THE DISINFODEMIC MITIGATION STRATEGY OF MAFINDO IN INDONESIA
DOI:
https://doi.org/10.53625/ijss.v1i6.1903Keywords:
Hoax Impact Mitigation Strategy COVID-19 MAFINDO DisinfodemicAbstract
Social media is usually utilized for literacy and education in articulating ideas and criticism. This study focused on the utilization of social media in the community during the COVID-19 outbreak, the implications for critical political awareness, and the wise use of social media. The data used was a compilation of hoax data circulating in Indonesia from independent fact-checking sites, government-run fact-checking sites, and fact-checking channels created by the mainstream media. The results revealed that the strong political polarization among the people was the cause of the massive spread of false news and hate speech during the COVID-19 outbreak. This study also described the mitigation strategy of MAFINDO to improve information security in facing an event that leads to an information crisis during the COVID-19 pandemic. Therefore, disinfodemic mitigation strategy was constructed as part of an early warning system in increasing public immunity against disinfodemic and controlling the spread of public suggestions and dangerous reactions. This study can be used an early warning system to prevent health misinformation by revitalizing the movement of the anti hoax community in online and offline education.
References
Allcott, H., & Gentzkow, M. (2017). Social Media and Fake News in the 2016 Election. Journal of Economic Perspectives, 31(2), 211–236. https://doi.org/10.1257/jep.31.2.211
Appling, D. S., Briscoe, E. J., & Hutto, C. J. (2015). Discriminative Models for Predicting Deception Strategies. Proceedings of the 24th International Conference on World Wide Web (WWW ’15 Companion), 947–952. https://doi.org/doi.org/10.1145/2740908.2742575
Belshaw, D. (2011). What is digital literacy? A Pragmatic investigation. Durham University.
Brainard, J., & Hunter, P. (2020). Misinformation making a disease outbreak worse: outcomes compared for influenza, monkeypox, and norovirus. Simulation, 96(4), 365–374. https://doi.org/10.1177/0037549719885021
Bronstein, M. V, Pennycook, G., Bear, A., Rand, D. G., & Cannon, T. D. (2019). Belief in Fake News is Associated with Delusionality, Dogmatism, Religious Fundamentalism, and Reduced Analytic Thinking. Journal of Applied Research in Memory and Cognition, 8(1), 108–117. https://doi.org/https://doi.org/10.1016/j.jarmac.2018.09.005
Chandra, A., Acosta, J., Meredith, L., Sanches, K., Howard, S., Uscher-Pines, L., … Yeung, D. (2010). Understanding Community Resilience in the Context of National Health Security: A Literature Review (No. WR-737-DHHS). https://doi.org/10.7249/WR737
Ding, H., & Zhang, J. (2010). Social Media and Participatory Risk Communication during the H1N1 Flu Epidemic: A Comparative Study of the United States and China. China Media Research, 6(4), 80–91.
Gong, B., Zhang, S., Yuan, L., & Chen, K. Z. (2020). A balance act: minimizing economic loss while controlling novel coronavirus pneumonia. Journal of Chinese Governance, 5(2), 249–268. https://doi.org/10.1080/23812346.2020.1741940
Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., & Lazer, D. (2019). Fake news on Twitter during the 2016 U.S. presidential election. Science, 363(6425), 374 LP – 378. https://doi.org/10.1126/science.aau2706
Heryanto, G. (2017). Bisnis Hoaks dan Literasi Digital. Retrieved May 29, 2019, from mediaindonesia.com/read/detail/120440-bisnis-hoaks-dan-literasi-digital
Hidayah, N. (2018). Siskamling Digital: Melawan Intoleransi Melalui Gerakan Anti Hoaks. Ar-Risalah, 16(2), 88–98.
Hua, T., So’od, S., & Hamid, B. (2019). Communicating insults in cyberbullying. SEARCH Journal of Media and Communication Research, 11(3), 91–109.
Jang, K., & Baek, Y. M. (2019). When Information from Public Health Officials is Untrustworthy: The Use of Online News, Interpersonal Networks, and Social Media during the MERS Outbreak in South Korea. Health Communication, 34(9), 991–998. https://doi.org/10.1080/10410236.2018.1449552
Kedar, H. E. (2020). Fake News in Media Art: Fake News as a Media Art Practice Vs. Fake News in Politics. Postdigital Science and Education, 2(1), 132–146. https://doi.org/10.1007/s42438-019-00053-y
Lazard, A. J., Scheinfeld, E., Bernhardt, J. M., Wilcox, G. B., & Suran, M. (2015). Detecting themes of public concern: A text mining analysis of the Centers for Disease Control and Prevention’s Ebola live Twitter chat. American Journal of Infection Control, 43(10), 1109–1111. https://doi.org/https://doi.org/10.1016/j.ajic.2015.05.025
Lin, L., McCloud, R. F., Bigman, C. A., & Viswanath, K. (2016). Tuning in and catching on? Examining the relationship between pandemic communication and awareness and knowledge of MERS in the USA. Journal of Public Health, 39(2), 282–289. https://doi.org/10.1093/pubmed/fdw028
MacKenzie, A., & Bhatt, I. (2020). Lies, Bullshit and Fake News: Some Epistemological Concerns. Postdigital Science and Education, 2(1), 9–13. https://doi.org/10.1007/s42438-018-0025-4
Ministry of Health. (2019). Promkes.kemkes.go.id. Retrieved June 2, 2020, from promkes.kemkes.go.id
Mustapha, L., Omar, B., & Atoloye, S. (2018). Influence of satirical mediacontent on orientation to politics among Nigerian youth. SEARCH Journal of Media and Communication Research, 11(2), 91–110.
