The effects of SNS communication: How expressing and receiving information predict MERS-preventive behavioral intentions in South Korea

Abstract Individuals use social network sites (SNSs) as an effective tool for communicating relevant information with others during the outbreak of infectious diseases. However, little is known about the underlying mechanism through which communicative behaviors influence preventive behaviors. Thus, in the context of Middle East respiratory syndrome (MERS) in South Korea, this study investigated how two communicative behaviors (message expression and reception) in SNSs affected the communicators’ intentions to engage in MERS-preventive behaviors. Using data collected from a nationally representative panel survey of 1000 Korean adults aged 19 or older, we examined a theoretical expression and reception effects model. Results support the presence of effects from expressing and receiving MERS-related information via SNSs and their underlying mechanism during South Korea’s MERS outbreak. Public health officials and communication professionals should actively use SNS communication in coping with public health crisis caused by emerging infectious diseases.

[1]  S. Shyam Sundar,et al.  Theorizing Interactivity's Effects , 2004, Inf. Soc..

[2]  R. Hornik,et al.  Can We Measure Encoded Exposure? Validation Evidence From a National Campaign , 2002, Journal of health communication.

[3]  Blackford Middleton,et al.  Communicating Health Information to an Alarmed Public Facing a Threat Such as a Bioterrorist Attack , 2004, Journal of health communication.

[4]  Yan Jin,et al.  Examining the Role of Social Media in Effective Crisis Management , 2014, Commun. Res..

[5]  M. Fishbein,et al.  The Role of Theory in Developing Effective Health Communications , 2006 .

[6]  L. Gideon Handbook of Survey Methodology for the Social Sciences , 2012 .

[7]  Tara L Crowell,et al.  Examining condom use self‐efficacy and coping in sexual situations , 2000 .

[8]  Marjorie M. Buckner,et al.  Social Media Messages in an Emerging Health Crisis: Tweeting Bird Flu , 2016, Journal of health communication.

[9]  J. Pennebaker Confession, Inhibition, and Disease , 1989 .

[10]  T. Robinson,et al.  Effects of the SMART Classroom Curriculum to Reduce Child and Family Screen Time , 2006 .

[11]  Lex van Velsen,et al.  Should Health Organizations Use Web 2.0 Media in Times of an Infectious Disease Crisis? An In-depth Qualitative Study of Citizens’ Information Behavior During an EHEC Outbreak , 2012, Journal of medical Internet research.

[12]  Ming Wang,et al.  Attitudes, practices and information needs regarding novel influenza A (H7N9) among employees of food production and operation in Guangzhou, Southern China: a cross-sectional study , 2014, BMC Infectious Diseases.

[13]  M. Defleur,et al.  A Dependency Model of Mass-Media Effects , 1976 .

[14]  Jeff Niederdeppe,et al.  Validating Measures of Scanned Information Exposure in the Context of Cancer Prevention and Screening Behaviors , 2009, Journal of health communication.

[15]  K Witte,et al.  Fear, threat, and perceptions of efficacy from frightening skin cancer messages. , 1998, Public health reviews.

[16]  Wanda Siu,et al.  Extended Parallel Process Model and H5N1 Influenza Virus , 2008, Psychological reports.

[17]  Louis Leung,et al.  User-generated content on the internet: an examination of gratifications, civic engagement and psychological empowerment , 2009, New Media Soc..

[18]  Cliff Lampe,et al.  It's Complicated: Facebook Users' Political Participation in the 2008 Election , 2011, Cyberpsychology Behav. Soc. Netw..

[19]  Dhavan V. Shah,et al.  The effects of expression: how providing emotional support online improves cancer patients' coping strategies. , 2013, Journal of the National Cancer Institute. Monographs.

[20]  Dhavan V. Shah,et al.  Campaign Ads, Online Messaging, and Participation: Extending the Communication Mediation Model , 2007, Journal of Communication.

