An Exploration Respond of COVID-19 Policy through Social Media in Indonesia

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Bambang Irawan Mohammad Jafar Loilatu Paisal Akbar Rizky Reynaldi

Abstract

This paper explores public responses through social media to the COVID-19 policy in Indonesia; public response to the COVID-19 policy shows that information about COVID-19 is sourced from crowd sources, thus creating misinformation on health information. To answer the research purpose, this research uses NodeXL to explore policy responses through social media Twitter; data collection was carried out from 3-25 July 2021. The result shows the public response to the COVID1-19 policy in Indonesia through topic distribution on social media Twitter. From these findings, 10 topics on social media became public responses to COVID-19 policies. This topic addresses the public's response to the COVID-19 condition in Indonesia and the policies taken by the government. We classify these topics based on the characteristics of public responses that indicate certain conditions such as vaccine policies, medical device crises, hoax information, collaboration, and political conditions. However, this research has limitations on access to the data taken. Therefore, further research can explore the function of social media in post-COVID-19 policies.

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IRAWAN, Bambang et al. An Exploration Respond of COVID-19 Policy through Social Media in Indonesia. Policy & Governance Review, [S.l.], v. 6, n. 3, p. 229-246, sep. 2022. ISSN 2580-4820. Available at: <https://journal.iapa.or.id/pgr/article/view/596>. Date accessed: 03 oct. 2022. doi: https://doi.org/10.30589/pgr.v6i3.596.
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References

Ahmed, W., Bath, P. A., Sbaffi, L., & Demartini, G. (2019). Novel insights into views towards H1N1 during the 2009 Pandemic: a thematic analysis of Twitter data. Health Information and Libraries Journal, 36(1), 60–72. https:// doi.org/10.1111/hir.12247

Ahmed, W., Seguí, F. L., Vidal-Alaball, J., & Katz, M. S. (2020). COVID-19 and the “Film Your Hospital” conspiracy theory: Social network analysis of Twitter data. Journal of Medical Internet Research, 22(10). https://doi. org/10.2196/22374

Arifin, B., & Anas, T. (2021). Lessons learned from COVID-19 vaccination in Indonesia: experiences, challenges, and opportunities. Human Vaccines and Immunotherapeutics, 17(11), 3898–3906. https://doi.org/10.1080/21645515.2021.1975450

Baines, D., & Elliott, R. J. R. (2020). Defining misinformation, disinformation and malinformation: An urgent need for clarity during the COVID-19 infodemic (pp. 1–23).

Batara, E., Nurmandi, A., Warsito, T., & Pribadi, U. (2018). Are government employees adopting local e-government transformation?: The need for having the right attitude, facilitating conditions and performance expectations. Transforming Government: People, Process and Policy, 11(3), 343–376.

Belso-Martínez,J. A., Mas-Tur, A., Sánchez, M., & López-Sánchez, M. J. (2020). The COVID-19 response system and collective social service provision. Strategic network dimensions and proximity considerations. Service Business, 14(3), 387–411. https:// doi.org/10.1007/s11628-020-00421-w

Beni-hssane, A. (2017). ScienceDirect Analyzing Social Media through Big Data using InfoSphere BigInsights and Apache Flume. Procedia Computer Science, 113, 280–285. https://doi.org/10.1016/j.procs.2017.08.299

Capano, G., Howlett, M., Jarvis, D. S. L., Ramesh, M., & Goyal, N. (2020). Mobilizing Policy (In) Capacity to Fight COVID-19: Understanding Variations in State Responses. Policy and Society, 39(3), 285–308. https://doi.org/10.1080/14494035.2020.1787628

Chair, S., Charrad, M., & Ben Saoud, N. B. (2019). Towards A Social Media-Based Framework for Disaster Communication. Procedia Computer Science, 164, 271–278. https:// doi.org/10.1016/j.procs.2019.12.183

Charrad, M., Bellamine, N., & Saoud, B. (2019). ScienceDirect ScienceDirect Towards A Social Media-Base dFrame workfor Disaster Towards A Social Media-Based Framework for Disaster Communication Communication. Procedia Computer Science,164, 271–278. https://doi.org/10.1016/j. procs.2019.12.183

Chen, E., Lerman, K., & Ferrara, E. (2020). Tracking social media discourse about the COVID-19 pandemic: Development of a public coronavirus Twitter data set. JMIR Public Health and Surveillance, 6(2). https://doi. org/10.2196/19273

