Social Media Postings Have Been Used to Predict a Flu Outbreak

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Peer-reviewed

Research Article

Using Social Media for Actionable Illness Surveillance and Outbreak Management: A Systematic Literature Review

• Lauren Eastward. Charles-Smith,
• Tera Fifty. Reynolds,
• Marking A. Cameron,
• Mike Conway,
• Eric H. Y. Lau,
• Jennifer One thousand. Olsen,
• Julie A. Pavlin,
• Mika Shigematsu,
• Laura C. Streichert,
• Katie J. Suda

x

• Published: October 5, 2015
• https://doi.org/10.1371/periodical.pone.0139701

Figures

Abstract

Objective

Enquiry studies bear witness that social media may be valuable tools in the disease surveillance toolkit used for improving public wellness professionals' ability to detect disease outbreaks faster than traditional methods and to raise outbreak response. A social media work group, consisting of surveillance practitioners, academic researchers, and other subject thing experts convened past the International Society for Disease Surveillance, conducted a systematic main literature review using the PRISMA framework to place research, published through February 2013, answering either of the following questions:

1. Can social media exist integrated into disease surveillance exercise and outbreak management to support and meliorate public health?
2. Tin can social media be used to effectively target populations, specifically vulnerable populations, to test an intervention and interact with a community to amend health outcomes?

Examples of social media included are Facebook, MySpace, microblogs (due east.g., Twitter), blogs, and word forums. For Question ane, 33 manuscripts were identified, starting in 2009 with topics on Influenza-like Illnesses (n = fifteen), 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 outset in 2000 with topics on Health Take chances Behaviors (n = 10), Infectious Diseases (due north = three), Non-infectious Diseases (n = 9), and Other (n = 10).

Conclusions

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. All the same, information gleaned from the manufactures demonstrates the effectiveness of social media in supporting and improving public health and in identifying target populations for intervention. A chief recommendation resulting from the review is to identify opportunities that enable public health professionals to integrate social media analytics into affliction surveillance and outbreak direction practice.

Citation: Charles-Smith LE, Reynolds TL, Cameron MA, Conway Thousand, Lau EHY, Olsen JM, et al. (2015) Using Social Media for Actionable Disease Surveillance and Outbreak Direction: A Systematic Literature Review. PLoS One x(10): e0139701. https://doi.org/x.1371/journal.pone.0139701

Editor: Lidia Adriana Braunstein, IFIMAR, UNMdP-CONICET, Argentina

Received: May 18, 2015; Accepted: September xv, 2015; Published: Oct 5, 2015

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, congenital upon, or otherwise used past anyone for any lawful purpose. The work is fabricated available under the Artistic Commons CC0 public domain dedication

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

Funding: Project management back up for this effort was provided by the International Society for Disease Surveillance. Participation of CDC and LECS was supported in role by Pacific Northwest National Laboratory's Laboratory Directed Research and Development Plan.

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

Introduction

Social media advice 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 existence captured past other sources. Social media can create a sense of anonymity, allowing for unadulterated personal expression when compared to traditional face-to-face meetings, specially amidst young people and about intimate matters [i]. In this respect, social media provide an boosted informal source of information that can be used to identify health information not reported to medical officials or health departments and to reveal viewpoints on health-related topics, peculiarly of a sensitive nature.

In the past 10 years, enquiry articles connecting affliction surveillance with Cyberspace use have increased in number, most likely due to the increase in availability of wellness-related information from various Internet sites. For example, Wikipedia article hits [2], Google search terms (Google Influenza Trends) [3], and online eating house reservation availability (OpenTable) [4] were modeled against the number of patients with flu-similar disease (ILI) reported by the Centers for Illness Control and Prevention (CDC). Several literature reviews accept looked at the potential of this type of research to benefit human wellness.

Moorhead et al. conducted a review of research studies to identify potential uses, benefits, and limitations of social media to appoint the full general public, patients, and health professionals in wellness communication [5]. Although articles identified benefit from using social media in health communications, the authors note a lack of enquiry focused on the evaluation of short- and long-term impacts on wellness communication practices. Bernardo et al. provided a scoping review of the use of search queries and social media in disease surveillance [6]. Get-go reported in 2006, the reviewed literature highlighted accurateness, speed, and cost functioning that was comparable to existing affliction surveillance systems and recommended the employ of social media programs to back up those systems.

Velasco et al. divers their literature review to contain but peer-reviewed articles on effect-based disease surveillance [7] in which they identified and described 12 existing systems. Walters et al. described numerous systems implemented and dedicated to biosurveillance, divers every bit "the subject field in which diverse information streams such as these are characterized in existent or well-nigh-real time to provide early warning and situational sensation of events affecting homo, establish, and creature health," many of which center around homo affliction outbreaks [8]. The paper points out that including emerging media, such as blogs and Curt 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 Social club for Illness Surveillance (ISDS), we established a social media working group (henceforth called the workgroup) to develop research, engineering science, and operational innovations in electronic public health surveillance. Nosotros proposed to evaluate the apply 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 accost these goals, we followed the PRISMA process [nine] by systematically compiling and analyzing literature that demonstrates innovation in electronic public health surveillance through the utilize of social media.

Past focusing on how research on social media information (further defined beneath) can exist used for actionable disease surveillance, nosotros are able to bring to light the best means of using these tools to target vulnerable populations and better public health in the broad spectrum from identifying and monitoring disease outbreaks to addressing traditionally intractable health concerns, such as boyish drug and alcohol employ.

