Abstract
The outbreak of COVID-19 has caused a huge shock for human society. As people experience the attack of the COVID-19 virus, they also are experiencing an information epidemic at the same time. Rumors about COVID-19 have caused severe panic and anxiety. Misinformation has even undermined epidemic prevention to some extent and exacerbated the epidemic. Social networks have allowed COVID-19 rumors to spread unchecked. Removing rumors could protect people’s health by reducing people’s anxiety and wrong behavior caused by the misinformation. Therefore, it is necessary to research COVID-19 rumor detection on social networks. Due to the development of deep learning, existing studies have proposed rumor detection methods from different perspectives. However, not all of these approaches could address COVID-19 rumor detection. COVID-19 rumors are more severe and profoundly influenced, and there are stricter time constraints on COVID-19 rumor detection. Therefore, this study proposed and verified the rumor detection method based on the content and user responses in limited time CR-LSTM-BE. The experimental results show that the performance of our approach is significantly improved compared with the existing baseline methods. User response information can effectively enhance COVID-19 rumor detection.
Highlights
Nowadays, the social network has become an indispensable tool in people’s daily life
We proposed rumor detection methods based on the features of rumor content and user responses because of the rapid propagation and prominent domain characteristics of COVID-19 rumor detection on social networks
In order to better capture and extract rumor content features, we combined the language model based on transfer learning with a post-training mechanism to construct CSN-BERT based on COVID-19 user posts on social networks
Summary
The social network has become an indispensable tool in people’s daily life. People carry out activities such as social communication, obtaining information, and expressing opinions on social network platforms. In the above activities, securing information and expressing opinions are frequent on social networks. Most of the content on social networks is usergenerated content (UGC), and the veracity of UGC is challenging to be guaranteed. Rumors in social networks are rampant when public incidents occur. During the COVID-19 epidemic outbreak in 2020, a large number of rumors spread widely on social platforms such as Twitter and Weibo, which aggravated people’s fear and anxiety about the epidemic, and made people experience an “information epidemic” in the virtual space [1]. Rumor governance on social networks is essential and necessary work
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