne, France. Available: http://www.isid.org/events/archives/IMED2011/Downloads/IMED2011_Presentations/IMED2011_Vaillant.pdf.
- 15. Sadilek A, Kautz H, Silenzio V. Predicting disease transmission from geo-tagged micro-blog data. AI Access Foundation; 2012. pp. 136–142.
- 16. Achrekar H, Gandhe A, Lazarus R, Yu SH, Liu B. Twitter improves seasonal influenza prediction. SciTePress; 2012. pp. 61–70. Available: http://www.cs.uml.edu/~bliu/pub/healthinf_2012.pdf.
- 17. Culotta A. Towards detecting influenza epidemics by analyzing Twitter messages. Association for Computing Machinery; 2010. pp. 115–122. Available:
- 18. Corley CD, Cook DJ, Mikler AR, Singh KP. Text and Structural Data Mining of Influenza Mentions in Web and Social Media. Int J Environ Res Public Health. 2010;7: 596–615. pmid:20616993
- 19. Signorini A, Segre AM, Polgreen PM. The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic. PLoS ONE. 2011;6: e19467. pmid:21573238
- 20. Salathé M, Khandelwal S. Assessing Vaccination Sentiments with Online Social Media: Implications for Infectious Disease Dynamics and Control. PLoS Comput Biol. 2011;7.
- 21. Salathé M, Vu DQ, Khandelwal S, Hunter DR. The Dynamics of Health Behavior Sentiments on a Large Online Social Network. arXiv:12077274. 2012; Available: http://arxiv.org/abs/1207.7274.
- 22. Bian J, Topaloglu U, Yu F. Towards large-scale twitter mining for drug-related adverse events. Association for Computing Machinery; 2012. pp. 25–32. Available:
- 23. Chunara R, Andrews JR, Brownstein JS. Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreak. Am J Trop Med Hyg. 2012;86: 39–45. pmid:22232449
- 24. Stuart Chester TL, Taylor M, Sandhu J, Forsting S, Ellis A, Stirling R, et al. Use of a Web Forum and an Online Questionnaire in the Detection and Investigation of an Outbreak. Online J Public Health Inform. 2011;3.
- 25. Moreno MA, Christakis DA, Egan KG, Brockman LN, Becker T. Associations between displayed alcohol references on Facebook and problem drinking among college students. Arch Pediatr Adolesc Med. 2012;166: 157–163. pmid:21969360
- 26. Prier KW, Smith MS, Giraud-Carrier C, Hanson CL. Identifying health-related topics on twitter an exploration of tobacco-related tweets as a test topic. Springer Verlag; 2011. pp. 18–25. Available:
- 27. Culotta A. Lightweight methods to estimate influenza rates and alcohol sales volume from Twitter messages. Lang Resour Eval. 2013;47: 217–238.
- 28. Moreno MA, Brockman L, Wasserheit J, Christakis DA. A Pilot Evaluation of Older Adolescents’ Sexual Reference Displays on Facebook. J Sex Res. 2012;49: 390–399. pmid:22239559
- 29. Paul MJ, Dredze M. A model for mining public health topics from Twitter. Health (N Y). 2012;11: 16–6.
- 30. Paul MJ, Dredze M. You Are What You Tweet: Analyzing Twitter for Public Health. ICWSM. 2011.
- 31. Lampos V, Cristianini N. Tracking the flu pandemic by monitoring the social web. IEEE Computer Society; 2010. pp. 411–416. Available:
- 32. Szomszor M, Kostkova P, De Quincey E. #Swineflu: Twitter predicts swine flu outbreak in 2009. Springer Verlag; 2011. pp. 18–26. Available:
- 33. Doan S, Ohno-Machado L, Collier N. Enhancing Twitter Data Analysis with Simple Semantic Filtering: Example in Tracking Influenza-Like Illnesses. 2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology (HISB). 2012. pp. 62–71.
- 34. Diaz-Aviles E, Stewart A. Tracking Twitter for Epidemic Intelligence. Case study: EHEC/HUS outbreak in Germany, 2011. Association for Computing Machinery; 2012. pp. 82–85. Available:
- 35. Diaz-Aviles E, Stewart A, Velasco E, Denecke K, Nejdl W. Towards personalized learning to rank for epidemic intelligence based on social media streams. Association for Computing Machinery; 2012. pp. 495–496. Available:
- 36. Sofean M, Smith M. A real-time architecture for detection of diseases using social networks: Design, implementation and evaluation. Association for Computing Machinery; 2012. pp. 309–310. Available:
- 37. Krieck M, Dreesman J, Otrusina L, Denecke K. A new age of public health: Identifying disease outbreaks by analyzing tweets. Proceedings of Health Web-Science Workshop, ACM Web Science Conference. 2011.