Methods

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 enquiry using social media analytics for active surveillance, S1 Checklist. A social media application was divers for this review as, "an Net-based awarding where people tin can communicate and share resources and information, and where users tin can activate and set up 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 give-and-take forums. Articles using data sources, such every bit Internet searches, ProMed-postal service, and citizen-generated information were not included. In March 2013, a query of scientific literature databases (PubMed, Embase, Scopus, and Ichushi-Spider web) was conducted for all literature published through February 2013 to make up one's mind potential publications for review by the workgroup (Table one).

Searches were further refined to include merely human subjects and to exclude review (i.due east., 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 improver to these searches, other manufactures reviewed for potential inclusion were the ISDS research committee monthly literature review collection (http://www.syndromic.org/cop/research) and references from relevant manufactures, 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 groundwork specialties, (e.g., public health physician, doctor of veterinary medicine, data scientist, public wellness 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: writer(s), date of publication, publication type (eastward.g., journal, conference proceedings, white or gray literature), data source blazon (e.g., social networking sites, microblogs, or open source databases). Requirements were that each study must be published as original researchand must clarify social media. The initial review was washed for all documents containing an abstruse, including peer-reviewed conference proceedings or white papers. An article was excluded if the full text was not available, if but methods were described (i.e., building an awarding programming interface, but no results), or if it did not direct address one of the 2 following research questions:

1. Q1. Tin social media be integrated into disease surveillance practice and outbreak management to support and meliorate public health?
2. Q2. Tin social media exist used to effectively target populations, specifically vulnerable populations, to test an intervention and interact with a community to ameliorate health outcomes?

Any differences of opinion about whether to include a newspaper were resolved through discussion until the workgroup accomplished a consensus on inclusion or exclusion.

For each article fitting the review inclusion criteria, one workgroup member was assigned to excerpt and tape specific details from the full-text commodity. This information included background (due east.chiliad., study objective, sample population and size, and the location, setting, time, and elapsing of the study), methods (due east.g., written report design, keywords, classification methods), outcomes measured (east.g., population, affliction studied, intervention or exploratory (i.e., whether the report evaluates the bear upon of or observes the employ of social media, respectively), hypothesis, outcomes related to either enquiry question), results, and conclusions. To appraise involvement of a public wellness jurisdiction in the written report 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 report. Note that to date, this study protocol has not been registered.

Results

Written report Option

We identified 1,405 English language studies published through Feb 2013 in peer-reviewed journals, briefing proceedings, and white/greyness literature through Embase, PubMed, and Scopus database searches, as well every bit 8 manufactures 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 abstruse screening step. We excluded one,205 of these studies because they were reviews, letters, commentaries, or did not accost either of the research questions. An additional viii studies were excluded because the total-text publications were not available. These excluded written report 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–thirteen] to detecting unusual events, anomalies, and social disruption for early warning systems [14].

Fig 1. Menses diagram for the selection of literature reviewed.

The abstract screening process resulted in 286 studies identified for detailed review of total-text manufactures. After this review, we farther excluded studies that did not meet our definition of social media (e.g., Internet search, ProMED-mail) or discussed methods exclusively. Nosotros identified a total of lx studies that met our eligibility criteria and addressed at to the lowest degree one of the ii research questions. This procedure took over a twelvemonth to consummate because the authors donated their gratis time to review and analyze the literature.

https://doi.org/10.1371/journal.pone.0139701.g001

Study Characteristics

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 Due north America [15–30], 24% (north = viii) 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 [46], and 3% (n = 1) on a global calibration [47]. Twitter was used equally the primary information source in 81% (due north = 27) of the studies although Facebook, various blogs, and health-related discussion forums were also investigated [fifteen–17,19–23,26,27,29–forty,43–47]. The studies examined information from Jan 2006 [46] to January 2012 [38] with the bulk focused on 2009. The drove menstruation spanned the 2009 H1N1 flu pandemic, and 45% (north = 15) of the papers focused on flu monitoring [15,xvi,xix,27,31–33,38–40,43–45,47]. Comparison to CDC reports were nearly commonly used to evaluate the effectiveness of the diverse surveillance techniques presented. Most Twitter-based studies identified study populations through automatic means, i.e., Twitter keyword searches such every bit "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 m [38] to ii billion [22], or the number of unique social media users, ranging from 118 users [28] to 24.five million [33]. About studies were published in English (ii in Japanese), and all were exploratory in nature.

Question 2 –Targeted Vulnerable Populations.

Thirty-ii studies were identified as targeting a vulnerable population to amend health outcomes. These studies emphasized interaction with users rather than automated algorithms and therefore typically contained focused populations and smaller datasets. The written report sizes ranged from 19 post-partum women [48] to 155,508 Twitter users from 9 distinct areas [26]. All of the studies included were published in English and 66% (n = 21) were conducted in Northward America [twenty,21,25,26,49–65], 12% (northward = four) in Australia [48,66–68], 9% (north = 3) in Asia [41,69,70], and half dozen% (n = 2) in Europe [1,71]. Most of the studies were classified every bit exploratory, although 24% (n = 8) of studies did include some type of intervention [1,55,56,58,60,64,69,71]. Populations studied were by and large more focused than Question ane studies, eastward.g., meaning smokers in Australia. The study populations dated from January 2000 [1] to Feb 2012 [66], although many do non disclose study periods. Interestingly, the studies addressing Question ii first appeared in 2000, merely published literature on Question 1 does not appear until 2010. Also, at that place is a spike in addressing both questions during 2011 (Fig 2).

