ArticlePDF AvailableLiterature Review

Systematic Literature Review on the Spread of Health-related Misinformation on Social Media

September 2019
Social Science & Medicine 240(6):112552

September 2019
240(6):112552

DOI:10.1016/j.socscimed.2019.112552

License
CC BY-NC-ND 4.0

Authors:

Yuxi Wang

Institut national d'études démographiques

Martin Mckee

London School of Hygiene and Tropical Medicine

Contemporary commentators describe the current period as "an era of fake news" in which misinformation, generated intentionally or unintentionally, spreads rapidly. Although affecting all areas of life, it poses particular problems in the health arena, where it can delay or prevent effective care, in some cases threatening the lives of individuals. While examples of the rapid spread of misinformation date back to the earliest days of scientific medicine, the internet, by allowing instantaneous communication and powerful amplification has brought about a quantum change. In democracies where ideas compete in the marketplace for attention, accurate scientific information, which may be difficult to comprehend and even dull, is easily crowded out by sensationalized news. In order to uncover the current evidence and better understand the mechanism of misinformation spread, we report a systematic review of the nature and potential drivers of health-related misinformation. We searched PubMed, Cochrane, Web of Science, Scopus and Google databases to identify relevant methodological and empirical articles published between 2012 and 2018. A total of 57 articles were included for full-text analysis. Overall, we observe an increasing trend in published articles on health-related misinformation and the role of social media in its propagation. The most extensively studied topics involving misinformation relate to vaccination, Ebola and Zika Virus, although others, such as nutrition, cancer, fluoridation of water and smoking also featured. Studies adopted theoretical frameworks from psychology and network science, while co-citation analysis revealed potential for greater collaboration across fields. Most studies employed content analysis, social network analysis or experiments, drawing on disparate disciplinary paradigms. Future research should examine susceptibility of different sociodemographic groups to misinformation and understand the role of belief systems on the intention to spread misinformation. Further interdisciplinary research is also warranted to identify effective and tailored interventions to counter the spread of health-related misinformation online.

Topic categories.

…

Figures - uploaded by Yuxi Wang

Content may be subject to copyright.

Content uploaded by Yuxi Wang

Content may be subject to copyright.

Contents lists available at ScienceDirect

Social Science & Medicine

journal homepage: www.elsevier.com/locate/socscimed

Review article

Systematic Literature Review on the Spread of Health-related

Misinformation on Social Media

Yuxi Wang

a,∗

, Martin McKee

, Aleksandra Torbica

, David Stuckler

Centre for Research on Health and Social Care, Department of Social and Political Science, Bocconi University, Italy

London School of Hygiene and Tropical Medicine, United Kingdom

Department of Social and Political Science, Bocconi University, Italy

ARTICLE INFO

Keywords:

Misinformation

Fake news

Health

Social media

ABSTRACT

Contemporary commentators describe the current period as “an era of fake news”in which misinformation,

generated intentionally or unintentionally, spreads rapidly. Although aﬀecting all areas of life, it poses particular

problems in the health arena, where it can delay or prevent eﬀective care, in some cases threatening the lives of

individuals. While examples of the rapid spread of misinformation date back to the earliest days of scientiﬁc

medicine, the internet, by allowing instantaneous communication and powerful ampliﬁcation has brought about

a quantum change. In democracies where ideas compete in the marketplace for attention, accurate scientiﬁc

information, which may be diﬃcult to comprehend and even dull, is easily crowded out by sensationalized news.

In order to uncover the current evidence and better understand the mechanism of misinformation spread, we

report a systematic review of the nature and potential drivers of health-related misinformation. We searched

PubMed, Cochrane, Web of Science, Scopus and Google databases to identify relevant methodological and

empirical articles published between 2012 and 2018. A total of 57 articles were included for full-text analysis.

Overall, we observe an increasing trend in published articles on health-related misinformation and the role of

social media in its propagation. The most extensively studied topics involving misinformation relate to vacci-

nation, Ebola and Zika Virus, although others, such as nutrition, cancer, ﬂuoridation of water and smoking also

featured. Studies adopted theoretical frameworks from psychology and network science, while co-citation

analysis revealed potential for greater collaboration across ﬁelds. Most studies employed content analysis, social

network analysis or experiments, drawing on disparate disciplinary paradigms. Future research should examine

susceptibility of diﬀerent sociodemographic groups to misinformation and understand the role of belief systems

on the intention to spread misinformation. Further interdisciplinary research is also warranted to identify ef-

fective and tailored interventions to counter the spread of health-related misinformation online.

1. Introduction

The spread of misinformation is not new, dating back at least to the

early days of printing. Even the term “fake news”, which has achieved

considerable contemporary prominence, was ﬁrst coined in 1925, when

an article in Harper's Magazine, entitled “Fake News and the Public”

mourned how newswires were allowing misinformation to disseminate

rapidly (McKernon, 1925). The growth of the Internet has, however,

initiated a fundamental change. In 2013, the World Economic Forum

warned that potential “digital wildﬁres”could cause the “viral spread”

of intentionally or unintentionally misleading information (World

Economic Forum, 2013). In the health arena, much concern has focused

on the spread of misinformation on immunisation, with social media

acting as a powerful catalyst for the ‘anti-vaxxer movement’.By

encouraging individuals not to vaccinate their children, this movement

has been linked to recent measles outbreaks in countries such as the UK,

the US, Germany and Italy (Datta et al., 2017;Filia et al., 2017). The

prevalence and persistence of such misinformation justiﬁes a careful

and systematic review of published literature on the nature and the

mechanisms by which misinformation spreads.

1.1. Deﬁning terminology: what is misinformation?

We ﬁrst review the distinctions between various terms that relate to

misinformation. Following the 2016 US presidential election, the term

“fake news”attracted substantial media and scholarly attention. The

term overlaps with other forms of misleading information, and espe-

cially misinformation and disinformation, all conveying messages,

https://doi.org/10.1016/j.socscimed.2019.112552

Received 21 January 2019; Received in revised form 29 August 2019; Accepted 12 September 2019

∗

Corresponding author. Department of Social and Political Science, Bocconi University, Via Guglielmö Röntgen 1, 20136 Milan, MI, Italy.

E-mail address: yuxi.wang@phd.unibocconi.it (Y. Wang).

Social Science & Medicine 240 (2019) 112552

Available online 18 September 2019

(http://creativecommons.org/licenses/BY-NC-ND/4.0/).

stories, theories, or opinions that spread rapidly through social contacts

or online media. They diﬀer primarily with respect to intent and mode

of spread. Misinformation involves information that is inadvertently

false and is shared without intent to cause harm, while disinformation

involves false information knowingly being created and shared to cause

harm (Wardle and Derakhshan, 2017). Although “fake news”is the

term that received most popular attention, it is arguably the most

problematic one in terms of deﬁnitional rigour. Lazer et al. (2018)

described it as fabricated information that mimics news media content,

but this does not capture the complexity of the phenomenon, which can

include both satire and information created deliberately to mislead as a

means to achieve a political or other goal (Wardle, 2017). A recent

report by a parliamentary committee in the UK concluded that “The

term ‘fake news’is bandied around with no clear idea of what it means,

or agreed deﬁnition (House of Commons, 2019). The term has taken on

a variety of meanings, including a description of any statement that is

not liked or agreed with by the reader. We recommend that the Gov-

ernment rejects the term ‘fake news’, and instead puts forward an

agreed deﬁnition of the words ‘misinformation’and ‘disinformation’”.

Since the phrase also has been politicized by powerful ﬁgures to dis-

credit certain news media (Vosoughi et al., 2018), we refrain from using

the term “fake news”throughout the paper.

While noting these distinctions, in practice it often seems diﬃcult to

diﬀerentiate these categories because of the problem in ascertaining

intent. For example, anti-vaccine propaganda may be spread by those

who have a genuine concern, however misguided, about safety, and by

those who are using the issue as a tool to undermine trust in particular

governments. Thus, unless the intent is clear, we use the term mis-

information as an umbrella term to include all forms of false information

related to health, thereby giving those generating it the beneﬁt of the

doubt.

1.2. Misinformation spread –from micro-to macro-level

Before discussing the macro-phenomenon of misinformation spread,

we ﬁrst conceptualize the potential mechanism following Wardle and

Derakhshan (2017). Three major components are involved in the

creation, production, distribution and re-production of misinformation

–agent, message and interpreter (Wardle and Derakhshan, 2017). Our

review will look at whether and how existing literature from diﬀerent

disciplines examine the type of actor behind the creation of health-re-

lated messages on social media platforms, the descriptive features of the

message –the durability and distribution of accurate and misleading

information - and most importantly, the interpreter's response and how

it contributes to the reproduction of misinformation. At the micro-level,

individuals who receive misinformation form judgement about the

believability of the message, depending on information source, narra-

tive and context, while the tendency to spread depends on the degree to

which receivers suspect such misinformation (Karlova and Fisher,

2013). At the macro-level, we observe patterns of misinformation cas-

cade and characteristics of networks.

Early literature on spread of rumours (circulating stories or reports

of uncertain or doubtful truth) identiﬁed the “basic law of rumour”–

the amount of rumour in circulation will vary with the importance of

the subject to the individuals concerned times the ambiguity of the

evidence pertaining to the topic in question (Allport and Postman,

1947). The link between psychological and cultural dimensions gen-

erated intriguing questions on what makes misinformation so easy to

spread and so hard to debunk.

According to Allport and Postman (1947), the ambiguity of the

message may be due to the receipt of conﬂicting stories, with no one

more credible than another. The concept of credibility, as investigated

extensively in communications research, encompasses message cred-

ibility, source credibility, and media credibility (Metzger et al., 2003).

With traditional media, each aspect of information credibility is rela-

tively well understood, although even there some caution is needed. In

contrast, with social media, it is particularly challenging to assess the

source credibility, as users themselves are the self-publisher, subject to

no form of factual veriﬁcation or accountability. We do know that

people regard information from the internet as being as credible as

conventional media such as television and radio, but not as that from

newspapers (Johnson and Kaye, 1998;Kim and Johnson, 2009). Many

studies have thus analysed the credibility of user-generated contents

and the cognitive process involved in the decision to spread online

information on social and political events (Abbasi and Liu, 2013;

Castillo et al., 2011;Lupia, 2013;Swire et al., 2017). This research has

highlighted the importance of source credibility and persuasiveness as

factors aﬀecting the susceptibility of users to the messages conveyed.

Other relevant studies have focused on important concepts such as

misperception and conﬁrmation bias, whereby people's views on factual

matters are strongly inﬂuenced by prior beliefs (Taber and Lodge, 2006;

Nyhan and Reiﬂer, 2010;Jerit and Barabas, 2012); polarization within

networks (Lewandowsky et al., 2012); and the combined eﬀects of these

phenomenon facilitated by social media (Del Vicario et al., 2016;

Boutyline and Willer, 2017;Shao et al., 2018). While much of the ex-

isting literature has examined social and political issues, we focus on

misinformation related to health and wellbeing.

1.3. Misinformation and health: gaps in the evidence base

There is limited understanding of why certain individuals, societies

and institutions are more vulnerable to misinformation about health.

This is perhaps surprising, as health promotion and public health re-

searchers now pay considerable attention to the potential of the in-

ternet as a tool to diﬀuse health-related information (Chew and

Eysenbach, 2010;Ritterband and Tate, 2009;Murray et al., 2009;

Scanfeld et al., 2010;Signorini et al., 2011), employing smart phones

and other mobile technologies in preventative interventions (Abroms

et al., 2013;Eng and Lee, 2013;Free et al., 2013;Steinhubl et al.,

2015). Although the internet provides immense opportunities, it also

lowers the cost of generating and disseminating information, allowing

misinformation and sensationalized stories to propagate. What was

once spread locally can rapidly become global, with ideas no longer

conﬁned or delayed by geography. This has generated a series of studies

of information diﬀusion (Serrano et al., 2015), rumour spread (He

et al., 2015), and consequent behavioural changes (Salathé and

Khandelwal, 2011;Wakamiya et al., 2016). These generally employ

sophisticated modelling and simulation techniques to identify the ru-

mour propagation dynamics. However, this is still in its infancy and one

recent systematic review of behavioural change models found that most

papers investigating spread of health-related information and beha-

vioural changes are theoretical, failing to use real-life social media data

(Verelst et al., 2016). The literature on misinformation spread is

growing, but spans disparate disciplines, including communication

studies, epidemiology, psychology, and computational science. We

contend that it is now necessary to integrate the diﬀerent perspective

and methodologies, to understand the characteristics of susceptible

populations and to devise interventions that are most eﬀective in

countering this spread.

To address this gap and provide a comprehensive view on the

available evidence, we undertake what is, to our knowledge, the ﬁrst

systematic review of studies that investigated the health-related mis-

information content on social media and how it spreads online. We

include papers stemming from diﬀerent disciplines and we analyse

them on diﬀerent dimensions.

First, we identify the main health-related topics where mis-

information tends to spread and the descriptive features of mis-

information. By focusing on the content and the spread of diﬀerent

health-related misinformation, we reveal a broad landscape of issues

that attract actors to espouse misleading claims. The ﬁndings shed light

on the extent to which diﬀerent topics are identiﬁed and investigated in

the literature. This approach can inform those working in these areas.

