MODELING OF COVID-19 TOPICS ON PUBLIC HEALTH MESSAGE COMMUNICATION PATTERNS ON RADAR BANYUMAS SOCIAL MEDIA

Gita Anggria Resticka, Erwita Nurdiyanto, Gigih Ariastuti Purwandari

Abstract

The global outbreak of Covid-19 has emerged as one of the most devastating and challenging threats to all peoples of the world. The purpose of this study is to identify the theme and topic of Covid-19 on the pattern of public health message communication on social media Radar Banyumas. The spread of Covid-19 disease was found to correlate with social media activity as a tool to promote Covid-19 News. Topic modeling revealed from time to time in the Radar Banyumas mass media can help understand the impact of the outbreak on the emotions, beliefs, and thoughts of the affected communities. This can open up great opportunities for proper education and dissemination of information on public health recommendations. This study shows that data from Banyumas Radar mass media is useful for infodemiology studies. This topic modeling consistently categorizes public health messages, risk factors, pandemic situations, the impact of Covid-19, measures to slow the spread of Covid-19, preventive measures, health authorities and government policies, negative psychological reactions, social stigma related to Covid-19, Covid-19 cases, Covid-19 in Banyumas, and Covid-19 cases in Indonesia.

Keywords

Topic Modeling, Discourse Analysis, Covid - 19, Banyumas Radar

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References

Abrams, T. (2020). Disability, deadly discourse, and collectivity amid coronavirus (COVID-19). Scandinavian Journal of Disability Research, 22(1), 168–174. https://doi.org/10.16993/SJDR.732

Ameli, M., Esfandabadi, Z. S., Sadeghi, S., Ranjbari, M., & Zanetti, M. (2022). COVID-19 and Sustainable Development Goals (SDGs): Scenario analysis through fuzzy cognitive map modeling. Gondwana Research, 14(1), 138-155. https://doi.org/10.1016/j.gr.2021.12.014

Bonefasius, R. dkk. (2020). Elipsis pada wacana tentang covid-19 dalam kolom Opini surat kabar Kompas. Jurnal Pendidikan dan kebudayaan Miissio, 12(2), 152–163. https://doi.org/10.36928/jpkm.v12i2.580

Buigut, S., & Kapar, B. (2021). Covid-19 cases, media attention and social mood. International Journal of Economics and Financial Issues, 11(4), 66-72. https://doi.org/10.32479/ijefi.11591

Chandrasekaran, R., Mehta, V., Valkunde, T., & Moustakas, E. (2020). Topics, trends, and sentiments of tweets about the COVID-19 pandemic: Temporal infoveillance study. Journal of Medical Internet Research, 22(10), 1-12, https://doi.org/10.2196/22624

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), 1-9. https://doi.org/10.2196/19273

Chew, C., & Eysenbach, G. (2009). Pandemics in the age of Twitter: Content analysis of Tweets during the 2009 H1N1 outbreak. PLoS ONE, 5(11), 1-13. https://doi.org/10.1371/journal.pone.0014118

Garcia, K., & Berton, L. (2020). Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA. Applied Soft Computing, 101, 107057. https://doi.org/10.1016/j.asoc.2020.107057

Gustomy, R. (2020). Pandemi ke Infodemi: Polarisasi Politik dalam Wacana Covid-19 Pengguna Twitter. JIIP: Jurnal Ilmiah Ilmu Pemerintahan, 5(2), 190-205. https://doi.org/10.14710/jiip.v5i2.8781

Hosseini, P., Hosseini, P., & Broniatowski, D. A. (2020). Content analysis of Persian/Farsi Tweets during COVID-19 pandemic in Iran using NLP. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.nlpcovid19-2.26

Ito, H., & Chakraborty, B. (2020). Social Media Mining with Dynamic Clustering: A Case Study by COVID-19 Tweets. 2020 11th International Conference on Awareness Science and Technology (iCAST), 1–6. https://doi.org/10.1109/iCAST51195.2020.9319496

Kwok, S. W., Vadde, S. K., & Wang, G. (2021). Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis. Journal of Medical Internet Research, 23(5), 1-16. https://doi.org/10.2196/26953