Bias Beyond and Inside Studies

The spectrum of studies selected for review were subject to publication bias because merely primary literature was included and, therefore, other not-published information collected past country or federal wellness agencies was non incorporated. The choice of data search engines may have excluded valid studies that may non have been published in journals exposed through this process. In add-on, at that place may be more recent articles published since our drove end engagement of March 2013.

Inside the 60 studies reviewed, no of import bias was identified by the authors and workgroup reviewers in 43% (north = 26) of the studies [1,nineteen–21,23,24,29,31,34–36,38,39,41,43–45,49,53,57,61–63,66,70,72]; 56% (northward = 34) displayed some caste of bias take a chance. The types of bias tin be broken down into six different categories. Pick bias (n = 17) was the most prevalent equally data was oft collected out of convenience [25,51,52,60], at focused locations [15,46], or within specific social groups [65] and, therefore, was often non representative of the total population [15,26,28,30,46–48,51,54,58,59,65,71,73]. There were fourteen articles displaying a faulty study pattern due to the choice of time period [xvi,17,27,33,37,40,42,47], data source [18,42,sixty], study telescopic [37,42], reporting of results [37,54], or lack of result measure out [50,51]. Temporal human relationship and directionality bias within 8 studies acquired problems 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 besides pocket-size to depict conclusions, i.east., sample size bias [17,xxx,55,64,67]. Other biases nowadays in the reviewed articles were reliability bias [22,28,54,68] and selective interpretation bias [32,40].

Public Health Involvement

In that location was a pocket-sized number of local (3%, n = 2) and state (15%, northward = 9) governmental public wellness agencies involved in the studies reviewed for actionable health and disease surveillance (Table 2). These supportive agencies reside in England (London) and the The states of America (USA) (California, Louisiana, Michigan, and Washington State). Public health involvement was mainly in budgetary support on a national level (n = 30) from Brazil, Canada, Germany, Nippon, Netherlands, Switzerland, Taiwan, and USA. One inquiry newspaper, funded by the Academy of Maryland, USA, described the implementation of social media advice past a local Taiwan government for disaster management, which showed promise over current national sensation and response protocols [69]. Other universities showing interest in support of social media research were located in Australia, Germany, Italy, Japan, United kingdom of great britain and northern ireland (UK), and USA. The private funding agencies that supported reviewed literature are found in the UK and USA. Simply iii papers contained a co-writer who was affiliated with a public health agency, i.e., Public Health Agency of Canada [24], Governmental Constitute of Public Health of Lower Saxony in Germany [37], and National Cancer Institute in USA [60].

For both research questions, this table records the number of articles in which private organizations, universities, and governments (local, state and national) contributed equally funding agencies and/or organizations with direct interest in each study or intervention reviewed. Annotation that some articles contained more than ane funding agency.

Question 1: Tin can social media exist integrated into disease surveillance practise and outbreak direction to support and improve public wellness?

The central to our systematic literature review of Question i was to identify if, when, and how social media have been applied for illness surveillance and outbreak direction to support and improve public health. Within the 33 manuscripts identified every bit addressing this question, we institute an overwhelming number focused on influenza-like illnesses (45%, n = 15). For the remaining articles, we classified the instances into Infectious Diseases (due north = vi), Non-infectious Diseases (n = iv), Medication and Vaccines (n = 3), and Other (n = 5) to understand the extent and focus of current research. All of these studies were exploratory inquiry and did not incorporate any type of intervention analysis.

Flu-like Illness.

Flu-like illness (ILI) was the first affliction 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.one thousand., Twitter and other blogs) from the USA, Great britain, and Nihon between 2008 and 2012. From simple text searches, (e.g., influenza or influenza [32,forty,45]) to more specific influenza subtypes (e.g., H1N1, Swine Flu [16,44,47]) and symptomatic illness sets [15,17,19,31,32,38,39], all of the studies claimed to exist able to use the social media data in existent-fourth dimension disease surveillance. A study past Sadilek, Kautz, and Silenzio (2012), practical their technique to identify the health of any person through geo-tagged Twitter microblogs in an effort to predict disease transmission [15]. In full general, correlation betwixt social media information and national health statistics, due east.g., from the CDC, ranged from 0.55 [18] to 0.95 [43] and was shown to predict outbreaks before the standard outbreak surveillance method favored by each land [19,31,32,38,39].

Infectious Diseases.

Nosotros identified 6 studies that used different social media programs to decide if the timeliness and sensitivity of detection for other infectious disease outbreaks (east.m., 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 beginning 100 days of the 2010 outbreak [23]. The changes in social media and news data trends were detected up to 2 weeks earlier than official case data, which they believe could take had straight implications on the affliction outbreak and control measures taken [23]. After analyzing 7 million tweets on medical conditions during the 2011 Enterohaemorrhagic E. coli (EHEC) outbreak in Germany, Diaz-Aviles et al. (2012) establish over 450,000 posts related to the outbreak and adamant that this information would have detected the outbreak 1 day before than other alert systems [34]. Gomide et al. (2011) showed a correlation between Twitter posts in Brazil and dengue outbreaks (e.one thousand., reported dengue cases correlated with the word "dengue" (0.78) and personal experience with dengue (0.96)) [46]. Withal, they reported that only 40% of tweets included location, which limited spatial assay [46]. Although the breadth of studies is limited and nearly ofttimes retrospective, detection of outbreaks through social media tracking appears to provide a timeliness advantage in a variety of infectious disease outbreak settings.

Not-infectious Diseases.

