| dc.description.abstract | With the outbreak of the COVID-19, many countries around the world have gone into lockdown to prevent the spread of the epidemic. People stay at home and reduce unnecessary going out to avoid the risk of infection. The physical social activities were changed to online social media, and information related to the epidemic was also obtained through social media. However, a lot of information was not verified, but was easily spread through the characteristics of social media, leading to COVID-19 fake news spread on major online social platforms.
At present, most scholars only use the content of social media posts to detect COVID-19 fake news, and few scholars consider the content of social media comments, or the content of social media reposts. Additionally, the corpus mainly used for training COVID-19 fake news detection models are mostly English-based social platforms such as Twitter in most study, there are few corpus used in Chinese languages. Therefore, this study will use text mining technology to extract the content of posts related to the epidemic on Sina Weibo, a well-known social media in China, the content of comments, and the content of reposts, and use machine learning methods like Bayesian classifier, logistic regression, random forest, support vector machine to build COVID-19 fake news detection models. The final experimental results show that the model can achieve better model detection accuracy by combining the content of posts, the content of comments, and the content of reposts. | en_US |