Why do People Share Misinformation during the COVID-19 Pandemic?

The World Health Organization have emphasised that misinformation - spreading rapidly through social media - poses a serious threat to the COVID-19 response. Drawing from theories of health perception and cognitive load, we develop and test a research model hypothesizing why people share unverified COVID-19 information through social media. Our findings suggest a person's trust in online information and perceived information overload are strong predictors of unverified information sharing. Furthermore, these factors, along with a person's perceived COVID-19 severity and vulnerability influence cyberchondria. Females were significantly more likely to suffer from cyberchondria, however, males were more likely to share news without fact checking their source. Our findings suggest that to mitigate the spread of COVID-19 misinformation and cyberchondria, measures should be taken to enhance a healthy skepticism of health news while simultaneously guarding against information overload.

[1]  R. W. Rogers,et al.  Protection Motivation Theory and preventive health: beyond the Health Belief Model , 1986 .

[2]  P. Chandler,et al.  Cognitive Load Theory and the Format of Instruction , 1991 .

[3]  Izak Benbasat,et al.  Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation , 1991, Inf. Syst. Res..

[4]  Detmar W. Straub,et al.  Structural Equation Modeling and Regression: Guidelines for Research Practice , 2000, Commun. Assoc. Inf. Syst..

[5]  R. M. Wolfe,et al.  Vaccine safety activists on the Internet , 2002, Expert review of vaccines.

[6]  F. Paas,et al.  Cognitive Load Measurement as a Means to Advance Cognitive Load Theory , 2003 .

[7]  Charles Abraham,et al.  The health belief model , 2005 .

[8]  Matthew S. Eastin,et al.  Worried and Wired: Effects of Health Anxiety on Information-Seeking and Health Care Utilization Behaviors , 2006, Cyberpsychology Behav. Soc. Netw..

[9]  Maria E. Fernandez,et al.  Planning Health Promotion Programs: An Intervention Mapping Approach , 2006 .

[10]  Tania Lewis,et al.  Seeking health information on the internet: lifestyle choice or bad attack of cyberchondria? , 2006 .

[11]  Kathryn Ley,et al.  A Learning Strategy to Compensate for Cognitive Overload in Online Learning: Learner Use of Printed Online Materials , 2006 .

[12]  Qing Hu,et al.  Assimilation of Enterprise Systems: The Effect of Institutional Pressures and the Mediating Role of Top Management , 2007, MIS Q..

[13]  Atreyi Kankanhalli,et al.  Studying users' computer security behavior: A health belief perspective , 2009, Decis. Support Syst..

[14]  Ryen W. White,et al.  Cyberchondria: Studies of the escalation of medical concerns in Web search , 2009, TOIS.

[15]  C. Tufanaru,et al.  Health Belief Model , 2009 .

[16]  Hsinchun Chen,et al.  Social Media Analytics and Intelligence , 2010, IEEE Intell. Syst..

[17]  S. Michie,et al.  Demographic and attitudinal determinants of protective behaviours during a pandemic: A review , 2010, British journal of health psychology.

[18]  Holly Seale,et al.  University life and pandemic influenza: Attitudes and intended behaviour of staff and students towards pandemic (H1N1) 2009 , 2010, BMC public health.

[19]  A. Kata.  A postmodern Pandora's box: anti-vaccination misinformation on the Internet. , 2010, Vaccine.

[20]  Jennifer J. Vogel-Walcutt,et al.  Cognitive load theory vs. constructivist approaches: which best leads to efficient, deep learning? , 2011, J. Comput. Assist. Learn..

[21]  John Sweller,et al.  Cognitive Load Theory , 2020, Encyclopedia of Education and Information Technologies.

[22]  Julita Vassileva,et al.  Towards an Effective Health Interventions Design: An Extension of the Health Belief Model , 2012, Online journal of public health informatics.

[23]  David Berle,et al.  Cyberchondria: towards a better understanding of excessive health-related Internet use , 2013, Expert review of neurotherapeutics.

[24]  Shuting Xu,et al.  Applying Protection Motivation Theory to Information Security Training for College Students , 2013 .

[25]  Miriam J. Metzger,et al.  Credibility and trust of information in online environments: The use of cognitive heuristics , 2013 .

[26]  Kasisomayajula Viswanath,et al.  What have we learned about communication inequalities during the H1N1 pandemic: a systematic review of the literature , 2014, BMC Public Health.

[27]  Stan Karanasios,et al.  Information sharing and interoperability: the case of major incident management , 2014, Eur. J. Inf. Syst..

[28]  Sven Laumer,et al.  Giving too much social support: social overload on social networking sites , 2015, Eur. J. Inf. Syst..

[29]  M. E. Oglesby,et al.  Anxiety sensitivity and intolerance of uncertainty as potential risk factors for cyberchondria. , 2015, Journal of affective disorders.

[30]  Sei-Ching Joanna Sin,et al.  Why Students Share Misinformation on Social Media: Motivation, Gender, and Study-level Differences , 2015 .

[31]  Lada A. Adamic,et al.  Exposure to ideologically diverse news and opinion on Facebook , 2015, Science.

