Japan Society for Medical Science Communication
Vol. 2, No.1 2024 (The Second Number)

The Vanguard of Medical Science Communication to the General Public: Leveraging Social Media, the Internet, and Publications

Mamoru Ichikawa1,2)

1)Department of Public Health, School of Medicine, Hiroshima University
2)Association of Medical Journalism, General Incorporated

Dissemination of medical information during the COVID-19 pandemic

Satoshi Kutsuna

Department of Infection Control and Prevention, Graduate School of Medicine/Faculty of Medicine, Osaka University

The COVID-19 pandemic has re-emphasized the critical role of direct communication from experts to the public. After the outbreak of COVID-19 in Wuhan in December 2019, the pandemic rapidly spread around the world, significantly impacting society and overwhelming the medical information ecosystem. As of March 28, 2022, there are over 241,717 papers listed in PubMed concerning COVID-19 or SARS-CoV-2, and this number increases daily. Alongside this explosion of medical literature, the advent of preprints has facilitated the dissemination of new findings. However, the digital age has also introduced challenges in communicating accurate health information. In particular, the widespread use of social media complicates the public’s ability to discern credible sources amidst misinformation. For example, social media was partly responsible for the premature advocacy of Favipiravir and Ivermectin as COVID-19 treatments at a time when sufficient evidence was still unavailable. This case illustrates the pitfalls of the rapid spread of information that lacks scientific backing. Accordingly, there is an urgent need for specialized risk communication professionals, who are currently scarce in Japan. Effective management of public health crises must involve communication through diverse media channels, catering to various demographics and ensuring timely dissemination of reliable and scientifically valid information.

Practical Application of Medical Science Communication Using Various Methods

Takehito Yamamoto1,2)

1)Department of Gastroenterological Surgery and Oncology, Tazuke Kofukai Medical Research Institute, Kitano Hospital
2)Department of Gastrointestinal Surgery, Kyoto University

In the medical field, it is not uncommon for patients to be misled by inaccurate medical information. From personal experience, the author recognizes the need for medical professionals to communicate information to patients and has been actively disseminating information through various channels since around 2013. This includes writing letters to newspapers, launching medical information websites, utilizing social media platforms such as X (formerly Twitter), and posting video lectures to YouTube. Furthermore, since 2019, the author has been engaged in writing books, achieving a cumulative total of 230,000 copies sold. This paper aims to provide insights for healthcare professionals based on these experiences in communicating information.

Estimating Target Demographics of Social Media Posts: A Linguistic Feature-Based Approach

Shoko Wakamiya

Nara Institute of Science and Technology

Social media is becoming more important for sharing information in real time. Many organizations now use it to share information with the public. However, it is difficult to share information that will capture people’s attention. Therefore, there is a need for new ways of sharing information on social media that take risk communication into account. We employ an analogy of microphones and speakers; in audio recording and transmission, the intended recipient’s location is envisioned, and the audio device is oriented accordingly. This concept is referred to as “directivity.” We estimate the “directivity” of a message, that is, the target demographics (e.g., age, gender) for which the information was disseminated, based on linguistic features. We collected tweets from government and public accounts, as well as from magazines, and labeled these tweets based on responses obtained through crowdsourcing. This paper introduces a model for estimating the target of the tweets and reports on the model’s performance.

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