Y. Wang, et al. Social Science & Medicine 240 (2019) 112552

This seeks to inform social scientists, psychologists, and experts in

other ﬁelds working to understand this issue, who may otherwise

overlook the range of theories that underpin the work of researchers

seeking to conceptualize the spread of misinformation. As this is a

phenomenon that can be examined from many diﬀerent perspectives,

we have undertaken a co-citation analysis to assess the extent to which

diﬀerent disciplinary paradigms are informing each other, thereby fa-

cilitating future interdisciplinary research that can contribute to a more

inclusive theoretical framework.

We then explore the existing theories used to explain the phenom-

enon and undertake a co-citation analysis to ascertain the extent to

which ideas spread among disciplinary communities.

We further discuss the diﬀerent empirical strategies adopted in the

analysis. In doing so, we identify the social media platforms where the

authors obtain the empirical data, how they incorporate diﬀerent sta-

tistical models to interpret the data, and the empirical progress in our

understanding of the mechanism. We conclude by examining the po-

tential for future interdisciplinary research and practical interventions

to counter misinformation spread.

2. Methods

2.1. Design and search strategy

Our reporting strategy follows the PRISMA guidelines (Moher et al.,

2009). We searched PubMed, Cochrane, Web of Science (WoS) and

Scopus for records published between January 2012 and November

2018, using the following search terms in title and abstract:

(i) [misinformation OR fake news OR disinformation OR rumo* OR

false OR mislead*]

AND

(ii) [online OR social OR media OR news OR twitter OR Facebook OR

google]

AND

(iii) [spread OR propagate* OR disseminat* OR circulat* OR commu-

nicat* OR diﬀuse OR broadcast]

AND

(iv) [health OR disease OR infectious OR virus OR vaccin* OR Ebola

OR Zika OR measles]

This yielded 206 records from PubMed, 33 records from Cochrane,

341 records from Web of Science, 51 records from Scopus and 62 re-

cords from Google (Fig. 1.). We identiﬁed and removed duplicates,

which resulted in 651 records that were ﬁrst screened based on title,

abstract, and keywords and then using full-text where necessary. All

eligible references were uploaded into reference management software

(Mendeley) for assessment of eligibility.

2.2. Screening and eligibility assessment

Next, we screened the results of the 651 records based on title and

abstract. Articles that were not original, not involving social media, not

related to health, not in English and not on human subjects were ex-

cluded. At this, and the subsequent stage, we also excluded the very

extensive literature on individual cognitive biases, which would be well

beyond the scope of a single review. Similarly, we excluded research on

static group decision-making, which can create misinformation (e.g. the

phenomenon termed groupthink), that subsequently spreads.

This left 131 potentially eligible papers, which were subject to full-

text analysis, applying the following pre-speciﬁed eligibility criteria:

Misinformation. Only records that concern misinformation, disin-

formation, fake news, rumour or any form of information disorder were

included.

Social media. Misinformation had to be propagated through online

media.

Health. Only records related to disease, treatments, public health

and wellbeing were included.

Model or empirical. Modelling (e.g. epidemiological, rumour spread)

studies or empirical analysis of the distribution or the dynamic eﬀect of

misinformation.

Humans. We are interested in humans and behaviour of humans,

and therefore excluded studies about animals and plants.

Original research. We excluded review articles and editorials.

Language. We excluded articles written in languages other than

English.

Finally, we excluded papers that lacked analytic rigour or did not

incorporate misinformation as the main component of the analysis,

which resulted in 57 articles. The PRISMA (Fig. 1)shows the results of

these exclusions.

2.3. Data extraction

For the 57 included studies, we analysed the following elements in

the full-text: (i) health-related issues and ﬁndings; (ii) theoretical fra-

mework (if any) and disciplines; (iii) study design.

2.4. Co-citation analysis

To gain further insights on the disciplines contributing to this in-

creasing area of research, we conducted a co-citation analysis of eligible

articles to measure the frequency with which two sources are cited

together by other documents. Co-citation analysis yields insight into

potential disciplinary siloes and theoretical or methodological gaps in

the literature. This was possible with 121 of the papers because 10

articles were not indexed on Scopus, where we extracted citation data

from.

3. Results

Fig. 2 shows the number of potentially eligible articles by year. Not

surprisingly, the number of studies that investigated health-related

misinformation increased over the years, from 7 in 2012 to 41 in 2018

(November) with a sharp rise in 2017. The trend implied the growing

scholarly interest in the social phenomenon, potentially ampliﬁed by

major political events in 2016. We exclude certain articles (n = 74) due

to their lack of analysis or interpretation of misinformation as men-

tioned above, and the remainder of this result section relates only to the

57 remaining papers after full-text analysis.

Key features of the studies included are in the web appendix. We

ﬁrst investigated what health-related topics have been studied in rela-

tion to misinformation. The largest category relates to communicable

diseases (n = 30), including vaccination in general (8) and speciﬁcally

against Human Papilloma Virus (HPV), Measles, Mumps and Rubella

(MMR) and inﬂuenza (3, 2 and 1 respectively), as well as infections

with Zika virus (9), Ebola (4), inﬂuenza (1), Middle East Respiratory

Syndrome (1) and Nile Virus (1). Many articles concern chronic non-

communicable diseases such as cancer (3), cardiovascular disease (3),

psoriasis (1) and bowel disease (1). Some also address issues of diet and

nutrition (3), smoking (3) and water safety or quality (2). Five studies

cover a broad range of health-related misinformation or rumour online,

while the remaining studies were placed in a miscellaneous category,

addressing other speciﬁc diseases, health problems or medical inter-

ventions (Fig. 3). We now brieﬂy describe each of these in turn.

Y. Wang, et al. Social Science & Medicine 240 (2019) 112552

Fig. 1. PRISMA ﬂow diagram.

Fig. 2. Numbers of potentially eligible articles.

Y. Wang, et al. Social Science & Medicine 240 (2019) 112552

3.1. Health-related issues and ﬁndings

3.1.1. Vaccines and communicable diseases

Vaccine uptake, especially in children, has ﬂuctuated in recent

decades in many developed countries, with marked declines during

certain periods. In 2012, the journal Vaccine devoted a special issue to

“The Role of Internet Use in Vaccination”, analysing some of the

communication strategies used by both the anti-vaccination movement

and public health professionals. Authors recommended comprehensive,

structured, and easily understandable responses to anti-vaccination

messages (Betsch and Sachse, 2012;Kata, 2012;Reyna, 2012;

Nicholson and Leask, 2012). Although refusal of vaccination and

movements opposing vaccines date back to the time of Jenner, pub-

lication of fraudulent research linking the MMR vaccine to autism and

bowel disease (Wakeﬁeld et al., 1998) was a seminal moment. The

concerns raised then, although long since discredited, have been widely

disseminated on social media and even now are highly inﬂuential

among some groups. For instance, Basch et al. (2017),Donzelli et al.

(2018) and Porat et al. (2018) report high online prevalence and po-

pularity of autism-related discussions in fora on vaccination. Tustin

et al. (2018) and Xu and Guo (2018) also reported widespread mis-

information about side eﬀects, as well as mistrust in government or

pharmaceutical companies in discussions on vaccination. Krishna's

(2017) study of active propagators of these messages found that those

who were knowledge-deﬁcient and vaccine-averse exhibit higher levels

of activity than those who are not. Aquino et al. (2017) reported a

signiﬁcant inverse correlation between MMR vaccination coverage and

online searches and social network activity on “autism and MMR vac-

cine”. Taken as a whole, the research identiﬁes anti-vaxxer and mem-

bers of online communities favouring conspiracy theories as sources or

propagators of misinformation, with discussions tending to revolve

around rhetorical and personal arguments that induce negative emo-

tions (fear, anger, sadness). Although there is less misinformation than

accurate information, the former has greater popularity among viewers.

The Zika epidemic stimulated considerable activity on Twitter

(Wood, 2018) and Facebook (Sharma et al., 2017), as well as spread of

news items (Sommariva et al., 2018), images (Seltzer et al., 2017), and

videos (Bora et al., 2018) on a range of media. Conspiracy theories

directed at institutions feature frequently in these discussions. For in-

stance, the Zika virus was portrayed as a bioweapon, while rumours

spread that the Zika vaccine had been developed to depopulate the

earth (Sommariva et al., 2018;Wood, 2018). Conspiracist ideation

played a crucial role in one's belief in misinformation (Lewandowsky

et al., 2013). However, Bode and Vraga (2018) did not ﬁnd that belief

in conspiracies reduced receptiveness to correction of misinformation

on Zika virus, although this research generated several important in-

sights for design of interventions to address this issue.

The Ebola outbreak also provided much additional material. For

instance, Fung et al. (2016) examined the role of Twitter and Sina

Weibo (Chinese microblog, equivalent to Twitter) in spreading rumours

and speculating on treatments. Pathak et al. (2015) found numerous

misleading videos online concerning Ebola virus disease. Similar to the

studies on vaccination, much of this misinformation comes from in-

dividuals who are highly active in inﬂuencing opinions, and rumours

often garner higher popularity than evidence-based information.

3.1.2. Chronic non-communicable diseases

Though most research on misinformation has focused on infectious

disease, misinformation on chronic illnesses such as cancer and cardi-

ovascular disease are not uncommon on social media. Okuhara et al.

(2017) looked at online discussions with opposing views on cancer

screening in Japan, ﬁnding that most propagated anti-cancer screening

messages. Staying in Asia, Chen et al. (2018a,b) examined the nature

and diﬀusion of misinformation on gynaecologic cancer in China. Chua

and Banerjee (2018) found that individuals are more likely to trust and

share cancer-related rumours if the rumours are dreadful rather than

wishful, and if one has had previous personal experience.

Studies on other chronic diseases mostly speculate on or promote

alternative treatments, for example on diabetes (Leong et al., 2017),

heart failure (Chen et al., 2013), hypertension (Kumar et al., 2014) and

psoriasis (Qi et al., 2016). Again, misleading videos are more inﬂuen-

tial. In addition, research by Leong et al. (2017) in India found that

diabetes videos tailored to South Asians were more misleading than

those not culturally-targeted.

3.1.3. Others

Unsubstantiated messages regarding diets and nutrition can have

detrimental eﬀects on susceptible individuals. For instance, Syed-Abdul

et al. (2013) investigated how anorexia is promoted as fashion and

linked to ideas of beauty in YouTube videos, gaining high popularity

among young female viewers. Bessi et al. (2015), analysing the diﬀu-

sion of diet, environment and geopolitics-related misinformation, found

that active users are more likely to span a range of categories, and that

online groups promoting conspiracy theories tend to exhibit

Fig. 3. Topic categories.

Y. Wang, et al. Social Science & Medicine 240 (2019) 112552

polarization. Similar patterns are observed in discussions on water

ﬂuoridation, as memorably invoked in the 1964 movie Dr. Strangelove.

Seymour et al. (2015) analysed the anti-ﬂuoride network online and

found that strong ties among the community are obstacles for expert

opinions to be accepted. This indicates that social homogeneity may

well be the primary driver of content diﬀusion and clustering. The

modelling of rumour spread is therefore informative of the cascades'

size and potential intervention designs in countering such spread.

The tobacco industry has a long history of distorting scientiﬁc evi-

dence and misleading consumers. Very recently, Albarracin et al.

(2018) showed how misleading portrayal of tobacco's health con-

sequences introduces positivity towards smoking. The advent of elec-

tronic cigarettes prompted Harris et al. (2014) to examine content and

tweet patterns related to an e-cigarette campaign by a local public

health department. The misinformation included arguments that divert

attention from the products to messages that sought to discredit au-

thorities.

A few studies have investigated speciﬁcally the psychology of in-

dividuals who believe and share rumours. Chua and Banerjee (2017),in

their analysis on epistemic belief and its eﬀect on the decision to share

rumour, showed that epistemologically naïve users have higher pro-

pensity to share online health rumours. Li and Sakamoto (2015) dis-

covered that exposing individuals to measures of collective opinion,

through counts of retweets and collective truthfulness ratings could

reduce the tendency to share inaccurate health-related messages. Taken

as a whole, the evidence indicates that the motivation to believe and

share rumours reﬂects both individual and collective makings, but the

consequences are diﬃcult to predict because of the complex psycho-

logical factors involved.

Finally, the group of miscellaneous studies mainly examined spe-

ciﬁc medical interventions or issues such as drugs (Al Khaja et al.,

2018), paediatric disease (Strychowsky et al., 2013), abortion (Bryant

et al., 2014), dialysis (Garg et al., 2015), suicide (Li et al., 2018) and

multiple sclerosis (Lavorgna et al., 2018). The common sources of

misinformation included advertisements or comments related to ad-

vertisements (Garg et al., 2015) and patients' anecdotal experiences

(Strychowsky et al., 2013). Again, misinformation was more popular

than factual messages.

3.2. Theoretical frameworks and disciplines (co-citation analysis)

We next investigated the theoretical foundations in the included

studies, but it rapidly became clear that there was no widely agreed

approach to this phenomenon, reﬂecting the broad range of disciplines

that have investigated it. The more dominant disciplines and research

areas according to the published journals include public health, health

policy and epidemiology (n = 14), health informatics (n = 8), com-

munications studies (n = 5), vaccines (=4), cyberpsychology (n = 3)

and system sciences (n = 3).

