Catalyst Awardee

Project Description

Analyzing strategies against health misinformation in Taiwan: A data mining approach


Hsin-Yu Kuo | National Tsing Hua University;  Su-Yen Chen, PhD; Yu-Ting Lai 
Competition Sponsor: Academia Sinica of Taiwan
Awardee Year: 2022

Our main mission is to determine profound and practical ways to counter health misinformation. The rate of misinformation in societies has increased and has severely affected public health. Especially, during the coronavirus pandemic, misinformation has caused social disruption, such as irrational stockpiling of materials and refusal to comply with the prevention rules. Moreover, the wrong knowledge about the coronavirus directly influences an individual’s physical and mental health. Thus, it is essential to confront and rebut misinformation crises in multiple ways. Corrective information, as a crisis communication strategy, is one of the countermeasures to reduce misperceptions and misbeliefs. Some current tools and platforms inform the public whether the information they read is correct, thereby accumulating false information and its corrective information content data and providing feedback from the audience. To gain insights from data to rebut misinformation, we analyze the features and effects of corrective information against misinformation using data science and psychological approaches to provide innovative research methods and perspectives on public information health. In brief, this project explores potential new technologies and social strategies that could help health and function during extended longevity. To understand and deal with the misinformation problem in Taiwan, we first focus on understanding Taiwan’s misinformation content development. Then, by extracting features of misinformation and corrective information, we classify the types and characteristics of the content on the corrective information platforms. Finally, we evaluate the acceptability among different corrective information features by supplying feedback data corresponding to the corrective information.

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