Predicting the sharing and reception of covid19-related information on social media

Aurelius Ratu, Endang Susilowati, Sukriyah Kustanti Moerad

Abstract


In a health crisis, social media is one of the primary means of disseminating and receiving information. Nevertheless, questions remain regarding what facilitates individual engagement in sharing or receiving information in the context of COVID-19, specifically for the Indonesian context. Analysis of a questionnaire survey among 255 from the random population of Indonesian showed that sharing information is predicted by gender and education. There was no significant relationship between social-demographic and receiving information. For psychosocial characteristics, personal risk perception predicted receiving information, while sharing information was predicted by societal risk perception. Societal risk perception and self-efficacy interacted with each other to predict the sharing of COVID-19-related information. One of the findings indicates a socio-cultural feature through which is considered to play an important role in personal and societal risk perception when sharing or expressing information. Given the benefits of social media communication during this pandemic, this study suggested that governments and policymakers should pay closer attention to a local community that is more effective in keeping everyone safe. Unfortunately, on another side, social media provides less information about narratives, storytelling, and empathy to maintain public trust.

References


R. N. Rimal and K. Real, “Perceived risk and efficacy beliefs as motivators of change: Use of the risk perception attitude (RPA) framework to understand health behaviors,” Hum. Commun. Res., vol. 29, no. 3, pp. 370–399, 2003.

C. M. L. Wong and O. Jensen, “The paradox of trust: perceived risk and public compliance during the COVID-19 pandemic in Singapore,” J. Risk Res., vol. 0, no. 0, pp. 1–10, 2020.

D. H. P. Balog-Way and K. A. McComas, “COVID-19: Reflections on trust, tradeoffs, and preparedness,” J. Risk Res., vol. 0, no. 0, pp. 1–11, 2020.

S. H. Oh, H. J. Paek, and T. Hove, “Cognitive and emotional dimensions of perceived risk characteristics, genre-specific media effects, and risk perceptions: the case of H1N1 influenza in South Korea,” Asian J. Commun., vol. 25, no. 1, pp. 14–32, 2015.

R. I. Bjarnadottir, M. Millery, E. Fleck, and S. Bakken, “Correlates of online health information-seeking behaviors in a low-income Hispanic community,” Informatics Heal. Soc. Care, vol. 41, no. 4, pp. 341–349, 2016.

J. Y. Han et al., “Factors Associated with Use of Interactive Cancer Communication System: An Application of the Comprehensive Model of Information Seeking,” J. Comput. Commun., vol. 15, no. 3, pp. 367–388, 2010.

E. Avery, “Contextual and audience moderators of channel selection and message reception of public health information in routine and crisis situations,” J. Public Relations Res., vol. 22, no. 4, pp. 378–403, 2010.

I. Basnyat, E. Nekmat, S. Jiang, and J. Lin, “Applying the Modified Comprehensive Model of Information Seeking to Online Health Information Seeking in the Context of India,” J. Health Commun., vol. 23, no. 6, pp. 563–572, 2018.

L. Zhang, L. Xu, and W. Zhang, “Social media as amplification station: factors that influence the speed of online public response to health emergencies,” Asian J. Commun., vol. 27, no. 3, pp. 322–338, 2017.

J. D. Johnson, W. A. Donohue, C. K. Atkin, and S. Johnson, A Comprehensive Model of Information Seeking: Tests Focusing on a Technical Organization, vol. 16, no. 3. 1995.

B. E. Weeks and R. L. Holbert, “Predicting Dissemination of News Content in Social Media:A Focus on Reception, Friending, and Partisanship,” Journal. Mass Commun. Q., vol. 90, no. 2, pp. 212–232, 2013.

C. M. Pulido, B. Villarejo-Carballido, G. Redondo-Sama, and A. Gómez, “COVID-19 infodemic: More retweets for science-based information on coronavirus than for false information,” Int. Sociol., 2020.

T. K. F. Fung, K. Namkoong, and D. Brossard, “Media, social proximity, and risk: A comparative analysis of newspaper coverage of avian flu in Hong Kong and in the United States,” J. Health Commun., vol. 16, no. 8, pp. 889–907, 2011.