Disciplinary approaches adopted to conceptualize the phenomenon

are varied, but primarily fall within the ﬁelds of psychology (n = 8) and

communication (n = 4), as well as network science (n = 7). While

theories in psychology focus on individual-level cognitive response to

misinformation and its corrections, frameworks in network and data

science characterise the (online) societal mechanisms involved. For

instance, Chua and Banerjee (2018), in investigating the online beha-

viour in the face of health rumours, invoked the seminal rumour theory

(Allport and Postman, 1947), which views personal involvement as a

common perception that dictates one's decision to spread rumour.

Moreover, rumours that are repeatedly circulated can be reinforced and

accepted as credible (Rosnow, 1991), and the consequent perceived

high credibility can in turn increase intention to trust and share ru-

mours (Shin et al., 2017). This relates to credibility research, which

suggests that perceived credibility and can heighten the persuasive

impact, especially for internet users who are not motivated to process

information (Metzger, 2007;Metzger et al., 2010). Similarly, Ozturk

et al. (2015) explored how diﬀerent social media settings can reduce

rumour spread based on rumour psychology research. Others have re-

ferred to psychological studies around conspiracist ideation, inocula-

tion theory and social conformity in understanding the mechanism

behind health misperception on social media (Bode and Vraga, 2018;

Bora et al., 2018;Li and Sakamoto, 2015). Contrastingly, the use of

system or network theories are aimed at explaining the patterns of

social inﬂuence, social learning, social contagion and homophily and

polarization processes (Bessi et al., 2015;Radzikowski et al., 2016;

Schmidt et al., 2018;Sicilia et al., 2017;Wood, 2018). The framework

typically assists the subsequent social network analysis.

Two studies borrowed insights from philosophy –Grant et al. (2015)

employed the rhetorical framework to examine the persuasive features

of pro- and anti-vaccine sites, while Chua and Banerjee (2017) used the

epistemology framework to explore the role of epistemic belief in af-

fecting rumour-sharing behaviour. Finally, situational theory of publics

(Grunig, 1997) from public relation studies are adopted to identify

vaccine-negative activists (Krishna, 2017). The remaining articles from

computational studies and clinical perspectives lack any theoretical

underpinning and are purely empirical.

Given that the ﬁndings are from disparate disciplines, we conduct

the co-citation analysis on all the potentially eligible articles to identify

the clusters of disciplinary communities. In co-citation network ana-

lysis, the unit of analysis is the cited source, and we include the journals

cited at least 5 times within the 121 articles. As seen in Fig. 4, the

distance in the map between any pair of journals reﬂects their similarity

to each other (van Eck and Waltman, 2010), and we use the LinLog/

modularity normalization technique to minimize the distance between

connected nodes (Noack, 2009). The size of the nodes represents the

number of citations, and the line indicates the presence of citation in

either direction. The analysis identiﬁed 4 distinct (inter-)disciplinary

clusters, which we assigned as follows (with randomly generated col-

ours, from left to right): Social Psychology and Communications (red),

General Science and Medicine (blue), Infectious Disease/Vaccine and

Public Health (green), Medical Internet and Biomedical Science

(purple). Overall, the literature is concentrated in general science and

vaccines/infectious diseases. Psychology and communications litera-

ture sit on the periphery, with relatively less cross-citation with the

science and medicine literature. Interestingly, we also observe a few

sociology journals at the bordering regions between clusters, implying

their incipient roles in acknowledging diﬀerent insights across dis-

ciplines. There is potential for greater interdisciplinary collaboration.

3.3. Study design

Turning to research design, most studies employed content analysis

(n = 38) either alone or as a component of the analysis, studying var-

ious forms of social media (n = 10), YouTube videos (n = 12), Twitter

or equivalents (n = 8), websites (n = 5), images (n = 1) or mobile

messengers (n = 2). Authors observe the distribution of useful and

misleading information, and the pattern of consumption by diﬀerent

users. Some studies incorporated social network analysis or epidemio-

logical modelling to better explain the dynamics of misinformation

spread (Bessi et al., 2015;Ghenai and Mejova, 2017;Harris et al., 2014;

Jin et al., 2014;Radzikowski et al., 2016;Wood, 2018). Many designs

were also complemented by sentiment measures, for instance, the “anti-

vaccine”sentiment (Bahk et al., 2016;Xu and Guo, 2018).

Seven studies used experimental designs. Bode and Vraga, in three

diﬀerent papers, manipulated Facebook's “related news”function to

conﬁrm or correct (or both) misinformation about the purported link

between vaccines and autism, as well as unfounded link between ge-

netically modiﬁed organisms (GMO) and health (Bode and Vraga, 2015;

Vraga and Bode, 2017). They also simulated Twitter feeds with false

information about Zika virus to evaluate the ability of corrective re-

sponses to reduce misperception (Vraga and Bode, 2017). Chua and

Banerjee (2017,2018) undertook web-based experiments with

Y. Wang, et al. Social Science & Medicine 240 (2019) 112552

participants exposed to combinations of rumours and counter-rumours.

Ozturk et al. (2015) explored diﬀerent ways to reduce rumour spread

on Twitter using Amazon's Mechanical Turk, an online crowdsourcing

platform. Albarracin et al. (2018) used the same platform to evaluate

the eﬀects of YouTube videos on viewer attitudes to tobacco products.

A few studies used survey instruments to understand how social

media can spread misconceptions about Ebola in West Africa

(Adebimpe et al., 2015) and inﬂammatory bowel disease in the USA

(Groshek et al., 2017), and to explore the relationship between

knowledge deﬁciency and negative attitudes towards vaccines (Krishna,

2017). One case-study adopted an anthropological approach and used

thick description to review the rhetorical features of both pro-vaccine

and vaccine-sceptical websites Grant et al. (2015).

4. Discussion

4.1. Findings

We found that, while there have been studies of the spread of

misinformation on a wide range of topics, the literature is dominated by

those of infectious disease, including vaccines. Overall, existing re-

search ﬁnds that misinformation is abundant on the internet and is

often more popular than accurate information. Several of the studies

address areas where state action challenges individual autonomy. The

classic example is vaccination, where eﬀective protection of the popu-

lation requires achievement of levels of uptake suﬃcient to achieve

herd immunity. This review conﬁrms that misconceptions about MMR

vaccine and autism, in particular, remain prevalent on social media

(Aquino et al., 2017;Chen et al., 2018a,b). Other topics share scientiﬁc

uncertainty, with the authorities unable to provide conﬁdent explana-

tions or advice, as with newly emerging virus infections such as Ebola

and Zika viruses (Basch et al., 2017;Fung et al., 2016;Sommariva et al.,

2018).

The agents that create misinformation are mostly individuals with

no oﬃcial or institutional aﬃliations. This relates to our initial dis-

cussions on credibility –what makes a source trustworthy for readers?

Formal institutions are increasingly challenged by the rise of, for

instance, “expert patient”, blurring the boundaries between authority

and quasi-proﬁciency (Seymour et al., 2015). Traditional vertical

health communication strategies are eroded by horizontal diﬀusion of

conspiracy-like messages. The narratives of misinformation are domi-

nated by personal, negative and opinionated tones, which often induce

fear, anxiety and mistrust in institutions (Bessi et al., 2015;Panatto

et al., 2018;Porat et al., 2018). When people are frightened and

doubtful, they can be more susceptible to misinformation. Once false

information gains acceptance in such circumstances, it is diﬃcult to

correct, and the eﬀectiveness of interventions vary according to each

individual's personal involvement, literacy and socio-demographic

characteristics, features that tend to be under-explored in existing re-

search.

The included articles adopted disparate theoretical approaches in

conceptualizing the phenomenon, with the dominant frameworks from

the ﬁelds of psychology and network science. Theories employed in

psychology aimed to explain individual-level cognitive response of

misinformation and rumour online (Bode and Vraga, 2018;Bora et al.,

2018;Chua and Banerjee, 2018;Li and Sakamoto, 2015;Ozturk et al.,

2015), whereas network theories focus on the social mechanism and

patterns of misinformation spread (Bessi et al., 2015;Radzikowski

et al., 2016;Schmidt et al., 2018;Sicilia et al., 2017;Wood, 2018).

Further co-citation analysis on all articles that investigated the phe-

nomenon revealed that the disciplinary landscape concentrates around

general science and vaccines/infectious disease, while psychology and

communication studies have less cross-citation with the science and

medicine literature. The sociology discipline has great potential to

bridge the diﬀerent communities.

Researchers have employed increasingly sophisticated analytic

techniques for empirical analysis, such as the use of social media data

for sentiment analysis. The majority of the articles included a content

analysis of the information on social media, ranging from text, images

and videos. Several studies employed complexity and network theories

to model the dynamics of rumour spread and opinion polarization

(Bessi et al., 2016;Jin et al., 2014). Other studies have adopted psy-

chological and linguistic perspectives (Fung et al., 2016;Li et al., 2018;

Waszak et al., 2018). While we have excluded research on both

Fig. 4. Co-citation analysis. We extracted citation data from Scopus and analysed citation patterns using network-clustering algorithms in VOSviewer 1.6.8. The

network map shows co-citation patterns of 121 journals cited at least 5 times within the studies that are potentially eligible. The node size represents the number of

citations, and the lines represent the presence of citation in either direction. We restricted the minimum cluster size to 20, which resulted in 4 disciplinary clusters

and 2367 links. We were not able to identify 10 articles because they were not indexed on Scopus, we therefore exclude them for the co-citation analysis.

Y. Wang, et al. Social Science & Medicine 240 (2019) 112552

individual and group biases, we feel it is important to note how several

studies invoked the concept of conﬁrmation bias, concluding that it plays

an important role in creating online echo-chambers (Bessi et al., 2015;

Donzelli et al., 2018). This highlights the need for much more research

on the socio-psychological characteristics of those who believe and

propagate misinformation. In particular, there is a need to understand

better the roles of both ideology and belief systems (Jost et al., 2018)

and what might be termed “lazy thinking”(Pennycook and Rand,

2018). For instance, although the role of literacy and cues to credibility

are critical concepts in the design of experiments, they should also be

explored in empirical studies, and especially those that use big data

from social media platforms.

4.2. Gaps and potential for future research

Although sociology and psychology pioneered research to under-

stand rumour (Allport and Postman, 1947;Bartlett, 1932;Kirkpatrick,

1932), psychologists are only beginning to study the implications of the

explosion in internet use (Stone and Wang, 2018). While we conclude

from the co-citation analysis that studies on misinformation in health

cover a wide range of disciplines, there is a marked lack of inter-

disciplinary research. This could, for example, allow hypotheses to be

generated by social scientists using rumour theory and tested using

quantitative analysis of social media data.

While most of the studies recommended courses of action based on

their results, only a handful of papers proposed speciﬁc and tested in-

terventions to reduce misinformation spread. For instance, Ozturk et al.

(2015) discovered that rumour-countering warnings such as “this tweet

may contain misinformation”did decrease participants' likelihood of

sharing a rumour, consistent with ﬁndings in the psychological litera-

ture (Bordia and Difonzo, 2004). Bode and Vraga (2018) showed that

algorithmic correction (by a platform) and social correction (by peer)

are equally eﬀective in correcting misinformation and call for cam-

paigns to encourage users to refuse false or misleading information. The

same authors have shown how expert organization can correct mis-

information without damaging its credibility, presenting an appealing

intervention to reduce misinformation spread (Vraga and Bode, 2017).

Finally, there is a need to characterise the scale and nature of the

phenomenon much better, for example with studies of which socio-

demographic characteristics make social media users more susceptible

to and therefore likely to share health-related misinformation.

4.3. Limitations

Before concluding, we will note several limitations of the systematic

review. First, although we have attempted to deﬁne the phenomenon

we are studying, our search strategy may not capture the terminology

used by others. This is not just a problem of language. There are many

related phenomena, such as denialism, groupthink, fearmongering, and

equivalents in other languages, such as Lügenpresse (lying press) in

German and it is possible that these or others may be used, in some

circumstances, to describe some elements of what we are studying.

Second, even when we agree the terms, such as misinformation and

‘fake news’, the meanings adopted by authors can vary. Third, as noted

at the outset, it is very diﬃcult to ascertain the motives of those

spreading particular rumours and myths, leaving us unable to answer

the old question “mad or bad?”. Fourth, while our focus has been on

messages concerning health-related issues, misinformation about other

issues can have health consequences. For instance, a man from North

Carolina travelled to Washington in 2016 and opened ﬁre at a pizzeria

following the spread of what became termed the Pizzagate theory,

whereby it was alleged that the pizzeria was the site of a paedophile

ring organised by Democratic Party leaders. Even though

comprehensively debunked, subsequent polls showed that this allega-

tion was still widely believed. Finally, since we excluded articles that

are not published in English, we may have omitted relevant papers

published in other languages.