The 4 studies identified as targeting non-infectious diseases were purely exploratory and focused on booze, tobacco, and sexual action (Table 5). Facebook [25] and Twitter [27] were used to place associations between alcohol references and misuse in college students or booze sales, respectively. It was shown that social media references to booze correlated with college students' cocky-reported alcohol utilize, including booze-related injuries, and the U.South. Census Bureau's alcohol sales volume. Therefore, social media data can enhance alcohol utilize surveillance and target specific audiences in need of health support. Another study, directed at college freshmen's Facebook utilize, 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 [28]. Prier et al. (2011) examined dissimilar tools available to nearly effectively place public health topics on Twitter [26]. They found that the Latent Dirichlet Allocation (LDA) topic modeling method was successful in identifying wide topics, e.yard., 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 find lower-frequency topics such as tobacco use. Overall, the study showed that social media tin can be used to promote both positive and negative heath behaviors.

Medication and Vaccines.

Social media discussions can be used to decide attitudes, misinformation, and adverse events related to medications, vaccines, and other drug uses (Tabular array half-dozen). Salathé and Khandelwal (2011) identified an increase in Twitter data between August and November 2009 related to the launch of the 2009 flu H1N1 vaccine [20]. Tweets amidst opinionated users most oftentimes 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 college gamble of infection. This evidence may assistance in targeting public wellness interventions of unvaccinated people at take chances 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 [21]. Twitter feeds provide a forum for discussions regarding medications and, therefore, can exist targeted to ameliorate data dissemination. Bian et al. scanned Twitter feeds for 5 different drugs and establish 239 drug users with 27 drug-related adverse event tweets [22]. This study identifies back up for pharmacovigilance through social media analysis, especially concerning new drug releases.

Other.

Many researchers have evaluated ways to best access and use wellness information on Twitter for affliction surveillance (Table 7). A group in Deutschland retrospectively reviewed tweets that contained keywords of infectious disease symptoms and institute 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 [37]. Sofean and Smith (2012) designed and evaluated a real-time architecture for collecting and filtering disease-related postings on Twitter and found they could rails health status in real time [36]. Other researchers developed methods for pulling social media, including using a Badu search engine [42] and the Ailment Topic Attribute Model (ATAM) [29]. 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, then analyzes the correlations of symptoms and treatments. The ATAM is able to discover whatever coherent ailments, symptoms and handling and does not have to exist disease-specific [thirty]. Using a variety of search engines and new tools, information technology is possible to discover and track a multifariousness of health ailments using social media.

Question ii: Can social media exist used to effectively target populations, specifically vulnerable populations, to examination an intervention and interact with a community to improve wellness outcomes?

For question 2, we identified if, when, and how social media have been used to target populations and transform information gleaned from this information into action. The bulk of studies within this group used social media to place health risk behaviors (n = 10) and evaluate utilise of virtual communities to aid in risk reduction. For the remaining articles, we classified the instances into Infectious Diseases (n = 3), Not-infectious Diseases (n = 9), and Other (n = 10) to get a ameliorate 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], take been used to target adolescents displaying health risk behaviors associated with substance abuse and sexual activities (Table viii). Specialized chat rooms, websites, and Twitter have been targeted for adult health adventure behavior with tobacco utilize [26,48,lx], substance corruption [53], and sexual activities [58]. The specific populations, located in the USA and Australia, include college students [49,56,68], post-partum women [48], men who have sex with men (MSM) [58], and depression-income youth [57]. These studies prove that social media tin can be effective at identifying adolescent populations displaying substance abuse, especially alcohol [25,49,68], in addition to sexual behavior [57], and that social media can improve community health outcomes in at-risk adolescents [56] and MSM [58]. Interestingly, tobacco-related subjects posed an event for researchers who tried to employ topic modeling in Twitter [26] and found that the use of a virtual customs bulletin lath to reduce smoking behavior was ineffective [60]. Every bit proposed by Prier et al. (2011), the utilize of low-frequency topics, such as tobacco use, may require human being intervention for selection of query terms and relevant subsequent assay to properly address health concerns [26].

Infectious Diseases.

Ii 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 example reports [62]. Targeted imprint 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–i.8), but with limited event on reducing dropouts. In the 2009 H1N1 pandemic, Szomszor, Kostkova, and de Quincey found that wellness advice via official Twitter feeds and trusted news organizations (e.yard., BBC) was most effective in reaching the public; nonetheless, timeliness of wellness information may not directly interpret to site popularity amidst these trusted sources [72]. In addition, they found twoscore% of appreciable wellness-related data identified on the Cyberspace containing poor scientific merit was directly linked to spam. Overall, the studies showed potential in reaching populations concerning socially stigmatized or sensitive health conditions, just time and effort are needed to build upward a trusted aqueduct for information broadcasting.

Non-Infectious Diseases.

Social media could potentially be used to target populations with illnesses of high prevalence and public wellness touch on (e.thousand, low, cancer, obesity, diabetes, and asthma) with an intervention to amend health outcomes. In a 16-week study of 32 women with chest cancer, an intervention using an electronic support grouping 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 [55]. At that place were some dropouts in participation, which was attributed to different personalities' response to the electronic support grouping. Similarly, researchers set up a chat room to provide an educational tool for adolescents with Blazon 1 diabetes and found that it significantly increased compliance and decreased HbA(1c) concentrations (from 8.9% to 7.8%) over a menstruum of iii months [ane]. Mobile support programs used to increase dietary self-monitoring and ameliorate weight loss resulted in body weight changes; however, a similar study using Twitter did not find any differences [64]. Therefore, the types of social media and the populations who volition utilize 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 x). For mental wellness, a study of college freshmen showed that 46% of female person and 21% of male students referenced stress, low, or stress-related conditions, e.thou., weight issues or drinking alcohol, on Facebook, and those who referred to stress were significantly more probable to mention weight concerns or low [51]. These researchers ended that Facebook may provide a mode of distribution of targeted stress reduction information. Similarly, researchers in Australia institute that 44% of students reported the need for mental health support; within this group, 50% of them already employ the Internet and 47% said they would utilise online social networks for mental health problems [67]. Social media could be used to identify those with non-infectious diseases and provide pedagogy and support to ameliorate public health.