Liu, Q., Zheng, Z., Zheng, J., Chen, Q., Liu, G., Chen, S.,& Ming, W. (2020). Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: Digital Topic Modeling Approach. Journal of Medical Internet Research, 22(4), 1-12. https://doi.org/10.2196/19118

Lwin, M., Lu, J., Sheldenkar, A., Schulz, P., Shin, W., Gupta, R., & Yang, Y. (2020). Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends. JMIR Public Health and Surveillance, 6(2), 1-10. https://doi.org/10.2196/19447

Majumder, M. S., & Mandl, K. D. (2020). Early in the epidemic: impact of preprints on global discourse about COVID-19 transmissibility. The Lancet Global Health, 8(5), e627–e630. https://doi.org/10.1016/S2214-109X(20)30113-3

Marcoux, T., & Agarwal, N. (2021). Narrative Trends of COVID-19 Misinformation. Diambil dari https://www.semanticscholar.org/paper/0709e03af0787bdd1cf6cad724283f9097e75a2a

Medford, R., Saleh, S., Sumarsono, A., Perl, T., & Lehmann, C. U. (2020). An “Infodemic”: Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the Coronavirus Disease 2019 Outbreak. Open Forum Infectious Diseases, 7. https://doi.org/10.1093/ofid/ofaa258

Muchtar, K., Bahrudin, B., & Khoyin, M. (2020). Analisis Tajuk Rencana Mengenai Covid 19 pada Harian Umum Pikiran Rakyat. 1–14. Diambil dari http://digilib.uinsgd.ac.id/id/eprint/31611

Mutanga, M. B., & Abayomi, A. (2020). Tweeting on COVID-19 pandemic in South Africa: LDA-based topic modelling approach. African Journal of Science, Technology, Innovation and Development, 14, 163–172. https://doi.org/10.1080/20421338.2020.1817262

Nur, M. (2020). Konstruksi Reputasi dan Responsitivitas Intansi Pemerintah melalui Media Sosial pada Situasi Pandemi Covid-19 (Analisis Wacana pada Laman Facebook Direktorat Jenderal Perbendaharaan Kementerian Keuangan RI). 5, 217–234.

Ortega, J., & Bernabé-Moreno, J. (2021). Modelling the Degree of Emotional Concern: COVID-19 Response in Social Media. Applied Sciences, 11(9),38-72. https://doi.org/10.3390/app11093872

Prajarto, N. (2021). A Content Analysis on Health Communication of Covid-19 in Online Media From February To July 2021. Jurnal Penelitian Komunikasi dan Opini Publik, 25(2), 132–148. https://doi.org/10.33299/jpkop.25.2.3544

Scannell, D. (2021). COVID-19 Vaccine Discourse on Twitter: A Content Analysis of Persuasion Techniques, Sentiment and Mis/Disinformation. Journal of Health Communication, 26(7), 443–459. https://doi.org/10.1080/10810730.2021.1955050

Sha, H., Hasan, M., Mohler, G., & Brantingham, P. (2020). Dynamic Topic Modeling of the COVID-19 Twitter Narrative Among U.S. Governors and Cabinet Executives. ArXiv, abs/2004.1. Diambil dari https://www.semanticscholar.org/paper/e767d7dc7a215fc7e1eed093425089a179eba77a

Xue, J., Chen, J., Chen, C., Zheng, C., Li, S., & Zhu, T. (2020). Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter. PLoS ONE, 15(1), 1-12. https://doi.org/10.1371/journal.pone.0239441

Yanti, A. D. dkk. (2020). Analisis Kesalahan Berbahasa dalam Wacana Media Sosial Lawan Covid-19. Journal Humanities Piktorial, 2(2), 1-8, http://dx.doi.org/10.32493/piktorial.v2i2.7643

Yin, H., Yang, S., & Li, J. (2020). Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media. in Advanced Data Mining and Applications (ADMA 2020). Lecture Notes in Computer Science, vol 12447. https://doi.org/10.1007/978-3-030-65390-3_46

DOI: https://doi.org/10.20961/hsb.v7i1.66599

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