5. Conclusion

Social media platforms, although providing immense opportunities

for people to engage with each other in ways that are beneﬁcial, also

allow misinformation to ﬂourish. Without ﬁltering or fact-checking,

these online platforms enable communities of denialists to thrive, for

instance by feeding into each other's feelings of persecution by a corrupt

elite (McKee and Diethelm, 2010). The accumulation of individual

beliefs in these unfounded stories, conspiracy theories, and pseu-

doscience can give rise to social movements, such as the anti-vaccina-

tion movement, with profound consequences for public health. This is

further exacerbated by the fact that it is politically incorrect to question

or criticize the belief of others, and the ﬁght for truth is nevertheless

against the ﬂow of true believers armed with ignorance and mis-

information (Kaufman et al., 2018).

We have shown that academic literature on this social phenomenon

mainly revolves around vaccination and infectious disease, drawing on

various disciplines, frameworks and empirical methods. Among the

articles examined, there is broad consensus that misinformation is

highly prevalent on social media and tends to be more popular than

accurate information, while its narrative often induces fear, anxiety and

mistrust in institutions. The severity and the deleterious eﬀects it may

pose on the society is hardly quantiﬁable, but evidence abounds that we

need more research on the identiﬁcation of susceptible populations, and

on the understanding of socio-demographic and ideological asymme-

tries in the intention to spread misinformation.

Finally, since the persistence of misinformation owes both to the

psychological responses and to the social contexts under which mis-

information spread, potential interventions should target both fronts. At

the individual level, although interventions to correct misperceptions

are proven eﬀective at times, eﬀorts to retract misinformation need to

be carried out with caution in order to prevent backﬁring. This requires

profound understanding on how epistemic and ideology beliefs act as

obstacles to accepting scientiﬁc evidence. A more constructive ap-

proach may be to cultivate critical thinking and to improve health and

media literacy, thereby equipping individuals with the faculty to criti-

cally assess the credibility of information. At the system level, how we

can amend our information ecosystem to reduce selective exposure and

opinion polarization is not a challenge for academics and policy-makers

alone to face. We therefore hope that our review can stimulate social

scientists, psychologists, computer scientist and medical professionals

to not only collaborate with each other, but also engage with industries

and internet consumers to understand and counter the eﬀects of this

increasingly important social phenomenon.

Authors' contributions

YW collected the data, performed the review and drafted the

manuscript. All authors contributed to the interpretation, writing and

editing of the manuscript.

Acknowledgements

YW is funded by the EU’s Research and Innovation Horizon 2020

Framework under grant agreement 721402.

DS is funded by a Wellcome Trust Investigator Award and ERC

HRES 313590.

Y. Wang, et al. Social Science & Medicine 240 (2019) 112552

Appendix Table

Characteristics of Included Studies

Health Issue (by category) Setting/Geographic Focus Theory/Framework Study Design Platforms Authors

Communicable Disease

Ebola Nigeria N/A Survey Facebook, Twitter Adebimpe

et al. (2015)

Ebola Chinese and English-speaking

Countries

N/A Content Analysis (Social

Media)

Sina Weibo, Twitter Fung et al.

(2016)

Ebola US Epidemiological Models Content Analysis (Twitter);

Epidemiological Modelling

Twitter Jin et al.

(2014)

Ebola English-speaking Countries N/A Content Analysis (Video) YouTube Pathak et al.

(2015)

Inﬂuenza China Rumour Theory

(Psychology)

Content Analysis (Social

Media)

Sina Weibo, Tencent Weibo Chen et al.

(2018a,b)

Middle East Respiratory S-

yndrome

South Korea N/A Social Network Analysis Twitter, Blogs, Online Community Text,

Discussion Board Text, News sites

Song et al.

(2017)

Nile Virus Infection English-speaking Countries N/A Content Analysis (Video) YouTube Dubey et al.

(2014)

Vaccination (general) English-speaking Countries N/A Content Analysis (Video) YouTube Basch et al.

(2017)

Vaccination (general) Italy N/A Content Analysis (Video) YouTube Donzelli et al.

(2018)

Vaccination (general) US Situational Theory of

Publics (Public

Relations)

Survey Websites Krishna

(2017)

Vaccination (general) Spain N/A Content Analysis (Twitter) Twitter Porat et al.

(2018)

Vaccination (general) English-speaking Countries Network Theory Social Network Analysis Facebook Schmidt et al.

(2018)

Vaccination (general) Canada N/A Content Analysis (Facebook) Facebook Instagram Twitter YouTube Tustin et al.

(2018)

Vaccination (general) English-speaking Countries N/A Sentiment Analysis Google Xu and Guo

(2018)

Vaccination (general) +

GMOs

US Psychological Theory Experiment Facebook Bode and

Vraga (2015)

Vaccination (HPV) US Theory of Rhetorical

Situation (Philosophy)

Qualitative Case Study

(Anthropology)

Websites Grant et al.

(2015)

Vaccination (HPV) US N/A Content Analysis (Social

Media)

Twitter Mahoney

et al. (2015)

Vaccination (inﬂuenza) Italy N/A Content Analysis (Website) Websites, Blogs Panatto et al.

(2018)

Vaccination (MMR) Italy N/A Content Analysis (Social

Media)

Google Trend, Twitter, Facebook; National

Institute of Health (Istituto Superiore di

Sanità)

Aquino et al.

(2017)

Vaccination (MMR) US Network Theory Content Analysis (Twitter);

Social Network Analysis

Twitter Radzikowski

et al. (2016)

Vaccination (Polio and H-

PV)

Pakistan, US N/A Sentiment Analysis Mainstream Media, Twitter, Vaccine

Sentimeter

Bahk et al.

(2016)

Zika Virus US Psychological Theory Experiment Facebook, Twitter Bode and

Vraga (2018)

Zika virus English-speaking Countries Inoculation Theory

(Psychology)

Content Analysis (Video) YouTube Bora et al.

(2018)

Zika Virus Global N/A Content Analysis (Twitter);

Social Network Analysis

Twitter Ghenai and

Mejova

(2017)

Zika Virus Global N/A Content Analysis (Image) Instagram ﬂicker Seltzer et al.

(2017)

Zika Virus US N/A Content Analysis (Social

Media)

Facebook Sharma et al.

(2017)

Zika Virus English-speaking Countries Network Theory Social Network Analysis Twitter Sicilia et al.

(2017)

Zika Virus English-speaking Countries N/A Content Analysis (Weibo) Facebook, Twitter, LinkedIn, Pinterest,

GooglePlus, Web links

Sommariva

et al. (2018)

Zika Virus US Psychological Theory Experiment Twitter Vraga and

Bode (2017)

Zika Virus English-speaking Countries Network Theory Content Analysis (Twitter);

Social Network Analysis

Twitter Wood (2018)

Chronic Non-communicable Disease

Cancer

Cancer + Diet and Nutrit-

ion

US Rumour Theory

(Psychology)

Experiment Websites Chua and

Banerjee

(2018)

Cancer Screening Japan N/A Content Analysis (Website) Websites, Blogs, Facebook Okuhara et al.

(2017)

Y. Wang, et al. Social Science & Medicine 240 (2019) 112552

Gynaecologic Cancer China N/A Content Analysis (Social

Media)

Sina Weibo Tencent Weibo Chen et al.

(2018a,b)

Cardiovascular Disease

Diabetes + Diet India N/A Content Analysis (Video) YouTube Leong et al.

(2017)

Heart Failure English-speaking Countries N/A Content Analysis (Video) YouTube Chen et al.

(2013)

Hypertension English-speaking Countries N/A Content Analysis (Video) YouTube Kumar et al.

(2014)

Other Chronic Non-communicable Disease

Inﬂammatory Bowel Dise-

ase

English-speaking Countries N/A Survey Facebook, Twitter, YouTube, Website Groshek et al.

(2017)

Psoriasis + Diet English-speaking countries N/A Content Analysis (Video) YouTube Qi et al.

(2016)

Others

Diet and Nutrition

Anorexia English, Spanish, Italian and

Portuguese-speaking

Countries

N/A Content Analysis (Video) YouTube, Vimeo and Voeh Syed-Abdul

et al. (2013)

Diet and Health-related In-

formation

Arabic-speaking Countries N/A Content Analysis (Twitter) Twitter Alnemer et al.

(2015)

Diet and Health-related R-

umour

Italy Network Theory Content Analysis (Social

Media); Social Network

Analysis

Facebook, Twitter and YouTube Bessi et al.

(2015)

Smoking

e-cigarette US (Chicago) Network Theory Content Analysis (Twitter);

Social Network Analysis

Twitter Harris et al.

(2014)

Hookah Tobacco Smoking US N/A Content Analysis (Website) Websites, Facebook, MySpace, Twitter Primack et al.

(2012)

Tobacco US N/A Experiment YouTube Albarracin

et al. (2018)

Water Safety/Quality

Water Fluoridation US N/A Content Analysis (Social

Media)

Facebook, Twitter, YouTube, Website Mertz and

Allukian

(2014)

Water Fluoridation US Network Theory Social Network Analysis;

Sentiment Analysis

Facebook, Websites Seymour et al.

(2015)

General Health (Rumour Psychology)

Health-related Rumours Southeast Asia Epistemology

(Philosophy)

Experiment Websites Chua and

Banerjee

(2017)

Health-related Rumours US Rumour Theory

(Psychology)

Experiment Twitter Li and

Sakamoto

(2015)

Health-related Rumours China N/A Content Analysis (Wechat) Wechat Li et al.

(2017)

Health-related Rumours US Psychological Theory Experiment Twitter, Websites Ozturk et al.

(2015)

Health-related Rumours Poland N/A Content Analysis (Social

Media)

Facebook, Twitter, LinkedIn, Pinterest Waszak et al.

(2018)

Miscellaneous

Abortion US N/A Content Analysis (Website) Websites Bryant et al.

(2014)

Dialysis English-speaking Countries N/A Content Analysis (Video) YouTube Garg et al.

(2015)

Drug Bahrain N/A Content Analysis

(WhatsApp)

WhatsApp Al Khaja et al.

(2018)

Multiple Sclerosis Italy N/A Content Analysis (Website) Websites Lavorgna

et al. (2018)

Paediatric

Tonsillectomy + Diet

English-speaking Countries N/A Content Analysis (Video) YouTube Strychowsky

et al. (2013)

Suicide China N/A Content Analysis (Weibo) Sina Weibo Li et al.

(2018)

References

Abbasi, M.-A., Liu, H., 2013. Measuring user credibility in social media. In: Greenberg,

A.M., Kennedy, W.G., Bos, N.D. (Eds.), Social Computing, Behavioral-Cultural

Modeling and Prediction. Springer Berlin Heidelberg, pp. 441–448.

Abroms, L.C., Lee Westmaas, J., Bontemps-Jones, J., Ramani, R., Mellerson, J., 2013. A

content analysis of popular smartphone apps for smoking cessation. Am. J. Prev. Med.

45 (6), 732–736. https://doi.org/10.1016/j.amepre.2013.07.008.

Adebimpe, W.O., Adeyemi, D.H., Faremi, A., Ojo, J.O., Efuntoye, A.E., 2015. The re-

levance of the social networking media in Ebola virus disease prevention and control

in Southwestern Nigeria. Pan Afr. Med. J. 22 (Suppl. 1). https://doi.org/10.11694/

pamj.supp.2015.22.1.6165.

Al Khaja, K.A.J., AlKhaja, A.K., Sequeira, R.P., 2018. Drug information, misinformation,

and disinformation on social media: a content analysis study. J. Public Health Policy.

https://doi.org/10.1057/s41271-018-0131-2.

Albarracin, D., Romer, D., Jones, C., Hall Jamieson, K., Jamieson, P., 2018. Misleading

claims about tobacco products in YouTube videos: experimental eﬀects of mis-

information on unhealthy attitudes. J. Med. Internet Res. 20 (6), e229. https://doi.

org/10.2196/jmir.9959.

Allport, G.W., Postman, L., 1947. The psychology of rumor. In: The Psychology of Rumor.

Henry Holt, Oxford, England.

Alnemer, K.A., Alhuzaim, W.M., Alnemer, A.A., Alharbi, B.B., Bawazir, A.S., Barayyan,

O.R., Balaraj, F.K., 2015. Are Health-Related Tweets Evidence Based? Review and

Analysis of Health-Related Tweets on Twitter. Journal of Medical Internet Research

17 (10), e246. https://doi.org/10.2196/jmir.4898.

Aquino, F., Donzelli, G., De Franco, E., Privitera, G., Lopalco, P.L., Carducci, A., 2017. The

web and public conﬁdence in MMR vaccination in Italy. Vaccine 35 (35 Pt B),

4494–4498. https://doi.org/10.1016/j.vaccine.2017.07.029.

Bahk, C.Y., Cumming, M., Paushter, L., Madoﬀ, L.C., Thomson, A., Brownstein, J.S., 2016.

Publicly available online tool facilitates real-time monitoring of vaccine

Y. Wang, et al. Social Science & Medicine 240 (2019) 112552

conversations and sentiments. Health Aﬀ. 35 (2), 341–347. https://doi.org/10.1377/

hlthaﬀ.2015.1092.

Bartlett, F.C., 1932. Remembering: a study in experimental and social psychology. In:

Remembering: A Study in Experimental and Social Psychology. Cambridge University

Press, New York, NY, US.

