Integrating Interpersonal Communication into the Influence of Presumed Media Influence Model: Understanding Intentions to Censor and Correct COVID-19 Misinformation on Social Media

References

• Al-Zaman, M. S. (2022). Prevalence and source analysis of COVID-19 misinformation in 138 countries. IFLA Journal, 48(1), 189–204. https://doi.org/10.1177/03400352211041135
   Google Scholar
• Bamman, D., O’Connor, B., & Smith, N. (2012). Censorship and deletion practices in Chinese social media. First Monday, 17(3). https://doi.org/10.5210/fm.v17i3.3943
   Google Scholar
• Brennen, J. S., Simon, F. M., Howard, P. N., & Nielsen, R. K. (2020). Types, sources, and claims of COVID-19 misinformation. Reuters Institute for the Study of Journalism. https://ora.ox.ac.uk/objects/uuid:178db677-fa8b-491d-beda-4bacdc9d7069
   Google Scholar
• Carr, C. T., & Hayes, R. A. (2015). Social media: Defining, developing, and divining. Atlantic Journal of Communication, 23(1), 46–65. https://doi.org/10.1080/15456870.2015.972282
   Web of Science ®Google Scholar
• Chaffee, S. H. (1982). Mass media and interpersonal channels: Competitive, convergent, or complementary. In G. Gumpert & R. S. Cathcart (Eds.), Inter/media: Interpersonal communication in a media world (Vol. 10, pp. 57–77). Oxford University Press.
   Google Scholar
• Chen, Z., Su, C. C., & Chen, A. (2019). Top-down or bottom-up? A network agenda-setting study of Chinese nationalism on social media. Journal of Broadcasting & Electronic Media, 63(3), 512–533. https://doi.org/10.1080/08838151.2019.1653104
   Web of Science ®Google Scholar
• Chen, L., Wang, X., & Peng, T. Q. (2018). Nature and diffusion of gynecologic cancer–related misinformation on social media: Analysis of tweets. Journal of Medical Internet Research, 20(10), e11515. https://www.jmir.org/2018/10/e11515
   PubMed Web of Science ®Google Scholar
• Chia, S. C. (2010). How social influence mediates media effects on adolescents’ materialism. Communication Research, 37(3), 400–419. https://doi.org/10.1177/0093650210362463
   Web of Science ®Google Scholar
• Cho, H., Shen, L., & Peng, L. (2021). Examining and extending the influence of presumed influence hypothesis in social media. Media Psychology, 24(3), 413–435. https://doi.org/10.1080/15213269.2020.1729812
   PubMed Web of Science ®Google Scholar
• Chou, W. Y. S., Oh, A., & Klein, W. M. P. (2018). Addressing health-related misinformation on social media. Journal of the American Medical Association, 320(23), 2417–2418. https://doi.org/10.1001/jama.2018.16865
   PubMed Web of Science ®Google Scholar
• Cohen, J., & Weimann, G. (2008). Who’s afraid of reality shows? Exploring the effects of perceived influence of reality shows and the concern over their social effects on willingness to censor. Communication Research, 35(3), 382–397. https://doi.org/10.1177/0093650208315964
   Web of Science ®Google Scholar
• Coleman, C.-L. (1993). The influence of mass media and interpersonal communication on societal and personal risk judgments. Communication Research, 20(4), 611–628. https://doi.org/10.1177/009365093020004006
   Web of Science ®Google Scholar
• Davison, W. P. (1983). The third-person effect in communication. Public Opinion Quarterly, 47(1), 1–15. https://doi.org/10.1086/268763
   Web of Science ®Google Scholar
• Enders, A. M., Uscinski, J. E., Klofstad, C., & Stoler, J. (2020). The different forms of COVID-19 misinformation and their consequences. The Harvard Kennedy School Misinformation Review, 1(8. https://doi.org/10.37016/mr-2020-48
   Google Scholar
• Eveland, W. P., Jr., Nathanson, A. I., Detenber, B. H., & McLeod, D. M. (1999). Rethinking the social distance corollary: Perceived likelihood of exposure and the third-person effect. Communication Research, 26(3), 275–302. https://doi.org/10.1177/009365099026003001
   Web of Science ®Google Scholar
• Ghenai, A., & Mejova, Y. (2017, August 23–26). Catching Zika fever: Application of crowdsourcing and machine learning for tracking health misinformation on Twitter [ Poster presentation]. The Fifth IEEE International Conference on Healthcare Informatics, Park City, UT, United States. https://arxiv.org/abs/1707.03778
