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This paper presents a comprehensive analysis of the social media posts of prefectural governors in Japan during the COVID-19 pandemic. It investigates the correlation between social media activity levels, governors' characteristics, and engagement metrics. To predict citizen engagement of a specific tweet, machine learning models (MLMs) are trained using three feature sets. The first set includes variables representing profile- and tweet-related features. The second set incorporates word embeddings from three popular models, while the third set combines the first set with one of the embeddings. Additionally, seven classifiers are employed. The best-performing model utilizes the first feature set with FastText embedding and the XGBoost classifier. This study aims to collect governors' COVID-19-related tweets, analyze engagement metrics, investigate correlations with governors' characteristics, examine tweet-related features, and train MLMs for prediction. This paper's main contributions are twofold. Firstly, it offers an analysis of social media engagement by prefectural governors during the COVID-19 pandemic, shedding light on their communication strategies and citizen engagement outcomes. Secondly, it explores the effectiveness of MLMs and word embeddings in predicting tweet engagement, providing practical implications for policymakers in crisis communication. The findings emphasize the importance of social media engagement for effective governance and provide insights into factors influencing citizen engagement. [ABSTRACT FROM AUTHOR]

This paper discusses the challenges of disseminating COVID-19 information to migrant populations by sharing our trial-and-error approach. In 2018, the Migrants' Neighbor Network & Action (MINNA), a consortium of individuals and organizations that addressed the issues of accessing relevant information and services for migrants in Japan, was launched. Amidst the COVID-19 pandemic, the MINNA attempted to investigate and improve access to health information among Vietnamese, Nepali, and Burmese migrants in Japan. We had three assumptions in distribution of information to reach a large audience, such as building a multilingual website, requesting stakeholders to disseminate information, or posting on Facebook. None of our assumptions were sufficient to reach the target audience in the context of COVID-19, as total number of views that accessed our materials were less than 300 at most. We viewed these myths as the result of overlooking critical elements of effective communication strategies. Eventually, MINNA managed to establish communication with the manager of a Facebook page with the largest number of followers from the Vietnamese community in Japan. Compared with our previous attempts, the messages were delivered to a large audience on the Facebook page, such as the article on COVID-19 vaccines that was viewed more than 300,000 times. In public health emergencies, interactive process of information dissemination is necessary. It is a key component for risk communication and should be prioritized. Breakthroughs in communicating with a larger audience could be possible through partnerships with online communities. [ABSTRACT FROM AUTHOR]

This debate examines the impact of infodemics – an over-abundance of information – on social distancing during the COVID-19 pandemic. Because of its external effects, social distancing behavior (SDB) shares fundamental properties with public goods, whose potential for undersupply has been examined extensively in the social sciences. Although the negative effects of infodemics have been emphasized by governments and international organizations, theoretical models suggest that infodemics may work as a mitigation mechanism. That is, infodemics may enhance people's SDBs. Based on original survey data, we show that media exposure can positively increase SDB. We conclude by discussing two public health implications. First, the media plays an important role in motivating SDB. Second, even if infodemics can increase SDB, we must be wary of their ability to pose other, non-negligible dangers. [ABSTRACT FROM AUTHOR]