Other.

Social media was used to identify "other" target populations, east.m., low-income groups [61] and older people in need of physical action [71], to assess vaccination sentiments [twenty] and misuse of antibiotics [73] (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.South. Section of Health and Human Service regions [21]. Clusters of unprotected individuals with negative vaccination sentiments tin exist identified and targeted for tailored interventions. Scanfeld, Scanfeld, and Larson (2010) identified individuals from Twitter who may take misused antibiotics for treating viral infections who could be targeted for health-related teaching [73]. Dissemination of valid health information among the identified groups may promote behavioral alter towards a healthier lifestyle.

Discussion

This systematic primary literature review on the use of social media to support public wellness do has identified many evidence gaps and biases in the current knowledge on this topic. There are few studies to engagement on interventions and a lack of apply of social media in do despite the high potential for success identified in exploratory studies. This mirrors the lack of scientific reports published (due north = 16) on performance assessment of disease surveillance methods found by Babaie et al. (2015), regardless of their necessity to public wellness response [74]. Our findings may suggest that it is particularly challenging to interpret research using social media for biosurveillance into practise. This claiming may be amplified by the lack of an upstanding framework for the integration of social media into public health surveillance systems [75]. In addition, the focus of many studies, particularly 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 resource or technical skills necessary for successful execution in the public health domain. Alternatively, public health departments may exist using social media as a tool only non publishing their efforts. Due to the number of heterogeneous data sources used in assay, a comparing and evaluation of techniques was not possible. Nonetheless, this review demonstrates some evidence that the use of social media information could provide real-fourth dimension surveillance of health issues, speed up outbreak management, and place target populations necessary to support and improve public wellness and intervention outcomes.

Social media tin impact the public wellness surveillance domain, bringing the wider media landscape to the public wellness customs. This impact has been particularly important in the context of public health emergencies, such as afterward Haiti's mail service-earthquake cholera outbreak, where the utility of using social media as a information source in rapidly changing and dynamic situations was clearly shown [23]. Pharmacovigilance is another key expanse where social media have demonstrated value. Traditional methods of reporting adverse drug events rely on gatekeepers (east.g., clinicians and pharmaceutical companies) to alert authorities of these events. Social media, in particular Twitter, have shown pregnant potential for creating real-fourth dimension admission to firsthand reports of adverse drug events, thereby bypassing the gatekeeper clogging [22,76].

Traditionally hard-to-reach groups, east.chiliad., MSM and adolescents, may be more likely to appoint with social media rather than with more conventional public health communication channels, creating a new avenue to accost sensitive health issues. A meaning proportion of the interventions reviewed (xl%) full-bodied on targeting populations with increased chance of STIs, a topic frequently avoided in public settings [xi,12,56,58]. Mental health intervention studies suggested that immature people would be willing to use social media to address mental wellness bug [51,65,67,70]. In this context, the type of mediations must fit the social media outlet targeted. For case, mental health interventions conducted via Twitter, with a 140 character limit, are likely to exist very dissimilar from the kinds of interventions conducted through the more than discursive advice possible with Internet discussion forums.

Different target groups, e.g., historic period groups, may adopt different social media outlets. Consequently, knowing the population and how they employ social media can be a critical part of successful intervention and surveillance. For example, in the articles reviewed focusing on health risk behaviors, nosotros establish that adolescents were targeted using Facebook [25,49,68] and MySpace [10,56,57], while adults were targeted inside Twitter and specialized chat rooms and websites [26,48,53,58,60]. Even so, to our knowledge, there is no directed health-related scientific research addressing which social media outlet should exist targeted for specific populations. For health surveillance, the impact of the potential lack of population representativeness in the utilise of social media to detect and track disease outbreaks has not been adequately researched.

In addition, different topics may crave dissimilar search techniques to identify targeted populations. In the study by Prier et al. (2011), they were successful at identifying wide topics in social media, eastward.k., physical action and obesity, using the LDA method, notwithstanding for lower-frequency topics, such as tobacco use, man intervention was required for selection of query terms to create a smaller, more than focused data set in which subsequent analysis was possible [26]. Similarly, the type of social media platform used in analyses may change based on the target population and the years beingness studied. For case, 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 advisable assay techniques to achieve the greatest number of results or largest audience for intervention.

Since February 2013, the last search appointment reported in this review, there has been an increase in the number of articles published per yr using a similar initial search query to this review. Estimating an eighty% exclusion charge per unit based on the results discussed above, around xc new articles per yr 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 beingness applied to a different affliction. Regardless, an upwards 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 wellness 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 wellness professionals.

