Indonesian Citizens' Health Behavior in a Pandemics: Twitter Conversation Analysis using Latent Dirichlet Allocation

Maylawati, Dian Sa'adillah and Ramdhani, Muhammad Ali (2022) Indonesian Citizens' Health Behavior in a Pandemics: Twitter Conversation Analysis using Latent Dirichlet Allocation. In: 2022 8th International Conference on Wireless and Telematics (ICWT), 21-22 Juli 2022, Yogyakarta.

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Abstract

Health is an essential thing in carrying out human activities. The COVID-19 pandemic has made people aware of the importance of maintaining health and hygiene for individuals, families, and the surrounding community. All countries, including Indonesia, are impacted by the COVID-19 pandemic, which has undoubtedly changed health behavior in the community. This study aims to reveal changes in health behavior during the pandemic through conversations on social media such as Twitter. The study was conducted using the Latent Dirichlet Allocation (LDA) method to analyze changes in Indonesian citizens' health behavior during the pandemic through social media analysis. The results of social media analysis using LDA on 495,740 tweet data indicate that it is true that there has been a change in public health behavior. At the beginning of the pandemic, many people still did not believe that various hoaxes were spread, and it was difficult to comply with health protocols. Hence, the government massively appealed to make regulations to break the chain of the spread of COVID-19. However, at a critical time with many victims falling, the public became more aware, maintained health protocols, followed the vaccination program, and finally, people got used to coexistence with COVID-19. These results indicate that the Indonesian people are wiser in dealing with the COVID-19 pandemic and following the applicable health protocols.

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