[32]  Katarzyna Samson,et al.  Effects of Cognitive Load on Trusting Behavior – An Experiment Using the Trust Game , 2015, PloS one.

[33]  Kate Starbird,et al.  Connected Through Crisis: Emotional Proximity and the Spread of Misinformation Online , 2015, CSCW.

[34]  David Berle,et al.  Cyberchondria: An old phenomenon in a new guise? , 2015 .

[35]  Pardis Pourghomi,et al.  Corporate Responsibility in Combating Online Misinformation , 2016 .

[36]  Matti Mäntymäki,et al.  The Janus face of Facebook: Positive and negative sides of social networking site use , 2016, Comput. Hum. Behav..

[37]  B. Fallon,et al.  Cyberchondria: Parsing Health Anxiety From Online Behavior. , 2016, Psychosomatics.

[38]  G. Caldarelli,et al.  The spreading of misinformation online , 2016, Proceedings of the National Academy of Sciences.

[39]  Ullrich K. H. Ecker,et al.  Beyond Misinformation: Understanding and coping with the post-truth era , 2017 .

[40]  M. Gentzkow,et al.  Social Media and Fake News in the 2016 Election , 2017 .

[41]  Daniel J. Levitin,et al.  Weaponized lies : how to think critically in the post-truth era , 2017 .

[42]  Dominic Spohr.  Fake news and ideological polarization , 2017 .

[43]  Fang Chen,et al.  Effects of Uncertainty and Cognitive Load on User Trust in Predictive Decision Making , 2017, INTERACT.

[44]  T. Fergus,et al.  Cyberchondria: Examining relations with problematic Internet use and metacognitive beliefs. , 2017, Clinical psychology & psychotherapy.

[45]  Suhang Wang,et al.  Fake News Detection on Social Media: A Data Mining Perspective , 2017, SKDD.

[46]  W. Nuland,et al.  Information operations and Facebook , 2017 .

[47]  Jaime E. Settle.  Frenemies: How Social Media Polarizes America , 2018 .

[48]  Kendra Auberry,et al.  Increasing students’ ability to identify fake news through information literacy education and content management systems , 2018, The Reference Librarian.

[49]  Sonnet Ireland,et al.  Fake news alerts: Teaching news literacy skills in a meme world , 2018 .

[50]  Miriam J. Metzger,et al.  The science of fake news , 2018, Science.

[51]  Alton Yeow-Kuan Chua,et al.  Intentions to trust and share online health rumors: An experiment with medical professionals , 2018, Comput. Hum. Behav..

[52]  Sinan Aral,et al.  The spread of true and false news online , 2018, Science.

[53]  A. Norr,et al.  Cyberchondria: Overlap with health anxiety and unique relations with impairment, quality of life, and service utilization , 2018, Psychiatry Research.

[54]  Xiongfei Cao,et al.  Exploring the effect of overload on the discontinuous intention of social media users: An S-O-R perspective , 2018, Comput. Hum. Behav..

[55]  Andrew Chadwick,et al.  News sharing on UK social media: misinformation, disinformation, and correction , 2019 .

[56]  Mathew Ling,et al.  Predicting intention to receive a seasonal influenza vaccination using Protection Motivation Theory. , 2019, Social science & medicine.

[57]  Alan R. Dennis,et al.  Says Who? The Effects of Presentation Format and Source Rating on Fake News in Social Media , 2019, MIS Q..

[58]  Puneet Kaur,et al.  Why do people share fake news? Associations between the dark side of social media use and fake news sharing behavior , 2019, Journal of Retailing and Consumer Services.

[59]  M. Laeeq Khan,et al.  Recognise misinformation and verify before sharing: a reasoned action and information literacy perspective , 2019, Behav. Inf. Technol..

[60]  David Berle,et al.  The relationships between health anxiety, online health information seeking, and cyberchondria: Systematic review and meta-analysis. , 2019, Journal of affective disorders.

[61]  J. Zarocostas.  How to fight an infodemic , 2020, The Lancet.

[62]  Yaqing Fang,et al.  Transmission dynamics of the COVID‐19 outbreak and effectiveness of government interventions: A data‐driven analysis , 2020, Journal of medical virology.

[63]  A. Wilder-Smith,et al.  Isolation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak , 2020, Journal of travel medicine.

[64]  A. L. Schmidt,et al.  The COVID-19 social media infodemic , 2020, Scientific Reports.

[65]  Izak Benbasat,et al.  What drives subscribing to premium in freemium services? A consumer value‐based view of differences between upgrading to and staying with premium , 2020, Inf. Syst. J..

[66]  Eoin Whelan,et al.  Applying the SOBC paradigm to explain how social media overload affects academic performance , 2020, Comput. Educ..

[67]  Jessica T Davis,et al.  The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak , 2020, Science.

[68]  Eoin Whelan,et al.  Is boredom proneness related to social media overload and fatigue? A stress-strain-outcome approach , 2020, Internet Res..

[69]  Yan Bai,et al.  Presumed Asymptomatic Carrier Transmission of COVID-19. , 2020, JAMA.