Basch, C.H., Zybert, P., Reeves, R., Basch, C.E., 2017. What do popular YouTubeTM vi-

deos say about vaccines? Child Care Health Dev. 43 (4), 499–503. https://doi.org/10.

1111/cch.12442.

Bessi, A., Zollo, F., Vicario, M.D., Scala, A., Caldarelli, G., Quattrociocchi, W., 2015. Trend

of narratives in the age of misinformation. PLoS One 10 (8), e0134641. https://doi.

org/10.1371/journal.pone.0134641.

Bessi, A., Petroni, F., Vicario, M.D., Zollo, F., Anagnostopoulos, A., Scala, A.,

Quattrociocchi, W., 2016. Homophily and polarization in the age of misinformation.

Eur. Phys. J. Spec. Top. 225 (10), 2047–2059. https://doi.org/10.1140/epjst/e2015-

50319-0.

Betsch, C., Sachse, K., 2012. Dr. Jekyll or Mr. Hyde? (How) the Internet inﬂuences vac-

cination decisions: recent evidence and tentative guidelines for online vaccine com-

munication. Vaccine 30 (25), 3723–3726. https://doi.org/10.1016/j.vaccine.2012.

03.078.

Bode, L., Vraga, E.K., 2015. In related news, that was wrong: the correction of mis-

information through related stories functionality in social media. J. Commun. 65 (4),

619–638. https://doi.org/10.1111/jcom.12166.

Bode, L., Vraga, E.K., 2018. See something, say something: correction of global health

misinformation on social media. Health Commun. 33 (9), 1131–1140. https://doi.

org/10.1080/10410236.2017.1331312.

Bora, K., Das, D., Barman, B., Borah, P., 2018. Are internet videos useful sources of in-

formation during global public health emergencies? A case study of YouTube videos

during the 2015-16 Zika virus pandemic. Pathog. Glob. Health 112 (6), 320–328.

https://doi.org/10.1080/20477724.2018.1507784.

Bordia, P., Difonzo, N., 2004. Problem solving in social interactions on the internet:

rumor as social cognition. Soc. Psychol. Q. 67 (1), 33–49. https://doi.org/10.1177/

019027250406700105.

Boutyline, A., Willer, R., 2017. The social structure of political echo chambers: variation

in ideological homophily in online networks. Political Psychol. 38 (3), 551–569.

https://doi.org/10.1111/pops.12337.

Bryant, A.G., Narasimhan, S., Bryant-Comstock, K., Levi, E.E., 2014. Crisis pregnancy

center websites: information, misinformation and disinformation. Contraception 90

(6), 601–605. https://doi.org/10.1016/j.contraception.2014.07.003.

Castillo, C., Mendoza, M., Poblete, B., 2011. Information credibility on twitter. In:

Proceedings of the 20th International Conference on World Wide Web, pp. 675–684.

https://doi.org/10.1145/1963405.1963500.

Chen, H.-M., Hu, Z.-K., Zheng, X.-L., Yuan, Z.-S., Xu, Z.-B., Yuan, L.-Q., Liao, X.-B., 2013.

Eﬀectiveness of YouTube as a source of medical information on heart transplantation.

Interactive J. Med. Res. 2 (2), e28. https://doi.org/10.2196/ijmr.2669.

Chen, B., Shao, J., Liu, K., Cai, G., Jiang, Z., Huang, Y., et al., 2018a. Does eating chicken

feet with pickled peppers cause Avian inﬂuenza? Observational case study on Chinese

social media during the Avian inﬂuenza a (H7N9) outbreak. JMIR Publ. Health Surv.

4 (1), e32. https://doi.org/10.2196/publichealth.8198.

Chen, L., Wang, X., Peng, T.-Q., 2018b. Nature and diﬀusion of gynecologic cancer-re-

lated misinformation on social media: analysis of tweets. J. Med. Internet Res. 20

(10), e11515. https://doi.org/10.2196/11515.

Chew, C., Eysenbach, G., 2010. Pandemics in the age of twitter: content analysis of tweets

during the 2009 H1N1 outbreak. PLoS One 5 (11), e14118. https://doi.org/10.1371/

journal.pone.0014118.

Chua, A.Y.K., Banerjee, S., 2017. To share or not to share: the role of epistemic belief in

online health rumors. Int. J. Med. Inform. 108, 36–41. https://doi.org/10.1016/j.

ijmedinf.2017.08.010.

Chua, A.Y.K., Banerjee, S., 2018. Intentions to trust and share online health rumors: an

experiment with medical professionals. Comput. Hum. Behav. 87, 1–9. https://doi.

org/10.1016/j.chb.2018.05.021.

Datta, S.S., O'Connor, P.M., Jankovic, D., Muscat, M., Ben Mamou, M.C., Singh, S., Butler,

R., 2017. Progress and challenges in measles and rubella elimination in the WHO

European Region. Vaccine. https://doi.org/10.1016/j.vaccine.2017.06.042.

Del Vicario, M.D., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Quattrociocchi,

W., 2016. The spreading of misinformation online. Proc. Natl. Acad. Sci. 113 (3),

554–559. https://doi.org/10.1073/pnas.1517441113.

Donzelli, G., Palomba, G., Federigi, I., Aquino, F., Cioni, L., Verani, M., Lopalco, P., 2018.

Misinformation on vaccination: a quantitative analysis of YouTube videos. Hum.

Vaccines Immunother. 14 (7), 1654–1659. https://doi.org/10.1080/21645515.2018.

1454572.

Dubey, D., Amritphale, A., Sawhney, A., Dubey, D., Srivastav, N., 2014. Analysis of

YouTube as a Source of Information for West Nile Virus Infection. Clinical Medicine &

Research 12 (3–4), 129–132. https://doi.org/10.3121/cmr.2013.1194.

Eng, D.S., Lee, J.M., 2013. The promise and peril of mobile health applications for dia-

betes and endocrinology. Pediatr. Diabetes 14 (4), 231–238. https://doi.org/10.

1111/pedi.12034.

Filia, A., Bella, A., Del Manso, M., Baggieri, M., Magurano, F., Rota, M.C., 2017. Ongoing

outbreak with well over 4,000 measles cases in Italy from January to end August

2017 −what is making elimination so diﬃcult? Euro Surveill. 22 (37). https://doi.

org/10.2807/1560-7917.ES.2017.22.37.30614.

Free, C., Phillips, G., Galli, L., Watson, L., Felix, L., Edwards, P., Haines, A., 2013. The

eﬀectiveness of mobile-health technology-based health behaviour change or disease

management interventions for health care consumers: a systematic review. PLoS Med.

10 (1), e1001362. https://doi.org/10.1371/journal.pmed.1001362.

Fung, I.C.-H., Fu, K.-W., Chan, C.-H., Chan, B.S.B., Cheung, C.-N., Abraham, T., Tse,

Z.T.H., 2016. Social media's initial reaction to information and misinformation on

Ebola, August 2014: facts and rumors. Public Health Rep. (Washington D. C: 1974)

131 (3), 461–473. https://doi.org/10.1177/003335491613100312.

Garg, N., Venkatraman, A., Pandey, A., Kumar, N., 2015. YouTube as a source of in-

formation on dialysis: a content analysis. Nephrology (Carlton, Vic.) 20 (5), 315–320.

https://doi.org/10.1111/nep.12397.

Ghenai, A., Mejova, Y., 2017. Catching Zika Fever: Application of Crowdsourcing and

Machine Learning for Tracking Health Misinformation on Twitter. ArXiv:1707.03778

[Cs]. Retrieved from. http://arxiv.org/abs/1707.03778.

Grant, L., Hausman, B.L., Cashion, M., Lucchesi, N., Patel, K., Roberts, J., 2015.

Vaccination persuasion online: a qualitative study of two provaccine and two vac-

cine-skeptical websites. J. Med. Internet Res. 17 (5), e133. https://doi.org/10.2196/

jmir.4153.

Groshek, J., Basil, M., Guo, L., Parker Ward, S., Farraye, F.A., Reich, J., 2017. Media

consumption and creation in attitudes toward and knowledge of inﬂammatory bowel

disease: web-based survey. J. Med. Internet Res. 19 (12), e403. https://doi.org/10.

2196/jmir.7624.

Grunig, J., 1997. A Situational Theory of Publics: conceptual history, recent challenges

and new research. In: Grunig, J. (Ed.), Public Relations Research: an International

Perspective, pp. 3–47. Retrieved from. https://succeed.stir.ac.uk/bbcswebdav/

courses/PREP86_201314_Autumn_A/Digitised_extracts/PREP86GrunigSituational.

pdf.

Harris, J.K., Moreland-Russell, S., Choucair, B., Mansour, R., Staub, M., Simmons, K.,

2014. Tweeting for and against public health policy: response to the Chicago

Department of Public Health's electronic cigarette Twitter campaign. J. Med. Internet

Res. 16 (10), e238. https://doi.org/10.2196/jmir.3622.

He, Z., Cai, Z., Wang, X., 2015. Modeling propagation dynamics and developing opti-

mized countermeasures for rumor spreading in online social networks. In: 2015 IEEE

35th International Conference on Distributed Computing Systems, pp. 205–214.

https://doi.org/10.1109/ICDCS.2015.29.

House of Commons Disinformation and ‘fake News’:ﬁnal report published - news from

parliament. Retrieved March 27, 2019, from UK Parliament Website. https://www.

parliament.uk/business/committees/committees-a-z/commons-select/digital-

culture-media-and-sport-committee/news/fake-news-report-published-17-19/.

Jerit, J., Barabas, J., 2012. Partisan perceptual bias and the information environment. J.

Politics 74 (3), 672–684. https://doi.org/10.1017/s0022381612000187.

Jin, F., Wang, W., Zhao, L., Dougherty, E., Cao, Y., Lu, C.T., Ramakrishnan, N., 2014.

Misinformation propagation in the age of twitter. Computer 47 (12), 90–94. https://

doi.org/10.1109/MC.2014.361.

Johnson, T.J., Kaye, B.K., 1998. Cruising is believing?: comparing internet and traditional

sources on media credibility measures. Journal. Mass Commun. Q. 75 (2), 325–340.

https://doi.org/10.1177/107769909807500208.

Jost, J.T., van der Linden, S., Panagopoulos, C., Hardin, C.D., 2018. Ideological asym-

metries in conformity, desire for shared reality, and the spread of misinformation.

Curr. Opin. Psychol. 23, 77–83. https://doi.org/10.1016/j.copsyc.2018.01.003.

Karlova, N., Fisher, K.E., 2013. A social diﬀusion model of misinformation and disin-

formation for understanding human information behaviour. Inf. Res. 18.

Kata, A., 2012. Anti-vaccine activists, Web 2.0, and the postmodern paradigm –an

overview of tactics and tropes used online by the anti-vaccination movement.

Vaccine 30 (25), 3778–3789. https://doi.org/10.1016/j.vaccine.2011.11.112.

Kaufman, A.B., Kaufman, J.C., Barnett, P.J., 2018. Pseudoscience: the Conspiracy against

Science. MIT Press.

Kim, D., Johnson, T.J., 2009. A shift in media credibility: comparing internet and tradi-

tional news sources in South Korea. Int. Commun. Gaz. 71 (4), 283–302. https://doi.

org/10.1177/1748048509102182.

Kirkpatrick, C., 1932. A tentative study in experimental social psychology. Am. J. Sociol.

38 (2), 194–206.

Krishna, A., 2017. Motivation with misinformation: conceptualizing lacuna individuals

and publics as knowledge-deﬁcient, issue-negative activists. J. Public Relat. Res. 29

(4), 176–193. https://doi.org/10.1080/1062726X.2017.1363047.

Kumar, N., Pandey, A., Venkatraman, A., Garg, N., 2014. Are video sharing Web sites a

useful source of information on hypertension? J. Am. Soc. Hypertens. 8 (7), 481–490.

https://doi.org/10.1016/j.jash.2014.05.001.

Lavorgna, L., De Stefano, M., Sparaco, M., Moccia, M., Abbadessa, G., Montella, P.,

Bonavita, S., 2018. Fake news, inﬂuencers and health-related professional partici-

pation on the Web: a pilot study on a social-network of people with Multiple

Sclerosis. Multiple Scler. Relat. Disorders 25, 175–178. https://doi.org/10.1016/j.

msard.2018.07.046.

Lazer, D.M.J., Baum, M.A., Benkler, Y., Berinsky, A.J., Greenhill, K.M., Menczer, F.,

Zittrain, J.L., 2018. The science of fake news. Science 359 (6380), 1094–1096.

https://doi.org/10.1126/science.aao2998.

Leong, A.Y., Sanghera, R., Jhajj, J., Desai, N., Jammu, B.S., Makowsky, M.J., 2017. Is

YouTube useful as a source of health information for adults with type 2 diabetes? A

South Asian perspective. Can. J. Diabetes 0 (0). https://doi.org/10.1016/j.jcjd.2017.

10.056.

Lewandowsky, S., Ecker, U.K.H., Seifert, C.M., Schwarz, N., Cook, J., 2012.

Misinformation and its correction: continued inﬂuence and successful debiasing.