[21]  Elmie Nekmat,et al.  Message Expression Effects in Online Social Communication , 2012, Journal of Broadcasting & Electronic Media.

[22]  J. P. Eveland The Effect of Political Discussion in Producing Informed Citizens: The Roles of Information, Motivation, and Elaboration , 2004 .

[23]  Ilwoo Ju,et al.  Prescription drug advertising, disease knowledge, and older adults’ optimistic bias about the future risk of alzheimer’s disease , 2016, Health communication.

[24]  E. Katz,et al.  The Uses of Mass Communications: Current Perspectives on Gratifications Research. Sage Annual Reviews of Communication Research Volume III. , 1975 .

[25]  R. W. Rogers,et al.  A Protection Motivation Theory of Fear Appeals and Attitude Change1. , 1975, The Journal of psychology.

[26]  Steven Prentice-Dunn,et al.  Protection Motivation Theory and Skin Cancer Risk: The Role of Individual Differences in Responses to Persuasive Appeals , 2005 .

[27]  Ying Kong,et al.  Media Use and Health Behavior in H1N1 Flu Crisis: The Mediating Role of Perceived Knowledge and Fear , 2015 .

[28]  Sora Kim,et al.  How organizations framed the 2009 H1N1 pandemic via social and traditional media: Implications for U , 2011 .

[29]  T. Chock,et al.  Effects of Hiv/aids Public Service Announcements on Attitude and Behavior: Interplay of Perceived Threat and Self-Efficacy , 2014 .

[30]  Emily M. Douglas,et al.  The effects of survey administration on disclosure rates to sensitive items among men: A comparison of an internet panel sample with a RDD telephone sample , 2010, Comput. Hum. Behav..

[31]  J. Pennebaker Writing About Emotional Experiences as a Therapeutic Process , 1997 .

[32]  Huiling Ding,et al.  Social Media and Participatory Risk Communication during the H1N1 Flu Epidemic: A Comparative Study of the United States and China , 2010 .

[33]  Kanayo Umeh,et al.  Cognitive appraisals, maladaptive coping, and past behaviour in protection motivation , 2004 .

[34]  A. Kaplan,et al.  Users of the world, unite! The challenges and opportunities of Social Media , 2010 .

[35]  Christine McNab,et al.  What social media offers to health professionals and citizens. , 2009, Bulletin of the World Health Organization.

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

[37]  Jingyuan Shi,et al.  The effects of fear appeal message repetition on perceived threat, perceived efficacy, and behavioral intention in the extended parallel process model , 2016, Health communication.

[38]  J. Pennebaker,et al.  Confronting a traumatic event: toward an understanding of inhibition and disease. , 1986, Journal of abnormal psychology.

[39]  G. Eysenbach Medicine 2.0: Social Networking, Collaboration, Participation, Apomediation, and Openness , 2008, Journal of medical Internet research.

[40]  Elia Gabarron,et al.  Ebola, Twitter, and misinformation: a dangerous combination? , 2014, BMJ : British Medical Journal.

[41]  Bret R. Shaw,et al.  The Effects of Expressing Religious Support Online for Breast Cancer Patients , 2016, Health communication.

[42]  Yan Jin,et al.  The Blog-Mediated Crisis Communication Model: Recommendations for Responding to Influential External Blogs , 2010 .

[43]  Jeong Yeob Han,et al.  Effects of prayer and religious expression within computer support groups on women with breast cancer , 2007, Psycho-oncology.

[44]  B. Cowling,et al.  Community psychological and behavioral responses through the first wave of the 2009 influenza A(H1N1) pandemic in Hong Kong. , 2010, The Journal of infectious diseases.

[45]  R. Dolan,et al.  Optimistic update bias increases in older age , 2013, Psychological Medicine.

[46]  Raymond J. Pingree How Messages Affect Their Senders: A More General Model of Message Effects and Implications for Deliberation , 2007 .

[47]  N. Weinstein Unrealistic optimism about susceptibility to health problems: Conclusions from a community-wide sample , 1987, Journal of Behavioral Medicine.