T. L. Sellnow, D. D. Sellnow, E. M. Helsel, J. M. Martin, and J. S. Parker, “Risk and crisis communication narratives in response to rapidly emerging diseases,” J. Risk Res., vol. 22, no. 7, pp. 897–908, 2019.

W. Yoo and D. H. Choi, “Predictors of expressing and receiving information on social networking sites during MERS-CoV outbreak in South Korea,” J. Risk Res., vol. 0, no. 0, pp. 1–16, 2019.

T. R. Tyler and F. L. Cook, “The mass media and judgments of risk: Distinguishing impact on personal and societal level judgments,” J. Pers. Soc. Psychol., vol. 47, no. 4, pp. 693–708, 1984.

X. Li, “Effects of Mass Media Exposure and Social Network Site Involvement on Risk Perception of and Precautionary Behavior Toward the Haze Issue in China,” vol. 11, pp. 3975–3997, 2017.

J. Berger and K. L. Milkman, “What makes online content viral?,” J. Mark. Res., vol. 49, no. 2, pp. 192–205, 2012.

J. Compton and M. Pfau, “Spreading inoculation: Inoculation, resistance to influence, and word-of-mouth communication,” Commun. Theory, vol. 19, no. 1, pp. 9–28, 2009.

D. K. Ahorsu, C. Y. Lin, V. Imani, M. Saffari, M. D. Griffiths, and A. H. Pakpour, “The Fear of COVID-19 Scale: Development and Initial Validation,” Int. J. Ment. Health Addict., 2020.

A. Reznik, V. Gritsenko, V. Konstantinov, N. Khamenka, and R. Isralowitz, “COVID-19 Fear in Eastern Europe: Validation of the Fear of COVID-19 Scale,” Int. J. Ment. Health Addict., 2020.

E. Ter Huurne and J. Gutteling, “Information needs and risk perception as predictors of risk information seeking,” J. Risk Res., vol. 11, no. 7, pp. 847–862, 2008.

J. Cohen, P. Cohen, S. G. West, and L. S. Aiken, Applied multiple regression/correlation analysis for the behavioral sciences, 3rd ed. Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers, 2003.

K. Giritli Nygren, A. Olofsson, and S. Öhman, “Conceptual Frames: Risk and Intersectionality,” in A Framework of Intersectional Risk Theory in the Age of Ambivalence, Palgrave Macmillan, Cham, 2020, pp. 19–36.

K. Neuwirth and E. Frederick, “Peer and social influence on opinion expression: Combining the theories of planned behavior and the spiral of silence,” Communic. Res., vol. 31, no. 6, pp. 669–703, 2004.

G. (Kevin) Han, J. (Mandy) Zhang, K. (Rebecca) Chu, and G. Shen, “Self-Other Differences in H1N1 Flu Risk Perception in a Global Context: A Comparative Study Between the United States and China,” Health Commun., vol. 29, no. 2, pp. 109–123, 2014.

M. B. Tannenbaum et al., “Appealing to Fear : A Meta-Analysis of Fear Appeal Effectiveness and Theories A Message-Behavior-Audience Framework,” vol. 141, no. 6, pp. 1178–1204, 2015.

S. Dryhurst et al., “Risk perceptions of COVID-19 around the world,” J. Risk Res., vol. 0, no. 0, pp. 1–13, 2020.

D. B. Susetyo, H. E. Widiyatmadi, and y. Sudiantara, “Konsep Self Dan Penghayatan Self Orang Jawa,” Psikodimensia, vol. 13, no. 1, p. 47, 2014.

A. P. Kurniawan and N. U. Hasanat, “Perbedaan ekspresi emosi pada beberapa tingkat generasi suku di Yogyakarta,” J. Psikol., vol. 34, no. 1, pp. 1–17, 2007.

S. Al Baqi, “Ekspresi Emosi Marah,” Bul. Psikol., vol. 23, no. 1, p. 22, 2015.

K. Giritli Nygren and A. Olofsson, “Managing the Covid-19 pandemic through individual responsibility: the consequences of a world risk society and enhanced ethopolitics,” J. Risk Res., vol. 0, no. 0, pp. 1–5, 2020.

R. Wijanarko, “Religious Populism and Public Sphere in Indonesia,” J. Sos. Hum., vol. Special Ed, pp. 1–9, Apr. 2021.




DOI: http://doi.org/10.11591/ijphs.v11i3.21117

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