Psychol. Sci. Public Interest 13 (3), 106–131. https://doi.org/10.1177/

1529100612451018.

Lewandowsky, S., Gignac, G.E., Oberauer, K., 2013. The role of conspiracist ideation and

worldviews in predicting rejection of science. PLoS One 8 (10), e75637. https://doi.

org/10.1371/journal.pone.0075637.

Li, H., Sakamoto, Y., 2015. Computing the veracity of information through crowds: a

method for reducing the spread of false messages on social media. In: 2015 48th

Hawaii International Conference on System Sciences, pp. 2003–2012. https://doi.

org/10.1109/HICSS.2015.240.

Li, A., Huang, X., Jiao, D., O'Dea, B., Zhu, T., Christensen, H., 2018. An analysis of stigma

and suicide literacy in responses to suicides broadcast on social media. Asia Pac.

Psychiatr. Oﬃc. J.e Pac. Rim Coll. Psychiatr. 10 (1). https://doi.org/10.1111/appy.

12314.

Li, Y., Zhang, X., Wang, S., 2017. Fake vs. Real health information in social media in

China. Proceedings of the Association for Information Science and Technology 54 (1),

742–743. https://doi.org/10.1002/pra2.2017.14505401139.

Lupia, A., 2013. Communicating science in politicized environments. Proc. Natl. Acad.

Sci. 110 (Suppl. 3), 14048–14054. https://doi.org/10.1073/pnas.1212726110.

Y. Wang, et al. Social Science & Medicine 240 (2019) 112552

Mahoney, L.M., Tang, T., Ji, K., Ulrich-Schad, J., 2015. The Digital Distribution of Public

Health News Surrounding the Human Papillomavirus Vaccination: A Longitudinal

Infodemiology Study. JMIR Public Health and Surveillance 1 (1), e2. https://doi.org/

10.2196/publichealth.3310.

McKee, M., Diethelm, P., 2010. How the growth of denialism undermines public health.

BMJ 341, c6950. https://doi.org/10.1136/bmj.c6950.

McKernon, E., 1925, October. Fake News and the Public. Harper's Magazine. Retrieved

from. https://harpers.org/archive/1925/10/fake-news-and-the-public/.

Mertz, A., Allukian, M., 2014. Community water ﬂuoridation on the Internet and social

media. Journal of the Massachusetts Dental Society 63 (2), 32–36.

Metzger, M.J., 2007. Making sense of credibility on the Web: models for evaluating online

information and recommendations for future research. J. Am. Soc. Inf. Sci. Technol.

58 (13), 2078–2091. https://doi.org/10.1002/asi.20672.

Metzger, M.J., Flanagin, A.J., Eyal, K., Lemus, D.R., Mccann, R.M., 2003. Credibility for

the 21st century: integrating perspectives on source, message, and media credibility

in the contemporary media environment. Ann. Int. Commun. Assoc. 27 (1), 293–335.

https://doi.org/10.1080/23808985.2003.11679029.

Metzger, M.J., Flanagin, A.J., Medders, R.B., 2010. Social and heuristic approaches to

credibility evaluation online. J. Commun. 60 (3), 413–439. https://doi.org/10.1111/

j.1460-2466.2010.01488.x.

Moher, D., Liberati, A., Tetzlaﬀ, J., Altman, D.G., Group, T.P., 2009. Preferred reporting

items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 6

(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097.

Murray, E., Khadjesari, Z., White, I.R., Kalaitzaki, E., Godfrey, C., McCambridge, J.,

Wallace, P., 2009. Methodological challenges in online trials. J. Med. Internet Res. 11

(2). https://doi.org/10.2196/jmir.1052.

Nicholson, M.S., Leask, J., 2012. Lessons from an online debate about measles–-

mumps–rubella (MMR) immunization. Vaccine 30 (25), 3806–3812. https://doi.org/

10.1016/j.vaccine.2011.10.072.

Noack, A., 2009. Modularity clustering is force-directed layout. Phys. Rev. E 79 (2),

026102. https://doi.org/10.1103/PhysRevE.79.026102.

Nyhan, B., Reiﬂer, J., 2010. When corrections fail: the persistence of political mis-

perceptions. Political Behav. 32 (2), 303–330. https://doi.org/10.1007/s11109-010-

9112-2.

Okuhara, T., Ishikawa, H., Okada, M., Kato, M., Kiuchi, T., 2017. Assertions of Japanese

websites for and against cancer screening: a text mining analysis. Asian Pac. J. Cancer

Prev. APJCP: Asian Pac. J. Cancer Prev. APJCP 18 (4), 1069–1075. https://doi.org/

10.22034/APJCP.2017.18.4.1069.

Ozturk, P., Li, H., Sakamoto, Y., 2015. Combating rumor spread on Social Media: the

eﬀectiveness of refutation and warning. In: 2015 48th Hawaii International

Conference on System Sciences, pp. 2406–2414. https://doi.org/10.1109/HICSS.

2015.288.

Panatto, D., Amicizia, D., Arata, L., Lai, P.L., Gasparini, R., 2018. A comprehensive

analysis of Italian web pages mentioning squalene-based inﬂuenza vaccine adjuvants

reveals a high prevalence of misinformation. Hum. Vaccines Immunother. 14 (4),

969–977. https://doi.org/10.1080/21645515.2017.1407483.

Pathak, R., Poudel, D.R., Karmacharya, P., Pathak, A., Aryal, M.R., Mahmood, M.,

Donato, A.A., 2015. YouTube as a source of information on Ebola virus disease. N.

Am. J. Med. Sci. 7 (7), 306–309. https://doi.org/10.4103/1947-2714.161244.

Pennycook, G., Rand, D.G., 2018. Lazy, not biased: susceptibility to partisan fake news is

better explained by lack of reasoning than by motivated reasoning. Cognition.

https://doi.org/10.1016/j.cognition.2018.06.011.

Porat, T., Garaizar, P., Ferrero, M., Jones, H., Ashworth, M., Vadillo, M.A., 2018. Content

and source analysis of popular tweets following a recent case of diphtheria in Spain.

Eur. J. Public Health. https://doi.org/10.1093/eurpub/cky144.

Primack, B.A., Rice, K.R., Shensa, A., Carroll, M.V., DePenna, E.J., Nakkash, R., Barnett,

T.E., 2012. U.S. hookah tobacco smoking establishments advertised on the internet.

American Journal of Preventive Medicine 42 (2), 150–156. https://doi.org/10.1016/

j.amepre.2011.10.013.

Qi, J., Trang, T., Doong, J., Kang, S., Chien, A.L., 2016. Misinformation is prevalent in

psoriasis-related YouTube videos. Dermatol. Online J. 22 (11).

Radzikowski, J., Stefanidis, A., Jacobsen, K.H., Croitoru, A., Crooks, A., Delamater, P.L.,

2016. The measles vaccination narrative in twitter: a quantitative analysis. JMIR

Publ. Health Surv. 2 (1), e1. https://doi.org/10.2196/publichealth.5059.

Reyna, V.F., 2012. Risk perception and communication in vaccination decisions: a fuzzy-

trace theory approach. Vaccine 30 (25), 3790–3797. https://doi.org/10.1016/j.

vaccine.2011.11.070.

Ritterband, L.M., Tate, D.F., 2009. The science of internet interventions. Introduction.

Ann. Behav. Med. Publ. Soc. Behav. Med. 38 (1), 1–3. https://doi.org/10.1007/

s12160-009-9132-5.

Rosnow, R.L., 1991. Inside rumor: a personal journey. Am. Psychol. 46 (5), 484–496.

https://doi.org/10.1037/0003-066X.46.5.484.

Salathé, M., Khandelwal, S., 2011. Assessing vaccination sentiments with online social

media: implications for infectious disease dynamics and control. PLoS Comput. Biol.

7 (10), e1002199. https://doi.org/10.1371/journal.pcbi.1002199.

Scanfeld, D., Scanfeld, V., Larson, E.L., 2010. Dissemination of health information

through social networks: twitter and antibiotics. Am. J. Infect. Contr. 38 (3),

182–188. https://doi.org/10.1016/j.ajic.2009.11.004.

Schmidt, A.L., Zollo, F., Scala, A., Betsch, C., Quattrociocchi, W., 2018. Polarization of the

vaccination debate on Facebook. Vaccine 36 (25), 3606–3612. https://doi.org/10.

1016/j.vaccine.2018.05.040.

Seltzer, E.K., Horst-Martz, E., Lu, M., Merchant, R.M., 2017. Public sentiment and dis-

course about Zika virus on Instagram. Public Health 150, 170–175. https://doi.org/

10.1016/j.puhe.2017.07.015.

Serrano, E., Iglesias, C.Á., Garijo, M., 2015. A novel agent-based rumor spreading model

in twitter. In: Proceedings of the 24th International Conference on World Wide Web,

pp. 811–814. https://doi.org/10.1145/2740908.2742466.

Seymour, B., Getman, R., Saraf, A., Zhang, L.H., Kalenderian, E., 2015. When advocacy

obscures accuracy online: digital pandemics of public health misinformation through

an antiﬂuoride case study. Am. J. Public Health 105 (3), 517–523. https://doi.org/

10.2105/AJPH.2014.302437.

Shao, C., Hui, P.-M., Wang, L., Jiang, X., Flammini, A., Menczer, F., Ciampaglia, G.L.,

2018. Anatomy of an online misinformation network. PLoS One 13 (4), e0196087.

https://doi.org/10.1371/journal.pone.0196087.

Sharma, M., Yadav, K., Yadav, N., Ferdinand, K.C., 2017. Zika virus pandemic-analysis of

Facebook as a social media health information platform. Am. J. Infect. Contr. 45 (3),

301–302. https://doi.org/10.1016/j.ajic.2016.08.022.

Shin, D.-H., Lee, S., Hwang, Y., 2017. How do credibility and utility play in the user

experience of health informatics services? Comput. Hum. Behav. 67 (C), 292–302.

https://doi.org/10.1016/j.chb.2016.11.007.

Sicilia, R., Giudice, S.L., Pei, Y., Pechenizkiy, M., Soda, P., 2017. Health-related rumour

detection on Twitter. In: 2017 IEEE International Conference on Bioinformatics and

Biomedicine. BIBM, pp. 1599–1606. https://doi.org/10.1109/BIBM.2017.8217899.

Signorini, A., Segre, A.M., Polgreen, P.M., 2011. The use of twitter to track levels of

disease activity and public concern in the U.S. During the inﬂuenza a H1N1 pan-

demic. PLoS One 6 (5), e19467. https://doi.org/10.1371/journal.pone.0019467.

Sommariva, S., Vamos, C., Mantzarlis, A., Đào, L.U.-L., Tyson, D.M., 2018. Spreading the

(fake) news: exploring health messages on social media and the implications for

health professionals using a case study. Am. J. Health Educ. 49 (4), 246–255. https://

doi.org/10.1080/19325037.2018.1473178.

Song, J., Song, T.M., Seo, D.-C., Jin, D.-L., Kim, J.S., 2017. Social Big Data Analysis of

Information Spread and Perceived Infection Risk During the 2015 Middle East

Respiratory Syndrome Outbreak in South Korea. Cyberpsychology, Behavior and

Social Networking 20 (1), 22–29. https://doi.org/10.1089/cyber.2016.0126.

Steinhubl, S.R., Muse, E.D., Topol, E.J., 2015. The emerging ﬁeld of mobile health. Sci.

Transl. Med. 7 (283) 283rv3-283rv3. https://doi.org/10.1126/scitranslmed.

aaa3487.

Stone, C.B., Wang, Q., 2018. From conversations to digital communication: the mnemonic

consequences of consuming and producing information via social media. Topics

Cognit. Sci. 0 (0). https://doi.org/10.1111/tops.12369.

Strychowsky, J.E., Nayan, S., Farrokhyar, F., MacLean, J., 2013. YouTube: a good source

of information on pediatric tonsillectomy? Int. J. Pediatr. Otorhinolaryngol. 77 (6),

972–975. https://doi.org/10.1016/j.ijporl.2013.03.023.

Swire, Briony, Berinsky Adam, J., Stephan, Lewandowsky, Ecker Ullrich, K.H., 2017.

Processing political misinformation: comprehending the Trump phenomenon. R. Soc.

Open Sci. 4 (3), 160802. https://doi.org/10.1098/rsos.160802.

Syed-Abdul, S., Fernandez-Luque, L., Jian, W.-S., Li, Y.-C., Crain, S., Hsu, M.-H., Liou, D.-

M., 2013. Misleading health-related information promoted through video-based so-

cial media: anorexia on YouTube. J. Med. Internet Res. 15 (2), e30. https://doi.org/

10.2196/jmir.2237.

Taber, C.S., Lodge, M., 2006. Motivated skepticism in the evaluation of political beliefs.

Am. J. Pol. Sci. 50 (3), 755–769. https://doi.org/10.1111/j.1540-5907.2006.

00214.x.

Tustin, J.L., Crowcroft, N.S., Gesink, D., Johnson, I., Keelan, J., Lachapelle, B., 2018.