- 38. Ishikawa T. Evaluation of microblog as influenza surveillance source. J Natl Inst Public Health. 2012;61.
- 39. Okamura N, Seki K, Uehara K. Using Microblog for Syndromic Surveillance. IPSJ SIG Tech Rep. 2011;
- 40. Aramaki E, Maskawa S, Morita M. Twitter catches the flu: Detecting influenza epidemics using Twitter. Association for Computational Linguistics (ACL); 2011. pp. 1568–1576.
- 41. Ku Y, Chiu C, Zhang Y, Fan L, Chen H. Global disease surveillance using social media: HIV/AIDS content intervention in web forums. IEEE Computer Society; 2010. p. 170. Available:
- 42. Yang M, Li YJ, Kiang M. Uncovering social media data for public health surveillance. Pacific Asia Conference on Information Systems; 2011.
- 43. Culotta A. Detecting influenza outbreaks by analyzing Twitter messages [Internet]. 2010 Jul. Report No.: 1007.4748. Available: http://arxiv.org/abs/1007.4748
- 44. Collier N, Son NT, Nguyen NM. OMG U got flu? Analysis of shared health messages for bio-surveillance. J Biomed Semant. 2011;2: S9.
- 45. De Quincey E, Kostkova P. Early warning and outbreak detection using social networking websites: The potential of Twitter. Springer Verlag; 2010. pp. 21–24. Available:
- 46. Gomide J, Veloso A, Meira W Jr, Almeida V, Benevenuto F, Ferraz F, et al. Dengue surveillance based on a computational model of spatio-temporal locality of twitter. ACM Web Science Conference (WebSci). 2011. pp. 1–8.
- 47. Chew C, Eysenbach G. Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak. PLoS ONE. 2010;5: e14118. pmid:21124761
- 48. Lowe JB, Barnes M, Teo C, Sutherns S. Investigating the use of social media to help women from going back to smoking post-partum. Aust N Z J Public Health. 2012;36: 30–32. pmid:22313703
- 49. Litt DM, Stock ML. Adolescent alcohol-related risk cognitions: the roles of social norms and social networking sites. Psychol Addict Behav J Soc Psychol Addict Behav. 2011;25: 708–713.
- 50. Baptist AP, Thompson M, Grossman KS, Mohammed L, Sy A, Sanders GM. Social media, text messaging, and email-preferences of asthma patients between 12 and 40 years old. J Asthma Off J Assoc Care Asthma. 2011;48: 824–830.
- 51. Egan KG, Moreno MA. Prevalence of stress references on college freshmen Facebook profiles. Comput Inform Nurs CIN. 2011;29: 586–592. pmid:21436681
- 52. Fisher J, Clayton M. Who Gives a Tweet: Assessing Patients’ Interest in the Use of Social Media for Health Care. Worldviews Evid Based Nurs. 2012;9: 100–108. pmid:22432730
- 53. Frost J, Okun S, Vaughan T, Heywood J, Wicks P. Patient-reported Outcomes as a Source of Evidence in Off-Label Prescribing: Analysis of Data From PatientsLikeMe. J Med Internet Res. 2011;13.
- 54. Idriss SZ, Kvedar JC, Watson AJ. The role of online support communities: benefits of expanded social networks to patients with psoriasis. Arch Dermatol. 2009;145: 46–51. pmid:19153342
- 55. Lieberman MA, Golant M, Giese-Davis J, Winzlenberg A, Benjamin H, Humphreys K, et al. Electronic support groups for breast carcinoma. Cancer. 2003;97: 920–925. pmid:12569591
- 56. Moreno MA, Vanderstoep A, Parks MR, Zimmerman FJ, Kurth A, Christakis DA. Reducing at-risk adolescents’ display of risk behavior on a social networking web site: a randomized controlled pilot intervention trial. Arch Pediatr Adolesc Med. 2009;163: 35–41. pmid:19124701
- 57. Ralph LJ, Berglas NF, Schwartz SL, Brindis CD. Finding Teens in TheirSpace: Using Social Networking Sites to Connect Youth to Sexual Health Services. Sex Res Soc Policy. 2011;8: 38–49.