Supporting Information

Acknowledgments

This work was supported by the International Society for Illness Surveillance. The authors thank Christine Noonan at Pacific Northwest National Laboratory (PNNL) for project support, equally well equally Amy Ising, University of North Carolina at Chapel Hill, William Storm, Ohio Section of Health, and Silvia Valkova, IMS Plant for Healthcare Informatics, for their contributions to the project. Participation of Courtney D. Corley and Lauren Due east. Charles-Smith was supported in part by the PNNL'south Laboratory Directed Research and Development Program. Pacific Northwest National Laboratory is operated for the U.S. Department of Free energy by Battelle under Contract DE-AC05-76RL01830. The views expressed in this commodity are those of the authors and do not necessarily represent the views of the U.S. Department of Defense, U.Southward. Section of Veterans Affairs, or Health Services Research and Evolution Service.

Author Contributions

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.

References

1. 1. Iafusco D, Ingenito Northward, Prisco F. The chatline as a communication and educational tool in adolescents with insulin-dependent diabetes: preliminary observations. Diabetes Care. 2000;23: 1853–1853.
   • View Article
   • Google Scholar
2. 2. McIver D, Brownstein J. Wikipedia Usage Estimates Prevalence of Influenza-Similar Illness in the United States in Nigh Real-Fourth dimension. PLoS Comput Biol. 2014;10: 1–8.
   • View Article
   • Google Scholar
3. 3. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Vivid L. Detecting influenza epidemics using search engine query information. Nature. 2009;457: 1012–1014. pmid:19020500
   • View Commodity
   • PubMed/NCBI
   • Google Scholar
4. 4. Nsoesie E, Buckeridge D, Brownstein J. Estimate Who's Not Coming to Dinner? Evaluating Online Eating house Reservations for Affliction Surveillance. J Med Cyberspace Res. 2014;16.
   • View Commodity
   • Google Scholar
5. 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 Advice. J Med Internet Res. 2013;15.
   • View Article
   • Google Scholar
6. half dozen. Bernardo TM, Rajic A, Young I, Robiadek Grand, 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
   • View Article
   • PubMed/NCBI
   • Google Scholar
7. 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: seven–33. pmid:24597553
   • View Commodity
   • PubMed/NCBI
   • Google Scholar
8. 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. 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.
   • View Commodity
   • Google Scholar
10. 10. Moreno MA, Brockman Fifty, Christakis DA. "Oops, I Did It Again": A Content Assay of Adolescents' Displayed Sexual References on Myspace. J Adolesc Wellness. 2009;44: S22–S23.
    • View Article
    • Google Scholar
11. 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 Social club for STD Research Conferences. London; 2009.
12. 12. Schmidt M, Currie One thousand, Bertram Southward, Bavinton T, Bowden F. Promoting Chlamydia Testing to Young Women, Their Partners and Their GPs using mod and social media. SEXUAL Health. 2009. pp. 369–369.
13. 13. Yamauchi E. MYMsta: Using Mobile Social Networking in HIV Prevention. Sex:: Tech Conference 2010. San Francisco; 2010.
14. 14. Vaillant L, Barboza P, Arthur R. Epidemic Intelligence: Assessing event-based tools and users' perception in the GHSAG community [Cyberspace]. Presentation presented at: IMED; 2011; Vienne, French republic. Bachelor: http://www.isid.org/events/archives/IMED2011/Downloads/IMED2011_Presentations/IMED2011_Vaillant.pdf.
15. 15. Sadilek A, Kautz H, Silenzio V. Predicting disease manual from geo-tagged micro-blog data. AI Access Foundation; 2012. pp. 136–142.
16. 16. Achrekar H, Gandhe A, Lazarus R, Yu SH, Liu B. Twitter improves seasonal flu prediction. SciTePress; 2012. pp. 61–70. Bachelor: http://www.cs.uml.edu/~bliu/pub/healthinf_2012.pdf.
17. 17. Culotta A. Towards detecting influenza epidemics past analyzing Twitter messages. Clan for Calculating Machinery; 2010. pp. 115–122. Bachelor: https://doi.org/10.1145/1964858.1964874
18. xviii. Corley CD, Melt 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
    • View Article
    • PubMed/NCBI
    • Google Scholar
19. 19. Signorini A, Segre AM, Polgreen PM. The Use of Twitter to Runway Levels of Illness Activity and Public Concern in the U.S. during the Flu A H1N1 Pandemic. PLoS ONE. 2011;half dozen: e19467. pmid:21573238
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
20. 20. Salathé G, Khandelwal South. Assessing Vaccination Sentiments with Online Social Media: Implications for Infectious disease Dynamics and Command. PLoS Comput Biol. 2011;seven.
    • View Article
    • Google Scholar
21. 21. Salathé 1000, 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.
    • View Article
    • Google Scholar
22. 22. Bian J, Topaloglu U, Yu F. Towards large-scale twitter mining for drug-related agin events. Association for Calculating Machinery; 2012. pp. 25–32. Available: https://doi.org/ten.1145/2389707.2389713
23. 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
    • View Article
    • PubMed/NCBI
    • Google Scholar
24. 24. Stuart Chester TL, Taylor M, Sandhu J, Forsting Due south, 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.
    • View Article
    • Google Scholar