User-driven comments on a Facebook advertisement recruiting Canadian parents in a

study on immunization: content analysis. JMIR Publ. Health Surv. 4 (3), e10090.

https://doi.org/10.2196/10090.

van Eck, N.J., Waltman, L., 2010. Software survey: VOSviewer, a computer program for

bibliometric mapping. Scientometrics 84 (2), 523–538. https://doi.org/10.1007/

s11192-009-0146-3.

Verelst, F., Willem, L., Beutels, P., 2016. Behavioural change models for infectious disease

transmission: a systematic review (2010–2015). J. R. Soc. Interface 13 (125),

20160820. https://doi.org/10.1098/rsif.2016.0820.

Vosoughi, S., Roy, D., Aral, S., 2018. The spread of true and false news online. Science

359 (6380), 1146–1151. https://doi.org/10.1126/science.aap9559.

Vraga, E.K., Bode, L., 2017. Using Expert Sources to Correct Health Misinformation in

Social Media: Science Communication.https://doi.org/10.1177/1075547017731776.

Wakamiya, S., Kawai, Y., Aramaki, E., 2016. After the Boom No One Tweets: Microblog-

Based Inﬂuenza Detection Incorporating Indirect Information. pp. 17–25. https://doi.

org/10.1145/3007818.3007822 October 17.

Wakeﬁeld, A.J., Murch, S.H., Anthony, A., Linnell, J., Casson, D.M., Malik, M., Walker-

Smith, J.A., 1998. RETRACTED: ileal-lymphoid-nodular hyperplasia, non-speciﬁc

colitis, and pervasive developmental disorder in children. The Lancet 351 (9103),

637–641. https://doi.org/10.1016/S0140-6736(97)11096-0.

Wardle, C., 2017, February 16. Fake news. It's complicated. retrieved June 9, 2018, from

ﬁrst draft news website. https://ﬁrstdraftnews.org:443/fake-news-complicated/.

Wardle, C., Derakhshan, H., 2017, October 31. Information disorder: an Interdisciplinary

framework. Retrieved January 19, 2019, from ﬁrst draft news website. https://

ﬁrstdraftnews.org:443/coe-report/.

Waszak, P.M., Kasprzycka-Waszak, W., Kubanek, A., 2018. The spread of medical fake

news in social media –the pilot quantitative study. Health Policy Technol. 7 (2),

115–118. https://doi.org/10.1016/j.hlpt.2018.03.002.

Wood, M.J., 2018. Propagating and debunking conspiracy theories on twitter during the

2015-2016 Zika virus outbreak. Cyberpsychol., Behav. Soc. Netw. 21 (8), 485–490.

https://doi.org/10.1089/cyber.2017.0669.

World Economic Forum, 2013. World Economic Forum –Global Risks 2013 Eighth

Edition. Retrieved June 9, 2018, from Global Risks 2013 Website. http://wef.ch/

GJKqei.

Xu, Z., Guo, H., 2018. Using text mining to compare online pro- and anti-vaccine head-

lines: word usage, sentiments, and online popularity. Commun. Stud. 69 (1),

103–122. https://doi.org/10.1080/10510974.2017.1414068.

Y. Wang, et al. Social Science & Medicine 240 (2019) 112552

Cancer misinformation on social media

Article

Full-text available

Jun 2024
CA-CANCER J CLIN

Social media is widely used globally by patients, families of patients, health professionals, scientists, and other stakeholders who seek and share information related to cancer. Despite many benefits of social media for cancer care and research, there is also a substantial risk of exposure to misinformation, or inaccurate information about cancer. Types of misinformation vary from inaccurate information about cancer risk factors or unproven treatment options to conspiracy theories and public relations articles or advertisements appearing as reliable medical content. Many characteristics of social media networks—such as their extensive use and the relative ease it allows to share information quickly—facilitate the spread of misinformation. Research shows that inaccurate and misleading health‐related posts on social media often get more views and engagement (e.g., likes, shares) from users compared with accurate information. Exposure to misinformation can have downstream implications for health‐related attitudes and behaviors. However, combatting misinformation is a complex process that requires engagement from media platforms, scientific and health experts, governmental organizations, and the general public. Cancer experts, for example, should actively combat misinformation in real time and should disseminate evidence‐based content on social media. Health professionals should give information prescriptions to patients and families and support health literacy. Patients and families should vet the quality of cancer information before acting upon it (e.g., by using publicly available checklists) and seek recommended resources from health care providers and trusted organizations. Future multidisciplinary research is needed to identify optimal ways of building resilience and combating misinformation across social media.

Meios, audiências e migrações: uma Revisão Sistemática da Literatura

Article

Full-text available

Jun 2024

Bruno Santos N Dias

O interesse da pesquisa de media, em especial os estudos de audiência, por questões étnicas e raciais tem início particularmente a partir dos anos 1990. Desde então, um vasto arcabouço conceitual, informado principalmente pelos estudos da diáspora, passou a incorporar os protocolos de pesquisa da área tornando-se fundamental para compreender diversos aspetos atravessados por processos transnacionais. Com o objetivo de mapear estudos que, ao longo das últimas décadas, propuseram um exame empírico das audiências com foco em pessoas e grupos migrantes, o presente artigo procura entender como estes trabalhos foram desenvolvidos e quais as suas principais características e interesses. O presente texto relata os resultados de uma revisão sistemática da literatura cujos registros recuperados compreendem artigos publicados em periódicos com revisão por pares e indexados nas bases de dados Scopus, Web of Science e SciELO até janeiro de 2023. A partir das informações extraídas destas bases e da análise de conteúdo do texto completo, esta revisão identifica as principais características dos estudos incluídos, como datas de publicação, autores, periódicos, opções metodológicas, objetos, enfoques e abordagem da receção, aspetos regionais e as principais referências e fenómenos analisados.

COVID-19’s myths, facts, concerning and obstinate posts on social network, and the mental health status of social network users in Bangladesh

Article

Full-text available

Jun 2024

Myths, misinformation, facts like posts spread by social media during COVID-19 pandemic had an enormous effect on psychological health. This study aimed to investigate social media based COVID-19’s posts and the psychological health status of participants. A cross-sectional, online survey-based study was conducted in between April to October 2021 using a structured and semi-structured questionnaire, predominantly involving 1200 active social network users in Bangladesh. Depression, anxiety, and stress were assessed using the Depression, Anxiety, and Stress Scale (DASS-21), while the Insomnia Severity Index (ISI) measured insomnia severity for selected participants. Internal reliabilities were calculated with Cronbach’s alpha coefficients (cut-off point 0.70). Unrelated multivariate logistic regression explored correlations among outcome errors, with the model assessing the impact of selected independent variables on mental health. The findings demonstrated that 27.8% individuals spread facts whereas 7.4% spread myths and misinformation about COVID-19 on social networks. Furthermore, 28.1% and 36.7% shared obstinate and concerning posts respectively. The prevalence of depression, anxiety and stress symptoms, ranging from mild to extremely severe, were 43.9%, 30.9%, and 23.8% respectively. However, 2.8% had severe level of insomnia. Facts, myths, tour attending, and no mask group photos were significantly associated with anxiety, and less likelihood of experiencing anxiety. Interestingly, circulating such activities on social networks had no significant association with depression, stress, or insomnia. The spread of misinformation on social media undermines any efforts to contain COVID-19 infection. The findings hugely recommend of using fact checking facilities and adaptation to the pandemic situations to maintain lower prevalence of depression, anxiety, stress and insomnia.

Digital cloning of online social networks for language-sensitive agent-based modeling of misinformation spread

Article

Full-text available

Jun 2024
PLOS ONE

We develop a simulation framework for studying misinformation spread within online social networks that blends agent-based modeling and natural language processing techniques. While many other agent-based simulations exist in this space, questions over their fidelity and generalization to existing networks in part hinder their ability to drive policy-relevant decision making. To partially address these concerns, we create a ’digital clone’ of a known misinformation sharing network by downloading social media histories for over ten thousand of its users. We parse these histories to both extract the structure of the network and model the nuanced ways in which information is shared and spread among its members. Unlike many other agent-based methods in this space, information sharing between users in our framework is sensitive to topic of discussion, user preferences, and online community dynamics. To evaluate the fidelity of our method, we seed our cloned network with a set of posts recorded in the base network and compare propagation dynamics between the two, observing reasonable agreement across the twin networks over a variety of metrics. Lastly, we explore how the cloned network may serve as a flexible, low-cost testbed for misinformation countermeasure evaluation and red teaming analysis. We hope the tools explored here augment existing efforts in the space and unlock new opportunities for misinformation countermeasure evaluation, a field that may become increasingly important to consider with the anticipated rise of misinformation campaigns fueled by generative artificial intelligence.

Understanding the complex links between social media and health behaviour

Article

Jun 2024
Br Med J

Key messages: 1) Monitoring social media is important to understand public perceptions, biases, and false beliefs 2) Drawing conclusions on how social media affects health behaviour is difficult because measures are unstandardised, sources are limited, and data are incomplete and biased 3) Rigorous research is needed from varied settings and demographics to improve understanding of the effect of social media on health behaviour

Early Post COVID-19 Psychological & Subtle Cognitive Impairment: Role of Basic Inflammatory, Coagulation & Clinical Risk Factors Thesis Submitted for Partial Fulfilment of the Degree of M.Sc. in Psychiatry Under Supervision of

Thesis

Jun 2024

Ehab Elbaz

Digital Transformation on Organizational Performance: A Literature Review Analysis

Chapter

Jun 2024

The massive development of digital technology accelerated the pace of disruption in almost every industry, creating immense ambiguity and continuing to accelerate uncertainty in the business environment. The essentiality for an organization to adapt to rapid digital transformation led businesses to remain competitive and relevant in the industry. With the rise of the pandemic Covid 19, creating more challenges for organizations in the effort to improve digital maturity. This study focuses on digital transformation and its impact on an organization's performance while narrowing down the research to the telecommunication industry in the Brunei Darussalam context. The contribution of this study is expected to fill the gap in the literature regarding antecedents of a successful digital transformation.

YouTube as a source of education in perioperative anesthesia for patients and trainees: a systematic reviewYouTube comme source d’éducation sur l’anesthésie périopératoire pour la patientèle et les stagiaires : une revue systématique

Article

Jun 2024

Online video sharing platforms like YouTube (Google LLC, San Bruno, CA, USA) have become a substantial source of health information. We sought to conduct a systematic review of studies assessing the overall quality of perioperative anesthesia videos on YouTube. We searched Embase, MEDLINE, and Ovid for articles published from database inception to 1 May 2023. We included primary studies evaluating YouTube videos as a source of information regarding perioperative anesthesia. We excluded studies not published in English and studies assessing acute or chronic pain. Studies were screened and data were extracted in duplicate by two reviewers. We appraised the quality of studies according to the social media framework published in the literature. We used descriptive statistics to report the results using mean, standard deviation, range, and n/total N (%). Among 8,908 citations, we identified 14 studies that examined 796 videos with 59.7 hr of content and 47.5 million views. Among the 14 studies that evaluated the video content quality, 17 different quality assessment tools were used, only three of which were externally validated (Global Quality Score, modified DISCERN score, and JAMA score). Per global assessment rating of video quality, 11/13 (85%) studies concluded the overall video quality as poor. Overall, the educational content quality of YouTube videos evaluated in the literature accessible as an educational resource regarding perioperative anesthesia was poor. While these videos are in demand, their impact on patient and trainee education remains unclear. A standardized methodology for evaluating online videos is merited to improve future reporting. A peer-reviewed approach to online open-access videos is needed to support patient and trainee education in anesthesia. Open Science Framework (https://osf.io/ajse9); first posted, 1 May 2023.

Disinformation about COVID-19 on Social Media and Epistemic Crisis – A Problem Overview

Article

Jun 2024

The text considers several critical issues related to the role of false information in the COVID-19 pandemic. It mainly focuses on social media, which often resemble echo chambers responsible for disseminating disinformation. In these echo chambers, users close themselves off from arguments and justifications different from their own, often with a strong tendency towards polarization of views and attitudes. A particular case of echo chambers is the conspiracy mentality propagated in social media, promoting conspirational beliefs about COVID-19, which, besides offering an alternative understanding of reality, deepens distrust towards epistemic authorities and methods of producing scientific knowledge. This indicates an epistemic crisis as a consequence of the pandemic, which must be addressed in order to rebuild and protect epistemic trust. The authors conclude that the consequence of this crisis is a regression of cognitive abilities, which may, in a feedback loop, exacerbate the epistemic crisis.

Investigation of Public Acceptance for Misinformation Correction in Chinese Social Media Based on Sentiment Attributions: Infodemiology Study Using Aspect-based Sentiment Analysis (Preprint)

Article

Jun 2023
J MED INTERNET RES

Homophily and polarization in the age of misinformation

Article

Full-text available

Oct 2016

The World Economic Forum listed massive digital misinformation as one of the main threats for our society. The spreading of unsubstantiated rumors may have serious consequences on public opinion such as in the case of rumors about Ebola causing disruption to health-care workers. In this work we target Facebook to characterize information consumption patterns of 1.2 M Italian users with respect to verified (science news) and unverified (conspiracy news) contents. Through a thorough quantitative analysis we provide important insights about the anatomy of the system across which misinformation might spread. In particular, we show that users’ engagement on verified (or unverified) content correlates with the number of friends having similar consumption patterns (homophily). Finally, we measure how this social system responded to the injection of 4,709 false information. We find that the frequent (and selective) exposure to specific kind of content (polarization) is a good proxy for the detection of homophile clusters where certain kind of rumors are more likely to spread.