Chou, W. S., Scie nces , P., C ance r, N., Oh , A., Sciences, P., Cancer, N., Klein, W. M. P., Sciences, P., & Cance r, N. (2017). The Persistence and Peril of Misinformation. American Scientist, 105(6), 372. https://doi. org/10.1511/2017.105.6.372

Chou, W. Y. S., & Budenz, A. (2020). Considering Emotion in COVID-19 Vaccine Communication: Addressing Vaccine Hesitancy and Fostering Vaccine Confidence. Health Communication, 35(14), 1718–1722. https://doi.org/10.1080/10410236.2020.1838096

Drone Emprit. (2020a). Corona Virus. DE Report. https://pers.droneemprit.id/corona-virus/

Drone Emprit. (2020b). KONSPIRASI #COVID19. DE Report. https://pers.droneemprit.id/konspirasi-covid19/

Drone Emprit. (2020c). Rumah Sakit Terjepit. DE Report. https://pers.droneemprit.id/rumah-sakit-terjepit/

Drone Emprit. (2020d). Stigma Terhadap Tenaga Kesehatan. DE Report. https://pers.droneemprit.id/stigma-terhadap-tenaga- kesehatan/

Drone Emprit. (2021a). Demo Tolak PPKM dan Tagar-tagar Turunkan Presiden. DE Report. https://pers.droneemprit.id/demo-tolak- ppkm-dan-tagar-tagar-turunkan-presiden/

Drone Emprit. (2021b). Kampanye Stop Berita Covid-19. DE Report. https://pers.droneemprit.id/kampanye-stop-berita- covid/

Dryhurst, S., Schneider, C. R., Kerr, J., Freeman, A. L. J., Recchia, G., van der Bles, A. M., Spiegelhalter, D., & van der Linden, S. (2020). Risk perceptions of COVID-19 around the world. Journal of Risk Research, 23(7–8), 994–1006. https://doi.org/10.1080/13669877.2020.1758193

Dunne, C. (2012). Cha rt ing Col l ect ions of Connections in Social Media: Creating Visualizations with NodeXL. The Proceedings of the 13th Annual International Conference on Digital Government Research, 4, 332–339. https://doi.org/10.1109/CSE.2009.120

Eriksson, M., & Olsson, E. K. (2016). Facebook and Twitter in Crisis Communication: A Comparative Study of Crisis Communication Professionals and Citizens. Journal of Contingencies and Crisis Management, 24(4), 198–208. https://doi.org/10.1111/1468-5973.12116

Eysenbach, G. (2002). Infodemiology: The Epidemiology of (Mis) information .9343(02), 763–765.

Eysenbach, G., Powell, J., Kuss, O., & Sa, E. R. (2002). Empirical studies assessing the quality of health information for consumers on the World Wide Web: A systematic review. Journal of the American Medical Association, 287(20), 2691–2700. https://doi.org/10.1001/jama.287.20.2691

Fernández-Torres, M. J., Almansa-Martínez, A., & Chamizo-Sánchez, R. (2021). Infodemic and fake news in spain during the COVID-19 pandemic. International Journal of Environmental Research and Public Health,18(4), 1–13. https://doi.org/10.3390/ijerph18041781

Freiling, I., Krause, N. M., Scheufele, D. A., & Brossard, D. (202 1). Believing and sharing misinformation, fact-checks, and accurate information on social media: The role of anxiety during COVID-19. New Media and Society. https://doi. org/10.1177/14614448211011451

Gabarron, E., Oyeyemi, S. O., & Wynn, R. (2021). Covid-19-related misinformation on social media: A systematic review. Bulletin of the World Health Organization, 99(6), 455-463A. https://doi.or g/10.2471/ BLT.20.276782

Hansen, D. L., Shneiderman, B., & Smith, M. A. (2011). Getting Star ted with NodeXL, Layout, Visual Design, and Labeling. Analyzing Social Media Net works with NodeXL, 53–67. https://doi.org/10.1016/ b978-0-12-382229-1.00004-7

Harapan, H., Wagner, A. L., Yufika, A., Winardi, W., Anwa r, S., Gan, A. K., Setiawan, A. M., Rajamoorthy, Y., Sofyan, H., Vo, T. Q., Hadisoemarto, P. F., Müller, R., Groneberg, D. A., & Mudatsir, M. (2020). Willingness-to-pay for a COVID-19 vaccine and its associated determinants in Indonesia. Human Vaccines and Immunotherapeutics, 16(12), 3074–3080. https://doi.org/10.1080/21645515.2020.1819741