25. 25. Moreno MA, Christakis DA, Egan KG, Brockman LN, Becker T. Associations between displayed alcohol references on Facebook and trouble drinking among college students. Arch Pediatr Adolesc Med. 2012;166: 157–163. pmid:21969360
    • View Article
    • PubMed/NCBI
    • Google Scholar
26. 26. Prier KW, Smith MS, Giraud-Carrier C, Hanson CL. Identifying health-related topics on twitter an exploration of tobacco-related tweets as a exam topic. Springer Verlag; 2011. pp. eighteen–25. Available: https://doi.org/10.1007/978-3-642-19656-0_4
27. 27. Culotta A. Lightweight methods to estimate flu rates and alcohol sales volume from Twitter messages. Lang Resour Eval. 2013;47: 217–238.
    • View Article
    • Google Scholar
28. 28. Moreno MA, Brockman L, Wasserheit J, Christakis DA. A Airplane pilot Evaluation of Older Adolescents' Sexual Reference Displays on Facebook. J Sexual activity Res. 2012;49: 390–399. pmid:22239559
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
29. 29. Paul MJ, Dredze M. A model for mining public wellness topics from Twitter. Wellness (N Y). 2012;11: 16–half dozen.
    • View Article
    • Google Scholar
30. 30. Paul MJ, Dredze M. You Are What You Tweet: Analyzing Twitter for Public Health. ICWSM. 2011.
    • View Article
    • Google Scholar
31. 31. Lampos V, Cristianini N. Tracking the flu pandemic past monitoring the social spider web. IEEE Computer Gild; 2010. pp. 411–416. Available: https://doi.org/x.1109/CIP.2010.5604088
32. 32. Szomszor M, Kostkova P, De Quincey E. #Swineflu: Twitter predicts swine flu outbreak in 2009. Springer Verlag; 2011. pp. 18–26. Bachelor: https://doi.org/10.1007/978-3-642-23635-8_3
33. 33. Doan S, Ohno-Machado 50, Collier N. Enhancing Twitter Data Analysis with Simple Semantic Filtering: Example in Tracking Flu-Like Illnesses. 2012 IEEE Second International Briefing on Healthcare Informatics, Imaging and Systems Biology (HISB). 2012. pp. 62–71. https://doi.org/ten.1109/HISB.2012.21
34. 34. Diaz-Aviles E, Stewart A. Tracking Twitter for Epidemic Intelligence. Instance written report: EHEC/HUS outbreak in Deutschland, 2011. Clan for Computing Mechanism; 2012. pp. 82–85. Available: https://doi.org/10.1145/2380718.2380730
35. 35. Diaz-Aviles East, Stewart A, Velasco E, Denecke Grand, Nejdl W. Towards personalized learning to rank for epidemic intelligence based on social media streams. Association for Computing Machinery; 2012. pp. 495–496. Available: https://doi.org/ten.1145/2187980.2188094
36. 36. Sofean Thou, Smith Grand. A real-time architecture for detection of diseases using social networks: Design, implementation and evaluation. Association for Calculating Machinery; 2012. pp. 309–310. Available: https://doi.org/10.1145/2309996.2310048
37. 37. Krieck Yard, Dreesman J, Otrusina L, Denecke K. A new historic period of public health: Identifying disease outbreaks by analyzing tweets. Proceedings of Wellness Web-Scientific discipline Workshop, ACM Web Science Conference. 2011.
38. 38. Ishikawa T. Evaluation of microblog every bit influenza surveillance source. J Natl Inst Public Health. 2012;61.
    • View Article
    • Google Scholar
39. 39. Okamura Due north, Seki K, Uehara G. Using Microblog for Syndromic Surveillance. IPSJ SIG Tech Rep. 2011;
    • View Article
    • Google Scholar
40. xl. Aramaki East, Maskawa S, Morita M. Twitter catches the flu: Detecting flu epidemics using Twitter. Association for Computational Linguistics (ACL); 2011. pp. 1568–1576.
41. 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. Bachelor: https://doi.org/10.1109/ISI.2010.5484749
42. 42. Yang M, Li YJ, Kiang M. Uncovering social media information for public health surveillance. Pacific Asia Briefing on Information Systems; 2011.
    • View Commodity
    • Google Scholar
43. 43. Culotta A. Detecting influenza outbreaks by analyzing Twitter letters [Internet]. 2010 Jul. Report No.: 1007.4748. Available: http://arxiv.org/abs/1007.4748
44. 44. Collier Northward, Son NT, Nguyen NM. OMG U got flu? Analysis of shared wellness letters for bio-surveillance. J Biomed Semant. 2011;2: S9.
    • View Article
    • Google Scholar
45. 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. Bachelor: https://doi.org/10.1007/978-iii-642-11745-9_4
46. 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 Spider web Science Conference (WebSci). 2011. pp. 1–8.
47. 47. Chew C, Eysenbach Yard. Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak. PLoS One. 2010;5: e14118. pmid:21124761
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
48. 48. Lowe JB, Barnes M, Teo C, Sutherns Southward. Investigating the use of social media to help women from going back to smoking post-partum. Aust North Z J Public Health. 2012;36: 30–32. pmid:22313703
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
49. 49. Litt DM, Stock ML. Boyish 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.
    • View Commodity
    • Google Scholar
50. 50. Baptist AP, Thompson Yard, 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.
    • View Article
    • Google Scholar
51. 51. Egan KG, Moreno MA. Prevalence of stress references on higher freshmen Facebook profiles. Comput Inform Nurs CIN. 2011;29: 586–592. pmid:21436681
    • View Article
    • PubMed/NCBI
    • Google Scholar
52. 52. Fisher J, Clayton 1000. Who Gives a Tweet: Assessing Patients' Interest in the Employ of Social Media for Wellness Care. Worldviews Evid Based Nurs. 2012;9: 100–108. pmid:22432730
    • View Article