- 58. Rhodes SD, Hergenrather KC, Duncan J, Vissman AT, Miller C, Wilkin AM, et al. A Pilot Intervention Utilizing Internet Chat Rooms to Prevent HIV Risk Behaviors Among Men Who Have Sex with Men. Public Health Rep. 2010;125: 29–37. pmid:20408385
- 59. Song H, Nam Y, Gould J, Sanders WS, McLaughlin M, Fulk J, et al. Cancer survivor identity shared in a social media intervention. J Pediatr Oncol Nurs Off J Assoc Pediatr Oncol Nurses. 2012;29: 80–91.
- 60. Stoddard JL, Augustson EM, Moser RP. Effect of Adding a Virtual Community (Bulletin Board) to Smokefree.gov: Randomized Controlled Trial. J Med Internet Res. 2008;10.
- 61. Stroever SJ, Mackert MS, McAlister AL, Hoelscher DM. Using social media to communicate child health information to low-income parents. Prev Chronic Dis. 2011;8: A148. pmid:22005641
- 62. Sullivan PS, Khosropour CM, Luisi N, Amsden M, Coggia T, Wingood GM, et al. Bias in Online Recruitment and Retention of Racial and Ethnic Minority Men Who Have Sex With Men. J Med Internet Res. 2011;13: e38. pmid:21571632
- 63. Tsaousides T, Matsuzawa Y, Lebowitz M. Familiarity and prevalence of Facebook use for social networking among individuals with traumatic brain injury. Brain Inj BI. 2011;25: 1155–1162. pmid:21961574
- 64. Turner-McGrievy G, Tate D. Tweets, Apps, and Pods: Results of the 6-Month Mobile Pounds Off Digitally (Mobile POD) Randomized Weight-Loss Intervention Among Adults. J Med Internet Res. 2011;13.
- 65. Wicks P, Massagli M, Frost J, Brownstein C, Okun S, Vaughan T, et al. Sharing Health Data for Better Outcomes on PatientsLikeMe. J Med Internet Res. 2010;12.
- 66. Dumbrell D, Steele R. What are the characteristics of highly disseminated public health-related tweets? Association for Computing Machinery; 2012. pp. 115–118. Available:
- 67. O’Dea B, Campbell A. Healthy connections: online social networks and their potential for peer support. Stud Health Technol Inform. 2011;168: 133–140. pmid:21893921
- 68. Ridout B, Campbell A, Ellis L. “Off your Face(book)”: alcohol in online social identity construction and its relation to problem drinking in university students. Drug Alcohol Rev. 2012;31: 20–26. pmid:21355935
- 69. Huang C- M, Chan E, Hyder AA. Web 2.0 and Internet Social Networking: A New tool for Disaster Management? Lessons from Taiwan. BMC Med Inform Decis Mak. 2010;10: 57. pmid:20925944
- 70. Takahashi Y, Uchida C, Miyaki K, Sakai M, Shimbo T, Nakayama T. Potential Benefits and Harms of a Peer Support Social Network Service on the Internet for People With Depressive Tendencies: Qualitative Content Analysis and Social Network Analysis. J Med Internet Res. 2009;11: e29. pmid:19632979
- 71. Peels DA, van Stralen MM, Bolman C, Golsteijn RHJ, de Vries H, Mudde AN, et al. Development of web-based computer-tailored advice to promote physical activity among people older than 50 years. J Med Internet Res. 2012;14: e39. pmid:22390878
- 72. Szomszor M, Kostkova P, St Louis C. Twitter informatics: Tracking and understanding public reaction during the 2009 Swine Flu pandemic. IEEE Computer Society; 2011. pp. 320–323. Available:
- 73. Scanfeld D, Scanfeld V, Larson EL. Dissemination of health information through social networks: Twitter and antibiotics. Am J Infect Control. 2010;38: 182–188. pmid:20347636
- 74. Babaie J, Ardalan A, Vatandoost H, Goya MM, Akbarisari A. Performance Assessment of Communicable Disease Surveillance in Disasters: A Systematic Review. PLOS Currents Disasters. 2015;Edition 1.
- 75. Vayena E, Salathé M, Madoff L, Brownstein J. Ethical Challenges of Big Data in Public Health. PLoS Comput Biol. 11: e1003904. pmid:25664461
- 76. Sarker A, Ginn R, Nikfarjam A, O’Connor K, Smith K, Jayaraman S, et al. Utilizing Social Media Data for Pharmacovigilance: A Review. J Biomed Inform. 2015;54: 202–212. pmid:25720841