Harrigan, P., Miles, M. P., Fang, Y., & Roy, S. K. (2020). The role of social media in the engagement and information processes of social CRM. International Journal of Information Management, 54(October 2019), 102151. https://doi.org/10.1016/j. ijinfomgt.2020.102151

Haupt, M. R., Jinich-Diamant, A., Li, J., Nali, M., & Mackey, T. K. (2021). Characterizing twitter user topics and communication network dynamics of the “Liberate” movement during COVID-19 using unsupervised machine learning and social network analysis. Online Social Networks and Media, 21(July 2020), 100114. https://doi.org/10.1016/j.osnem.2020.100114

Irawan, B. (2022). Policies for controlling the covid-19 pandemic through social media communications by the East Kalimantan p rovin cia l government. International Journal of Communication and Society, 4(1), 125–136.

Islam, A. K. M. N., Laato, S., Talukder, S., & Sutinen, E. (2020). Misinformation sharing and social media fatigue during COVID-19: An affordance and cognitive load perspective. Technological Forecasting and Social Change, 159( July), 120201. https://doi.org/10.1016/j.techfore.2020.120201

Jovanovic, D., & Van Leeuwen, T. (2018). Multimodaldialogue on social media. Social Semiotics, 28(5), 683–699. https://doi.org/10.1080/10350330.2018.1504732

Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horiz ons, 53 (1), 59–68. https://doi.org/10.1016/j.bushor.2009.09.003

Kashyap, R., & Nahapetian, A. (2014). Tweet Analysis for User Health Monitoring. 348–351. https://doi.org/10.4108/icst. mobihealth.2014.257537

Khadafi, R., Nurmandi, A., Qodir, Z., & Misran. (2022). Hashtag as a new weapon to resist the COVID-19 vaccination policy: a qualitative study of the anti-vaccine movement in Brazil, USA, and Indonesia. Human Vaccines and Immunotherapeutics,18(1). https://doi.org/10.1080/21645515.2022.2042135

Kosasih, I. (2016). Peran Media Sosia l Facebook dan Twitter Dalam Membangun Komunikasi (Persepsi dan Motifasi Masyarakat Jejaring Sosial Dalam Pergaulan). Lembaran Masyarakat: Jurnal Pengembangan Masyarakat Islam, 2(1), 29–42. https://doi.org/10.1017/ CBO9781107415324.004

Kumar Singh, R., & Kumar Verma, H. (2020). Effective Parallel Processing Social Media Analytics Framework. Journal of King Saud Universit y - Computer and Information Sciences, xxxx. https://doi.org/10.1016/j.jksuci.2020.04.019

Lachlan, K. A., Spence, P. R., Lin, X., Najarian, K., & Del Greco, M. (2016). Social media and crisis management: CERC, search strategies, and Twitter content. Computers in Human Behavior, 54, 647–652. https://doi.org/10.1016/j.chb.2015.05.027

Leng, Y., Zhai, Y., Sun, S., Wu, Y., Selzer, J., Strover, S., Zhang, H., Chen, A., & Ding, Y. (2021). Misin formation during the COVID-19 outbreak in China: Cultural, social and political entanglements. IEEE Transactions on Big Data, 7(1), 69–80. https://doi. org/10.1109/TBDATA.2021.3055758

Lipschultz, J. H. (2017). Organizations, HR, CSR, and Their Social Networks: “Sustainability” on Twitter. Corporate Social Responsibility, Sustainability, and Ethical Public Relations,35–52. https://doi.org/10.1108/978-1-78714-585-620181002

López-Rabadán, P. (2022). Framing studies evolution in the social media era. Digital advancement and reorientation of the research agenda. Social Sciences, 11(1). https://doi.org/10.3390/socsci11010009

Machmud, M., Irawan, B., Karinda, K., Susilo, J., & Salahudin,. (2021). Analysis of the Int ensity of Communication and Coordination of Government Officials on Twitter Social Media during the Covid-19 Handling in Indonesia. Academic Journal of Interdisciplinary Studies, 10(3), 319. https://doi.org/10.36941/ajis-2021-0087

Martínez-Rojas, M., Pardo-Ferreira, M. del C., & Rubio-Romero, J. C. (2018). Twitter as a tool for the management and analysis of emergency situations: A systematic literature review. International Journal of Information Management, 43(April), 196–208. https://doi.org/10.1016/jijinfomgt.2018.07.008

Näkki, P., Bäck, A., Ropponen, T., Kronqvist, J., Hintikka, K. A., Harju, A., Pöyhtäri, R., & Kola, P. (2011). Social media for citizen participation report on the somus project. In VTT Publications (Issue 755).