    • PubMed/NCBI
    • Google Scholar
53. 53. Frost J, Okun South, Vaughan T, Heywood J, Wicks P. Patient-reported Outcomes every bit a Source of Evidence in Off-Characterization Prescribing: Analysis of Data From PatientsLikeMe. J Med Internet Res. 2011;thirteen.
    • View Article
    • Google Scholar
54. 54. Idriss SZ, Kvedar JC, Watson AJ. The part of online back up communities: benefits of expanded social networks to patients with psoriasis. Curvation Dermatol. 2009;145: 46–51. pmid:19153342
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
55. 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
    • View Article
    • PubMed/NCBI
    • Google Scholar
56. 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 spider web site: a randomized controlled pilot intervention trial. Arch Pediatr Adolesc Med. 2009;163: 35–41. pmid:19124701
    • View Article
    • PubMed/NCBI
    • Google Scholar
57. 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.
    • View Article
    • Google Scholar
58. 58. Rhodes SD, Hergenrather KC, Duncan J, Vissman AT, Miller C, Wilkin AM, et al. A Airplane pilot Intervention Utilizing Internet Chat Rooms to Preclude HIV Risk Behaviors Among Men Who Have Sex with Men. Public Health Rep. 2010;125: 29–37. pmid:20408385
    • View Article
    • PubMed/NCBI
    • Google Scholar
59. 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.
    • View Article
    • Google Scholar
60. sixty. Stoddard JL, Augustson EM, Moser RP. Outcome of Adding a Virtual Customs (Message Board) to Smokefree.gov: Randomized Controlled Trial. J Med Internet Res. 2008;x.
    • View Commodity
    • Google Scholar
61. 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;viii: A148. pmid:22005641
    • View Article
    • PubMed/NCBI
    • Google Scholar
62. 62. Sullivan PS, Khosropour CM, Luisi Northward, Amsden M, Coggia T, Wingood GM, et al. Bias in Online Recruitment and Memory of Racial and Ethnic Minority Men Who Have Sex With Men. J Med Cyberspace Res. 2011;13: e38. pmid:21571632
    • View Article
    • PubMed/NCBI
    • Google Scholar
63. 63. Tsaousides T, Matsuzawa Y, Lebowitz M. Familiarity and prevalence of Facebook use for social networking amidst individuals with traumatic brain injury. Encephalon Inj BI. 2011;25: 1155–1162. pmid:21961574
    • View Article
    • PubMed/NCBI
    • Google Scholar
64. 64. Turner-McGrievy Grand, Tate D. Tweets, Apps, and Pods: Results of the vi-Month Mobile Pounds Off Digitally (Mobile POD) Randomized Weight-Loss Intervention Among Adults. J Med Internet Res. 2011;13.
    • View Article
    • Google Scholar
65. 65. Wicks P, Massagli Grand, Frost J, Brownstein C, Okun S, Vaughan T, et al. Sharing Health Information for Better Outcomes on PatientsLikeMe. J Med Internet Res. 2010;12.
    • View Commodity
    • Google Scholar
66. 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: https://doi.org/x.1145/2414536.2414555
67. 67. O'Dea B, Campbell A. Healthy connections: online social networks and their potential for peer back up. Stud Health Technol Inform. 2011;168: 133–140. pmid:21893921
    • View Article
    • PubMed/NCBI
    • Google Scholar
68. 68. Ridout B, Campbell A, Ellis L. "Off your Face up(book)": alcohol in online social identity structure and its relation to trouble drinking in university students. Drug Alcohol Rev. 2012;31: 20–26. pmid:21355935
    • View Article
    • PubMed/NCBI
    • Google Scholar
69. 69. Huang C- Yard, Chan Eastward, Hyder AA. Web 2.0 and Net Social Networking: A New tool for Disaster Management? Lessons from Taiwan. BMC Med Inform Decis Mak. 2010;10: 57. pmid:20925944
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
70. 70. Takahashi Y, Uchida C, Miyaki K, Sakai Thousand, Shimbo T, Nakayama T. Potential Benefits and Harms of a Peer Back up Social Network Service on the Internet for People With Depressive Tendencies: Qualitative Content Analysis and Social Network Analysis. J Med Net Res. 2009;11: e29. pmid:19632979
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
71. 71. Peels DA, van Stralen MM, Bolman C, Golsteijn RHJ, de Vries H, Mudde AN, et al. Development of web-based figurer-tailored advice to promote physical activity amidst people older than 50 years. J Med Internet Res. 2012;14: e39. pmid:22390878
    • View Article
    • PubMed/NCBI
    • Google Scholar
72. 72. Szomszor G, Kostkova P, St Louis C. Twitter information science: Tracking and understanding public reaction during the 2009 Swine Flu pandemic. IEEE Figurer Society; 2011. pp. 320–323. Bachelor: https://doi.org/10.1109/WI-IAT.2011.311
73. 73. Scanfeld D, Scanfeld V, Larson EL. Dissemination of health data through social networks: Twitter and antibiotics. Am J Infect Control. 2010;38: 182–188. pmid:20347636
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
74. 74. Babaie J, Ardalan A, Vatandoost H, Goya MM, Akbarisari A. Performance Assessment of Catching Disease Surveillance in Disasters: A Systematic Review. PLOS Currents Disasters. 2015;Edition one.
    • View Article
    • Google Scholar
75. 75. Vayena E, Salathé M, Madoff Fifty, Brownstein J. Ethical Challenges of Big Data in Public Health. PLoS Comput Biol. 11: e1003904. pmid:25664461
    • View Article
    • PubMed/NCBI
    • Google Scholar
76. 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
    • View Article
    • PubMed/NCBI
    • Google Scholar

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