A collaborative intelligence approach to fighting COVID-19 false news ::a Chinese case

The rapid outbreak of COVID-19 has heightened interest in news about the pandemic. In addition to obtaining real-time developments about COVID-19, people have learned about prevention methods through the news media. Ironically, false COVID-19 news has spread faster than the virus, posing an additional health threat with advice being as dangerous as infection. In this study, we developed a Chinese news article dataset on COVID-19 misinformation, which contained 1266 verified articles from 118 Chinese digital newspaper platforms from January 2020 to January 2021. This dataset uses machine learning methods to detect false news in the Chinese language. Because automated classification methods, combined with human computation-based approaches, are effective for combating digital misinformation, we applied and evaluated a collaborative intelligence approach that leverages human fact-checking skills with feedback on news stories using four criteria: source, author, message, and spelling. The results show that reliable human feedback can help detect false news with high accuracy