Nature and Diffusion of Gynecologic Cancer–Related Misinformation on Social Media: Analysis of Tweets

Article

Full-text available

Oct 2018
J MED INTERNET RES

Background: Over the last two decades, the incidence and mortality rates of gynecologic cancers have increased at a constant rate in China. Gynecologic cancers have become one of the most serious threats to women's health in China. With the widespread use of social media, an increasing number of individuals have employed social media to produce, seek, and share cancer-related information. However, health information on social media is not always accurate. Health, and especially cancer-related, misinformation has been widely spread on social media, which can affect individuals' attitudinal and behavioral responses to cancer. Objective: The aim of this study was to examine the nature and diffusion of gynecologic cancer-related misinformation on Weibo, the Chinese equivalent of Twitter. Methods: A total of 2691 tweets related to 2 gynecologic cancers-breast cancer and cervical cancer-posted on Weibo from June 2015 to June 2016 were extracted using the Python Web Crawler. Two medical school graduate students with expertise in gynecologic diseases were recruited to code the tweets to differentiate between true information and misinformation as well as to identify the types of falsehoods. The diffusion characteristics of gynecologic cancer-related misinformation were compared with those of the true information. Results: While most of the gynecologic cancer-related tweets provided medically accurate information, approximately 30% of them were found to contain misinformation. Furthermore, it was found that tweets about cancer treatment contained a higher percentage of misinformation than prevention-related tweets. Nevertheless, the prevention-related misinformation diffused significantly more broadly and deeply than true information on social media. Conclusions: The findings of this study suggest the need for controlling and reducing the cancer-related misinformation on social media with the efforts from both service providers and medical professionals. More specifically, it is important to correct falsehoods related to the prevention of gynecologic cancers on social media and increase individuals' capacity to assess the veracity of Web-based information to curb the spread and thus minimize the consequences of cancer-related misinformation.

Propagating and Debunking Conspiracy Theories on Twitter During the 2015–2016 Zika Virus Outbreak

Article

Full-text available

Jul 2018

Michael J. Wood

The present study investigates the characteristics of discussion of conspiracy theories about the Zika virus outbreak of 2015-16 on Twitter. Content and social network analysis of a dataset of 25,162 original Tweets about Zika virus conspiracy theories showed that relative to debunking messages, conspiracy theories spread through a more decentralized network, are more likely to invoke supposedly knowledgeable authorities in making arguments, and ask more rhetorical questions. These trends can be understood in the context of previous work on conspiracy theories, including the "just asking questions" style of rhetoric, the importance of sourcing and authority, and the tendency to simultaneously consider many different potential conspiracies that might underlie an important topic or event.

User-driven comments on a Facebook advertisement recruiting Canadian parents in a study on immunization: Content Analysis (Preprint)

Article

Full-text available

Feb 2018

Background: More people are searching for immunization information online and potentially being exposed to misinformation and antivaccination sentiment in content and discussions on social media platforms. As vaccination coverage rates remain suboptimal in several developed countries, and outbreaks of vaccine-preventable diseases become more prevalent, it is important that we build on previous research by analyzing themes in online vaccination discussions, including those that individuals may see without actively searching for information on immunization. Objective: The study aimed to explore the sentiments and themes behind an unsolicited debate on immunization in order to better inform public health interventions countering antivaccination sentiment. Methods: We analyzed and quantified 117 user-driven open-ended comments on immunization posted in the Comments section of a Facebook advertisement that targeted Canadian parents for recruitment into a larger study on immunization. Then, 2 raters coded all comments using content analysis. Results: Of 117 comments, 85 were posted by unique commentators, with most being female (65/85, 77%). The largest proportion of the immunization comments were positive (51/117, 43.6%), followed by negative (41/117, 35.0%), ambiguous (20/117, 17.1%), and hesitant (5/117, 4.3%). Inaccurate knowledge (27/130, 20.8%) and misperceptions of risk (23/130, 17.7%) were most prevalent in the 130 nonpositive comments. Other claims included distrust of pharmaceutical companies or government agencies (18/130, 13.8%), distrust of the health care system or providers (15/130, 11.5%), past negative experiences with vaccination or beliefs (10/130, 7.7%), and attitudes about health and prevention (10/130, 7.7%). Almost 40% (29/74, 39%) of the positive comments communicated the risks of not vaccinating, followed by judgments on the knowledge level of nonvaccinators (13/74, 18%). A total of 10 positive comments (10/74, 14%) specifically refuted the link between autism and vaccination. Conclusions: The presence of more than 100 unsolicited user-driven comments on a platform not intended for discussion, nor providing any information on immunization, illustrates the strong sentiments associated with immunization and the arbitrariness of the online platforms used for immunization debates. Health authorities should be more proactive in finding mechanisms to refute misinformation and misperceptions that are propagating uncontested online. Online debates and communications on immunization need to be identified by continuous monitoring in order for health authorities to understand the current themes and trends, and to engage in the discussion.

From Conversations to Digital Communication: The Mnemonic Consequences of Consuming and Producing Information via Social Media

Article

Full-text available

Jul 2018

Social media has become one of the most powerful and ubiquitous means by which individuals curate, share, and communicate information with their friends, family, and the world at large. Indeed, 90% of the American adolescents are active social media users, as well as 65% of American adults (Perrin, 2015; see also Duggan & Brenner, 2013). Despite this, psychologists are only beginning to understand the mnemonic consequences associated with social media use. In this article, we will distill this nascent literature by focusing on two primary factors: the type of information (personal vs. public) and the role (producer vs. consumer) individuals play when engaging with social media. In particular, we will highlight research examining induced forgetting for personal information as well as false memories and truthiness for public information. We will end by providing some tentative conclusions and a discussion of areas in need of additional research that will provide a more holistic understanding of the mnemonic consequences associated with social media use.

Pseudoscience: The conspiracy against science

Book

Jan 2018

In a post-truth, fake news world, we are particularly susceptible to the claims of pseudoscience. When emotions and opinions are more widely disseminated than scientific findings, and self-proclaimed experts get their expertise from Google, how can the average person distinguish real science from fake? This book examines pseudoscience from a variety of perspectives, through case studies, analysis, and personal accounts that show how to recognize pseudoscience, why it is so widely accepted, and how to advocate for real science. Contributors examine the basics of pseudoscience, including issues of cognitive bias; the costs of pseudoscience, with accounts of naturopathy and logical fallacies in the anti-vaccination movement; perceptions of scientific soundness; the mainstream presence of "integrative medicine," hypnosis, and parapsychology; and the use of case studies and new media in science advocacy. © 2018 Massachusetts Institute of Technology. All rights reserved.

Are internet videos useful sources of information during global public health emergencies? A case study of YouTube videos during the 2015-16 Zika virus pandemic

Article

Aug 2018

Background: Internet-videos, though popular sources of public health information, are often unverified and anecdotal. We critically evaluated YouTube videos about Zika virus available during the recent Zika pandemic. Methods: Hundred-and-one videos were retrieved from YouTube (search term: zika virus). Based upon content, they were classified as: informative, misleading or personal experience videos. Quality and reliability of these videos were evaluated using standardized tools. The viewer interaction metrics (e.g. no. of views, shares, etc.), video characteristics (video length, etc.) and the sources of upload were also assessed; and their relationship with the type, quality and reliability of the videos analyzed. Results: Overall, 70.3% videos were informative, while 23.8% and 5.9% videos were misleading and related to personal experiences, respectively. Although with shorter lengths (P < 0.01) and superior quality (P < 0.01), yet informative videos were viewed (P = 0.054), liked (P < 0.01) and shared (P < 0.05) less often than their misleading counterparts. Videos from independent users were more likely to be misleading (adjusted OR = 6.48, 95% CI: 1.69 - 24.83), of poorer (P < 0.05) quality and reliability than government/news agency videos. Conclusion: A considerable chunk of the videos were misleading. They were more popular (than informative videos) and could potentially spread misinformation. Videos from trustworthy sources like university/health organizations were scarce. Curation/authentication of health information in online video platforms (like YouTube) is necessary. We discuss means to harness them as useful source of information and highlight measures to curb dissemination of misinformation during public health emergencies. (The full article may be accessed at the free eprint link: https://www.tandfonline.com/eprint/rwJx2g7i9Hq9mXuiI9nN/full)

Content and source analysis of popular tweets following a recent case of diphtheria in Spain

Article

Jul 2018
EUR J PUBLIC HEALTH

Background: Despite major progress in global vaccination coverage, immunization rates are falling, resulting in outbreaks of vaccine-preventable diseases. This study analyses content and source of the most popular tweets related to a recent case in Spain where an unvaccinated child contracted and later died from diphtheria. Understanding the characteristics of these tweets in the context of vaccination could inform efforts by health promotion professionals to increase their reach and impact. Methods: We extracted tweets containing keywords related to the diphtheria case (from 1 May to 15 July 2015). We explored the prevalence of terms relating to policy and misinformation and manually coded the 194 most popular tweets (retweeted 100 or more times) with regard to source, topic, tone and sentiment. Results: A total of 722 974 tweets were collected. Prevalence of terms relating to policy and misinformation increased at the onset of the case and after the death of the child. Popular tweets (194) were either pro-vaccination (58%) or neutral, with none classified as anti-vaccination. Popular topics included criticism towards anti-vaccination groups (35%) and effectiveness of immunization (22%). Popular tweets were informative (47%) or opinions (53%), which mainly expressed frustration (24%) or humour/sarcasm (23%). Popular Twitter accounts were newspaper and TV channels (15%), as well as individual journalists and authors of popular science (13.4%). Conclusions: Healthcare organizations could collaborate with popular journalists or news outlets and employ authors of popular science to disseminate health information on social media, while addressing public concerns and misinformation in accessible ways.

Fake news, Influencers and health-related professional participation on the Web: a pilot study on a social-network of people with Multiple Sclerosis

Article

Jul 2018

Background: Over the last few decades, patients have increasingly been searching for health information on the Internet. This aspect of information seeking is important, especially for people affected by chronic pathologies and require lifelong treatment and management. These people are usually very well informed about the disease but are nonetheless vulnerable to hopes of being cured or saved, often amplified by misinformation, myths, legends, and therapies that are not always scientifically proven. Many studies suggest that some individuals prefer to rely on the Internet as their main source of information, often hindering the patient-doctor relationship. A professional approach is imperative to maintain confidentiality, honesty, and trust in the medical profession. Objective: we aimed to examine, in a medically supervised Italian web community (SMsocialnetwotk.com) dedicated to people with Multiple Sclerosis (pwMS), the posts shared by users and to verify the reliability of contents of posts shared by users pinpointed as Influencers through an online questionnaire. Methods: we grouped the posts published on SMsocialnetwork from April to June 2015 into those with medical content (scientifically correct or fake news), and those related to social interactions. Later, we gave a questionnaire to the community asking to identify the three users/Influencers providing the most reliable advice for everyday life with MS and the three users/Influencers providing the most useful information about MS treatments. Results: 308 posts reported scientific and relevant medical information, whereas 72 posts included pieces of fake news. 1420 posts were of general interest. Four out of the 6 Influencers had written only posts with correct medical information (3 were pwMS, 1 was a Neurologist) and never any fake news. The remaining 2 appointed Influencers (2 pwMS) had written only posts about general interests. Conclusion: the identification of fake news and their authors has shown that the latter are never appointed as Influencers. SMsocialnetwork.com acted as a "web safe environment" where the Influencers contributed by sharing only correct medical information and never fake news. We speculate that the presence of neurologists and psychologists supervising the information flow might have contributed to reduce the risk of fake news spreading and to avoid their acquisition of authoritative meaning.

Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning

Article

Jun 2018
COGNITION

Why do people believe blatantly inaccurate news headlines ("fake news")? Do we use our reasoning abilities to convince ourselves that statements that align with our ideology are true, or does reasoning allow us to effectively differentiate fake from real regardless of political ideology? Here we test these competing accounts in two studies (total N = 3446 Mechanical Turk workers) by using the Cognitive Reflection Test (CRT) as a measure of the propensity to engage in analytical reasoning. We find that CRT performance is negatively correlated with the perceived accuracy of fake news, and positively correlated with the ability to discern fake news from real news - even for headlines that align with individuals' political ideology. Moreover, overall discernment was actually better for ideologically aligned headlines than for misaligned headlines. Finally, a headline-level analysis finds that CRT is negatively correlated with perceived accuracy of relatively implausible (primarily fake) headlines, and positively correlated with perceived accuracy of relatively plausible (primarily real) headlines. In contrast, the correlation between CRT and perceived accuracy is unrelated to how closely the headline aligns with the participant's ideology. Thus, we conclude that analytic thinking is used to assess the plausibility of headlines, regardless of whether the stories are consistent or inconsistent with one's political ideology. Our findings therefore suggest that susceptibility to fake news is driven more by lazy thinking than it is by partisan bias per se - a finding that opens potential avenues for fighting fake news.