   Google Scholar
• Golan, G. J., & Lim, J. S. (2016). Third person effect of ISIS’s recruitment propaganda: Online political self-efficacy and social media activism. International Journal of Communication, 10, 4681–4701. https://ijoc.org/index.php/ijoc/article/viewFile/5551/1792
   Web of Science ®Google Scholar
• Green, A. (2020). Li Weiliang. The Lancet, 395(10225), 682–782. https://doi.org/10.1016/S0140-6736(20)30382-2
   Google Scholar
• Gui, X., Kou, Y., Pine, K. H., & Chen, Y. (2017). Managing uncertainty: Using social media for risk assessment during a public health crisis. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, Denver, CO, United States, 4520–4533. https://doi.org/10.1145/3025453.3025891
   Google Scholar
• Gunther, A. C. (1995). Overrating the X-rating: The third-person perception and support for censorship of pornography. Journal of Communication, 45(1), 27–38. https://doi.org/10.1111/j.1460-2466.1995.tb00712.x
   Web of Science ®Google Scholar
• Gunther, A. C. (1998). The persuasive press inference: Effects of mass media on perceived public opinion. Communication Research, 25(5), 486–504. https://doi.org/10.1177/009365098025005002
   Web of Science ®Google Scholar
• Gunther, A. C., Bolt, D., Borzekowski, D. L., Liebhart, J. L., & Dillard, J. P. (2006). Presumed influence on peer norms: How mass media indirectly affect adolescent smoking. Journal of Communication, 56(1), 52–68. https://doi.org/10.1177/009365098025005002
   Web of Science ®Google Scholar
• Gunther, A. C., & Storey, J. D. (2003). The influence of presumed influence. Journal of Communication, 53(2), 199–215. https://doi.org/10.1111/j.1460-2466.2003.tb02586.x
   Web of Science ®Google Scholar
• Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed.). The Guilford Press.
   Google Scholar
• Heider, F. (1958). The psychology of interpersonal relations. Wiley.
   Google Scholar
• Ho, S. S., Goh, T. J., Chuah, A. S. F., Leung, Y. W., Bekalu, M. A., & Viswanath, K. (2020). Past debates, fresh impact on nano-enabled food: A multigroup comparison of presumed media influence model based on spillover effects of attitude toward genetically modified food. Journal of Communication, 70(4), 598–621. https://doi.org/10.1093/joc/jqaa019
   Web of Science ®Google Scholar
• Ho, S. S., Scheufele, D. A., & Corley, E. A. (2011). Factors influencing public risk-benefit considerations of nanotechnology: Assessing the effects of mass media, interpersonal communication, and elaborative processing. Public Understanding of Science, 22(5), 606–623. https://doi.org/10.1177/0963662511417936
   PubMed Web of Science ®Google Scholar
• Hoffner, C., & Buchanan, M. (2002). Parents’ responses to television violence: The third-person perception, parental mediation, and support for censorship. Media Psychology, 4(3), 231–252. https://doi.org/10.1207/S1532785XMEP0403_02
   Web of Science ®Google Scholar
• Hong, Y., & Kim, S. (2020). Influence of presumed media influence for health prevention: How mass media indirectly promote health prevention behaviors through descriptive norms. Health Communication, 35(14), 1800–1810. https://doi.org/10.1080/10410236.2019.1663585
   PubMed Web of Science ®Google Scholar
• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
   Web of Science ®Google Scholar
• Jamison, A., Broniatowski, D. A., Smith, M. C., Parikh, K. S., Malik, A., Dredze, M., & Quinn, S. C. (2020). Adapting and extending a typology to identify vaccine misinformation on Twitter. American Journal of Public Health, 110(S3), S331–S339. https://doi.org/10.2105/AJPH.2020.305940
   PubMedGoogle Scholar
• Jang, S. M., & Kim, J. K. (2018). Third person effects of fake news: Fake news regulation and media literacy interventions. Computers in Human Behavior, 80, 295–302. https://doi.org/10.1016/j.chb.2017.11.034
   Web of Science ®Google Scholar
• Katz, E., & Lazarsfeld, P. F. (1955). Personal influence: The part played by people in the flow of mass communications. The Free Press.