Nugraha, R. R., Miranda, A. V., Ahmadi, A., & Lucero-Prisno, D. E. (2021). Accelerating Indonesian COVID-19 vaccination rollout: a critical task amid the second wave. Tropical Medicine and Health, 49(1). https://doi. org/10.1186/s41182-021-00367-3

Odlum, M., & Yoon, S. (2015). What can we learn about the Ebola outbreak from tweets? American Journal of Infection Control, 43(6), 563–571. https://doi.org/10.1016/j. ajic.2015.02.023

Osman, A. M S. (2019). A novel big data analytics framework for smart cities. Future Generation Computer Systems, 91, 620-633. https://doi.org/10.1016/j.future.2018.06.046

Park, H. W., Park, S., & Chong, M. (2020). Conversations and medical news frames on twitter: Infodemiological study on COVID-19 in South Korea. Journal of Medical Internet Research, 22(5). https://doi.org/10.2196/18897

Park, M., Cook, A. R., Lim, J. T., Sun, Y., & Dickens, B. L. (2020). A Systematic Review of COVID-19 Epidemiology Based on Current Evidence. Journal of Clinical Medicine, 9(4), 967. https://doi.org/10.3390/jcm9040967

Park, S., Han, S., Kim, J., Molaie, M. M., Vu, H. D., Singh, K., Han, J., Lee, W., & Cha, M. (2021). COVID-19 discourse on twitter in four asian countries: Case study of risk communication. Journal of Medical Internet Research, 23(3), 1–17. https://doi.org/10.2196/23272

Paul, M. J., Dredze, M., & Broniatowski, D. (2014). Twitter Improves Influenza Forecasting. PLoS Currents, October 2014. https://doi. org/10.1371/currents.outbreaks.90b9ed0f59bae4ccaa683a39865d9117

Petersen, K., & Gerken, J. M. (2021). #Covid-19: An exploratory investigation of hashtag usage on Twitter. Health Policy, 125(4), 541-547. https://doi.org/10.1016/j.healthpol.2021.01.001

Purnomo, E. P., Loilatu, M. J., Nurmandi, A., Salahudin, Qodir, Z., Sihidi, I. T., & Lutfi, M. (2021). How Public Transportation Use Social Media Platform during Covid-19: Study on Jakarta Public Transportations’ Twitter Accounts? Webology, 18(1), 1–19. https://doi.org/10.14704/WEB/V18I1/ WEB18001

Rathore, F. A., & Farooq, F. (2020). Information overload and infodemic in the COVID-19 pandemic. Journal of the Pakistan Medical Association, 70(5), S162–S165. https://doi. org/10.5455/JPMA.38

Rosenberg, H., Syed, S., & Rezaie, S. (2020). The Twitter pandemic: The critical role of Twitter in the dissemination of medical information and misinformation during the COVID-19 pandemic. Canadian Journal of Emergency Medicine, 22(4), 418–421. https://doi.org/10.1017/cem.2020.361

Saleh, S. N., Lehmann, C. U., McDonald, S. A., Basit, M. A., & Medford, R. J. (2021). Understanding public perception of coronavirus disease 2019 (COVID-19) social distancing on Twitter. In Infection Control and Hospital Epidemiology (Vol. 42, Issue 2, pp. 131–138). https://doi.org/10.1017/ice.2020.406

Schroeder, R. (2014). Big Data and the brave new world of social media research. Big Data and Society, 1(2), 1–11. https://doi. org/10.1177/2053951714563194

Schwarz, A. (2012). How publics use social media to respond to blame games in crisis communication: The Love Parade tragedy in Duisburg 2010. Public Relations Review, 38(3), 430–437. https://doi.org/10.1016/j. pubrev.2012.01.009

Shahi, G. K., Dirkson, A., & Majchrzak, T. A. (2021). An exploratory study of COVID-19 misinformation on Twitter. Online Social Networks and Media, 22(September 2020), 100104. https://doi.or g/10.1016/j. osnem.2020.100104

Silver, C., & Lewins, A. (2007). QDA Miner 3 . 2 ( with WordStat & Simstat ) Distinguishing features and functions. Database, 2.