[48]  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.

[49]  A. Satorra,et al.  Scaled test statistics and robust standard errors for non-normal data in covariance structure analysis: a Monte Carlo study. , 1991, The British journal of mathematical and statistical psychology.

[50]  Y. T. Yang,et al.  Mining Social Media and Web Searches For Disease Detection , 2013, Journal of public health research.

[51]  C. Rudisill How do we handle new health risks? Risk perception, optimism, and behaviors regarding the H1N1 virus , 2013 .

[52]  L. Wong,et al.  Public Sources of Information and Information Needs for Pandemic Influenza A(H1N1) , 2010, Journal of Community Health.

[53]  Brooke Fisher Liu,et al.  Social media use during disasters: a review of the knowledge base and gaps. , 2012 .

[54]  B. J. Fogg,et al.  Online Persuasion in Facebook and Mixi: A Cross-Cultural Comparison , 2008, PERSUASIVE.

[55]  R. Solomon On Fate and Fatalism , 2003 .

[56]  R. Krueger,et al.  A cross-cultural study of the structure of comorbidity among common psychopathological syndromes in the general health care setting. , 2003, Journal of abnormal psychology.

[57]  A. Satorra,et al.  Corrections to test statistics and standard errors in covariance structure analysis. , 1994 .

[58]  I. Ajzen,et al.  Predicting and Changing Behavior: The Reasoned Action Approach , 2009 .

[59]  R. Rimal,et al.  Extending the Purview of the Risk Perception Attitude Framework: Findings from HIV/AIDS Prevention Research in Malawi , 2009, Health communication.

[60]  Toomas Timpka,et al.  Importance of Internet Surveillance in Public Health Emergency Control and Prevention: Evidence From a Digital Epidemiologic Study During Avian Influenza A H7N9 Outbreaks , 2014, Journal of medical Internet research.

[61]  K. Witte Fear control and danger control: A test of the extended parallel process model (EPPM) , 1994 .

[62]  Dhavan V. Shah,et al.  COMMUNICATION THEORY Communication Theory ISSN 1050-3293 ORIGINAL ARTICLE Campaigns, Reflection, and Deliberation: Advancing an O-S-R-O-R Model of Communication Effects , 2022 .

[63]  Shahedur Rahman,et al.  Examining the Role of Social Media in Disaster Management from an Attribution Theory Perspective , 2016, ISCRAM.

[64]  Thomas J. Johnson,et al.  Choosing Is Believing? How Web Gratifications and Reliance Affect Internet Credibility Among Politically Interested Users , 2010 .

[65]  William P. Eveland The Cognitive Mediation Model of Learning From the News , 2001, Commun. Res..

[66]  A. Bandura Perceived self-efficacy in the exercise of control over AIDS infection , 1990 .

[67]  Carolyn A. Lin,et al.  Communicating Food Safety via the Social Media , 2014 .

[68]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[69]  Scott L. Althaus,et al.  Please Scroll down for Article Political Communication Patterns of Internet and Traditional News Media Use in a Networked Community Patterns of Internet and Traditional News Media Use in a Networked Community , 2022 .

[70]  L. Perloff,et al.  Self–other judgments and perceived vulnerability to victimization. , 1986 .

[71]  Suzanne Pingree,et al.  Effects of Insightful Disclosure Within Computer Mediated Support Groups on Women With Breast Cancer , 2006, Health communication.

[72]  James W. Pennebaker,et al.  Opening up : the healing power of expressing emotions , 1990 .

[73]  Gustavo S. Mesch,et al.  E-Mail Surveys , 2012 .

[74]  Kate Faasse,et al.  Public Anxiety and Information Seeking Following the H1N1 Outbreak: Blogs, Newspaper Articles, and Wikipedia Visits , 2012, Health communication.

[75]  Edbert B Hsu,et al.  Characterizing hospital workers' willingness to report to duty in an influenza pandemic through threat- and efficacy-based assessment , 2010, BMC public health.