   Google Scholar
• Kim, H. K., Ahn, J., Atkinson, L., & Kahlor, L. A. (2020). Effects of COVID-19 misinformation on information seeking, avoidance, and processing: A multicountry comparative study. Science Communication, 42(5), 586–615. https://doi.org/10.1177/1075547020959670
   PubMed Web of Science ®Google Scholar
• Leskin, P. (2020, March 31). One of the internet’s oldest fact-checking organizations is overwhelmed by coronavirus misinformation — And it could have deadly consequences. Business Insider, https://www.businessinsider.com/coronavirus-snopes-misinformation-fact-checking-overwhelmed-deadly-consequences-2020-3
   Google Scholar
• Lim, J. S. (2017). The third-person effect of online advertising of cosmetic surgery: A path model for predicting restrictive versus corrective actions. Journalism & Mass Communication Quarterly, 94(4), 972–993. https://doi.org/10.1177/1077699016687722
   Web of Science ®Google Scholar
• McLeod, D. M., Eveland, W. P., Jr, & Nathanson, A. I. (1997). Support for censorship of violent and misogynic rap lyrics: An analysis of the third-person effect. Communication Research, 24(2), 153–174. https://doi.org/10.1177/009365097024002003
   Web of Science ®Google Scholar
• Nathanson, A. I. (1999). Identifying and explaining the relationship between parental mediation and children’s aggression. Communication Research, 26(2), 124–143. https://doi.org/10.1177/009365099026002002
   Web of Science ®Google Scholar
• Oyeyemi, S. O., Gabarron, E., & Wynn, R. (2014). Ebola, Twitter, and misinformation: A dangerous combination? The BMJ, 349(oct14 5), g6178. https://doi.org/10.1136/bmj.g6178
   PubMedGoogle Scholar
• Paek, H. J., & Gunther, A. C. (2007). How peer proximity moderates indirect media influence on adolescent smoking. Communication Research, 34(4), 407–432. https://doi.org/10.1177/0093650207302785
   Web of Science ®Google Scholar
• Park, S. Y. (2005). The influence of presumed media influence on women’s desire to be thin. Communication Research, 32(5), 594–614. https://doi.org/10.1177/0093650205279350
   Web of Science ®Google Scholar
• Reardon, K. K., & Rogers, E. M. (1988). Interpersonal versus mass media communication a false dichotomy. Human Communication Research, 15(2), 284–303. https://doi.org/10.1111/j.1468-2958.1988.tb00185.x
   Web of Science ®Google Scholar
• Rojas, H., Shah, D. V., & Faber, R. J. (1996). For the good of others: Censorship and the third-person effect. International Journal of Public Opinion Research, 8(2), 163–186. https://doi.org/10.1093/ijpor/8.2.163
   Web of Science ®Google Scholar
• Salwen, M. B., & Driscoll, P. D. (1997). Consequences of third-person perception in support of press restrictions in the O. J. Simpson Trial. Journal of Communication, 47(2), 60–78. https://academic.oup.com/joc/article/47/2/60/4160305
   Google Scholar
• Scheufele, D. A. (2001). Democracy for some? How political talk both informs and polarizes the electorate. In R. P. Hart & D. Shaw (Eds.), Communication and U.S. elections: New agendas (pp. 19–32). Rowman & Littlefield.
   Google Scholar
• Southwell, B. G., & Yzer, M. C. (2007). The roles of interpersonal communication in mass media campaigns. Annals of the International Communication Association, 31(1), 420–462. https://doi.org/10.1080/23808985.2007.11679072
   Google Scholar
• Ştefăniţă, O., Corbu, N., & Buturoiu, R. (2018). Fake news and the third-person effect: They are more influenced than me and you. Journal of Media Research, 11(3), 5–23. https://doi.org/10.24193/jmr.32.1
   Google Scholar
• Sun, Y., Chia, S. C., Lu, F., & Oktavianus, J. (2022). The battle is on: Factors that motivate people to combat anti-vaccine misinformation. Health Communication, 37(3), 327–336. https://doi.org/10.1080/10410236.2020.1838108
   PubMed Web of Science ®Google Scholar
• Sun, Y., Shen, L., & Pan, Z. (2008). On the behavioral component of the third-person effect. Communication Research, 35(2), 257–278. https://doi.org/10.1177/0093650207313167
   Web of Science ®Google Scholar
• Tal-Or, N., Tsfati, Y., & Gunther, A. C. (2009). The influence of presumed media influence: Origins and implications of the third-person perception. In L. Nabi & M. B. Oliver (Eds.), The SAGE handbook of media processes and effects (pp. 99–112). SAGE Publications.
   Google Scholar
• Tsang, S. J., & Rojas, H. (2020). Opinion leaders, perceived media hostility and political participation. Communication Studies, 71(5), 753–767. https://doi.org/10.1080/10510974.2020.1791203
   Web of Science ®Google Scholar
• Viswanath, K., Lee, E. W. J., & Pinnamaneni, R. (2020). We need the lens of equity in COVID-19 communication. Health Communication, 35(14), 1743–1746. https://doi.org/10.1080/10410236.2020.1837445
   PubMed Web of Science ®Google Scholar
• Wheaton, B., Muthén, B., Alwin, D. F., & Summers, G. (1977). Assessing reliability and stability in panel models. Sociological Methodology, 80, 84–136. https://doi.org/10.2307/270754
   Google Scholar
• World Health Organization. (2020a, February 1). Novel coronavirus (2019-nCoV) situation report – 12. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200201-sitrep-12-ncov.pdf?sfvrsn=273c5d35_2
   Google Scholar
• World Health Organization. (2020b, February 15). Coronavirus disease 2019 (COVID-19). situation report – 26, https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200215-sitrep-26-covid-19.pdf?sfvrsn=a4cc6787_2
   Google Scholar