Song, C., & Lee, J. (2016). Citizens Use of Social Media in Government, Perceived Transparency, and Trust in Government. Public Perf ormance and Manag ement Review, 39(2), 430–453. https://doi.org/10.1080/15309576.2015.1108798

Steffens, M. S., Dunn, A. G., Wiley, K. E., & Leask, J. (2019). How organisations promoting vaccination respond to misinformation on social media: a qualitative investigation. BMC Public Health, 19(1), 1–12. https://doi. org/10.1186/s12889-019-7659-3

Su, Y., Venkat, A., Yadav, Y., Puglisi, L. B., & Fodeh, S. J. (2021). Twitter-based analysis reveals differential COVID-19 concerns across areas with socioeconomic disparities. C omput ers in Biolog y and Medicine , 132(March). https://doi.org/10.1016/j. compbiomed.2021.104336

Szmuda, T., Ali, S., Özdemir, C., Syed, M. T., Singh, A., Hetzger, T. V., Rosvall, P., Fedorow, K., Alkhater, A., Majlöf, A., Albrahim, M., Alquraya, E., Dunquwah, R. Al, Al-Hakeem, Z., Almohisin, E., Alradhi, M., Zydowicz, W. M., Müller, C., Egeland, A., … Kieronska, S. (2020). Datasets and future research suggestions concerning SARS-CoV-2. European Journal of Translational and Clinical Medicine, 3(2), 80–85. https://doi. org/10.31373/ejtcm/124734

Tarai, J., Finau, G., Kant, R., & Titifanue, J. (2015). Fiji Flag Change: Social Media Responds. https://openresearch-repository.anu.edu. au/bitstream/1885/142860/1/ib2015.42_ tarai_finau_et_al.pdf

Wajahat Hussain. (2020). Role of Social Media in COVID-19 Pandemic. The International Journal of Frontier Sciences, 4(2), 59–60. https://doi.org/10.37978/tijfs.v4i2.144

WHO. (2021a). An overview of infodemic management during COVID-19. In Who (Issue May). https://www.who.int/health- topics/infodemic#tab=tab_1

WHO. (2021b). COVID-19 Episode #33 - Medical oxygen (pp. 1–3). WHO. https://www. who.int/emergencies/diseases/novel- cor onavirus-2019/media-r esour ces/ science-in-5/episode-33---medical-oxyge n?gclid=CjwKCAiAgvKQBhBbEiwAaPQw3AVIYLEE6tJWXPhOx4jxpilT4F9FkkL6j5HzfinQGw28USX6YNErrRoCcQEQAvD_BwE

Widjaja, G., Zahari, M., Hastuti, P., Nugraha, A. R., & Kusumawaty, I. (2021). Understanding COVID-19 vaccination program among Indonesian public: A challenge and hope for government. International Journal of Health Sciences, 5(3), 212–223. https://doi. org/10.53730/ijhs.v5n3.1429

Witanto, J. N., Lim, H., & Atiquzzaman, M. (2018). Smart government framework with geo- crowdsourcing and social media analysis. Futu re Generat ion Computer Systems, 89, 1–9. https://doi.org/10.1016/j.future.2018.06.019

Yang, K. C., Pierri, F., Hui, P. M., Axelrod, D., Torres- Lugo, C., Bryden, J., & Menczer, F. (2021). The COVID-19 Infodemic: Twitter versus Facebook. Big Data and Society, 8(1). https:// doi.org/10.1177/20539517211013861

Yoon, S., Odlum, M., Broadwell, P., Davis, N., Cho, H., Deng, N., Patrao, M., Schauer, D., Bales, M. E., & Alcantara, C. (2020). Application of social network analysis of COVID-19 elated tweets mentioning cannabis and opioids to gain insights for drug abuse research. Studies in Health Technology and Informatics,272(June), 5–8. https://doi.org/10.3233/ SHTI200479

Zulfaroh, A. N. (2021). Media Asing Soroti Penanganan Corona di Indonesia, Epidemiolog Harus Jadi Evaluasi. Kompas.Com. https://www.kompas.com/tren/read/2021/07/30/153000765/media-asing-soroti-penanganan-corona-di-indonesia-epidemiolog--harus-jadi?page=all