[76]  Shawn M. Bergman,et al.  Twitter versus Facebook: Exploring the role of narcissism in the motives and usage of different social media platforms , 2014, Comput. Hum. Behav..

[77]  Jeong Yeob Han,et al.  How Does Insightful and Emotional Disclosure Bring Potential Health Benefits?: Study Based on Online Support Groups for Women with Breast Cancer. , 2011, The Journal of communication.

[78]  R. Rogers Cognitive and physiological processes in fear appeals and attitude change: a revised theory of prote , 1983 .

[79]  Gerjo Kok,et al.  Perceived risk, anxiety, and behavioural responses of the general public during the early phase of the Influenza A (H1N1) pandemic in the Netherlands: results of three consecutive online surveys , 2011, BMC public health.

[80]  Alberto Maria Segre,et al.  The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic , 2011, PloS one.

[81]  Danah Boyd,et al.  Social Network Sites: Definition, History, and Scholarship , 2007, J. Comput. Mediat. Commun..

[82]  S Michie,et al.  The impact of communications about swine flu (influenza A H1N1v) on public responses to the outbreak: results from 36 national telephone surveys in the UK. , 2010, Health technology assessment.

[83]  Lucinda L. Austin,et al.  How Audiences Seek Out Crisis Information: Exploring the Social-Mediated Crisis Communication Model , 2012 .

[84]  E. Kelloway Using LISREL for Structural Equation Modeling: A Researcher′s Guide , 1998 .

[85]  J. Lunt Ethics and Security Aspects of Infectious Disease Control: Interdisciplinary Perspectives , 2013 .

[86]  K. Witte Putting the fear back into fear appeals: The extended parallel process model , 1992 .

[87]  J. Zhang,et al.  Self–Other Differences in H1N1 Flu Risk Perception in a Global Context: A Comparative Study Between the United States and China , 2014, Health communication.

[88]  Gerjo Kok,et al.  Disease Detection or Public Opinion Reflection? Content Analysis of Tweets, Other Social Media, and Online Newspapers During the Measles Outbreak in the Netherlands in 2013 , 2015, Journal of medical Internet research.

[89]  Z. J. Yang,et al.  Predicting Young Adults’ Intentions to Get the H1N1 Vaccine: An Integrated Model , 2015, Journal of health communication.

[90]  Barbara M. Byrne,et al.  Structural equation modeling with EQS : basic concepts, applications, and programming , 2000 .

[91]  S. Chaffee,et al.  Measurement and Effects of Attention to Media News , 1986 .

[92]  H. Kim,et al.  Exploring Optimistic Bias and the Integrative Model of Behavioral Prediction in the Context of a Campus Influenza Outbreak , 2013, Journal of health communication.

[93]  Cecilia Cheng,et al.  Psychosocial Factors Predicting SARS-Preventive Behaviors in Four Major SARS-Affected Regions , 2006 .

[94]  Tao Sun,et al.  Media dependencies in a changing media environment: the case of the 2003 SARS epidemic in China , 2007, New Media Soc..

[95]  Vera Hoorens,et al.  Self‐Favoring Biases, Self‐Presentation, and the Self‐Other Asymmetry in Social Comparison , 1995 .

[96]  A. Bandura Self-Efficacy: The Exercise of Control , 1997, Journal of Cognitive Psychotherapy.

[97]  J. Pennebaker,et al.  Forming a story: the health benefits of narrative. , 1999, Journal of clinical psychology.

[98]  Cheryl Campanella Bracken,et al.  Testing the Theoretical Design of a Health Risk Message: Reexamining the Major Tenets of the Extended Parallel Process Model , 2005, Health education & behavior : the official publication of the Society for Public Health Education.

[99]  G. Eysenbach,et al.  Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak , 2010, PloS one.

[100]  Gary L. Kreps,et al.  New directions in eHealth communication: opportunities and challenges. , 2010, Patient education and counseling.