Detecting Chinese Fake News on Twitter during the COVID-19 Pandemic

• URL: http://arxiv.org/abs/2304.03454v1
• Date: Fri, 7 Apr 2023 03:03:11 GMT
• Title: Detecting Chinese Fake News on Twitter during the COVID-19 Pandemic
• Authors: Yongjun Zhang, Sijia Liu, Yi Wang, Xinguang Fan
• Abstract summary: The outbreak of COVID-19 has led to a global surge of Sinophobia partly because of the spread of misinformation, disinformation, and fake news on China. We report on the creation of a novel classifier that detects whether Chinese-language social media posts from Twitter are related to fake news about China. We provide the final model and a new training dataset with 18,425 tweets for researchers to study fake news in the Chinese language during the COVID-19 pandemic.
• Score: 14.572408454393042
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The outbreak of COVID-19 has led to a global surge of Sinophobia partly because of the spread of misinformation, disinformation, and fake news on China. In this paper, we report on the creation of a novel classifier that detects whether Chinese-language social media posts from Twitter are related to fake news about China. The classifier achieves an F1 score of 0.64 and an accuracy rate of 93%. We provide the final model and a new training dataset with 18,425 tweets for researchers to study fake news in the Chinese language during the COVID-19 pandemic. We also introduce a new dataset generated by our classifier that tracks the dynamics of fake news in the Chinese language during the early pandemic.

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