Nugroho, S. (2020). White Paper Mitigation and Management of COVID-19 Hoax in Indonesia. Jakarta: MAFINDO Presidium.
Oh, S.-H., Lee, S. Y., & Han, C. (2020). The Effects of Social Media Use on Preventive Behaviors during Infectious Disease Outbreaks: The Mediating Role of Self-relevant Emotions and Public Risk Perception. Health Communication, 1–10. https://doi.org/10.1080/10410236.2020.1724639
Oh, S.-H., Paek, H.-J., & Hove, T. (2015). Cognitive and emotional dimensions of perceived risk characteristics, genre-specific media effects, and risk perceptions: the case of H1N1 influenza in South Korea. Asian Journal of Communication, 25(1), 14–32. https://doi.org/10.1080/01292986.2014.989240
Ozbay, F., & Alatas, B. (2020). Fake news detection within online social media using supervised artificial intelligence algorithms. Physica A: Statistical Mechanics and Its Applications, 540, 1–17. https://doi.org/10.1016/j.physa.2019.123174
Parmer, J., Baur, C., Eroglu, D., Lubell, K., Prue, C., Reynolds, B., & Weaver, J. (2016). Crisis and Emergency Risk Messaging in Mass Media News Stories: Is the Public Getting the Information They Need to Protect Their Health? Health Communication, 31(10), 1215–1222. https://doi.org/10.1080/10410236.2015.1049728
Pennycook, G., & Rand, D. G. (2019). Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition, 188, 39–50. https://doi.org/https://doi.org/10.1016/j.cognition.2018.06.011
Posetti, J., & Bontcheva, K. (2020). Disinfodemic Deciphering COVID-19 disinformation: a Policy Brief 1. Paris.
Potthast, M., Kiesel, J., Reinartz, K., Bevendorff J, & Stein, B. (2018). A Stylometric Inquiry into Hyperpartisan and Fake News. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 231–240. https://doi.org/10.18653/v1/P18-1022
Rahmaniah, S., & Rupita, H. (2020). Stop Hoax Indonesia : Digital Literacy and Education in Prevent Hoax and Hate Speech in the Regional Head Election of West Kalimantan 2020. Talent and Development and Excellence, 12(2s), 1266–1274.
Reynolds, B., & Seeger, W. (2005). Crisis and Emergency Risk Communication as an Integrative Model. Journal of Health Communication, 10(1), 43–55. https://doi.org/10.1080/10810730590904571
Rubin, V. L., Chen, Y., & Conroy, N. K. (2015). Deception detection for news: Three types of fakes. Proceedings of the Association for Information Science and Technology, 52(1), 1–4. https://doi.org/10.1002/pra2.2015.145052010083
Silva, R. M., Santos, R. L. S., Almeida, T. A., & Pardo, T. A. S. (2020). Towards automatically filtering fake news in Portuguese. Expert Systems with Applications, 146, 113199. https://doi.org/https://doi.org/10.1016/j.eswa.2020.113199
Toppenberg-Pejcic, D., Noyes, J., Allen, T., Alexander, N., Vanderford, M., & Gamhewage, G. (2019). Emergency Risk Communication: Lessons Learned from a Rapid Review of Recent Gray Literature on Ebola, Zika, and Yellow Fever. Health Communication, 34(4), 437–455. https://doi.org/10.1080/10410236.2017.1405488
Tsaniyah, N., & Juliana, K. (2019). Literasi Digital Sebagai Upaya Menangkal Hoaks di Era Disrupsi. Al-Balagh: Jurnal Dakwah Dan Komunikasi, 4(1), 121–140. https://doi.org/10.22515/balagh.v4i1.1555
Turnbackhoax.id. (2020a). Salah: Belajar dari Rumah TVRI Murid Dicekoki Mimbar Katolik. Retrieved March 30, 2020, from turnbackhoax.id/2020/04/16/salah-belajar-dari-rumah-tvri-murid-dicekoki-mimbar-katolik/
Turnbackhoax.id. (2020b). Salah: Vietnam Tidak Ada Korban Meninggal COVID-19 karena Teh dan Lemon. Retrieved March 30, 2020, from turnbackhoax.id/2020/05/02/salah-vietnam-tidak-ada-korban-meninggal-covid-19-karena-teh-dan-lemon/
Turnbackhoax.id. (2020c). Turnbackhoax.id. Retrieved March 3, 2020, from turnbackhoax.id
Urman, A. (2019). Context matters: political polarization on Twitter from a comparative perspective. Media, Culture & Society, 0163443719876541. https://doi.org/10.1177/0163443719876541
van der Linden, S., Panagopoulos, C., & Roozenbeek, J. (2020). You are fake news: political bias in perceptions of fake news. Media, Culture & Society, 42(3), 460–470. https://doi.org/10.1177/0163443720906992
Van Heekeren, M. (2020). The Curative Effect of Social Media on Fake News: A Historical Re-evaluation. Journalism Studies, 21(3), 306–318. https://doi.org/10.1080/1461670X.2019.1642136
Wen, G., L., S. R., & M., M. L. (2013). Factors influencing consumers’ online shopping in China. Journal of Asia Business Studies, 7(3), 214–230. https://doi.org/10.1108/JABS-02-2013-0006
Würz, A., Nurm, Ü.-K., & Ekdahl, K. (2013). Enhancing the Role of Health Communication in the Prevention of Infectious Diseases. Journal of Health Communication, 18(12), 1566–1571. https://doi.org/10.1080/10810730.2013.840698
Zhao, Z., Zhao, J., Sano, Y., Levy, O., Takayasu, H., Takayasu, M., Havlin, S. (2020). Fake news propagates differently from real news even at early stages of spreading. EPJ Data Science, 9(1), 7. https://doi.org/10.1140/epjds/s13688-020-00224-z
