Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemic
This work addresses the need for better tools to study and categorize misinformation in the COVID-19 infodemic, though it is incremental as it builds on existing detection and analysis efforts by adding social behavior annotations.
The paper tackles the problem of COVID-19 misinformation on social media by introducing a fine-grained annotated dataset of tweets that includes social behaviors, enabling analysis and classification tasks, and demonstrates through leave-claim-out validation that classification performance varies significantly on unseen misinformation.
The spreading COVID-19 misinformation over social media already draws the attention of many researchers. According to Google Scholar, about 26000 COVID-19 related misinformation studies have been published to date. Most of these studies focusing on 1) detect and/or 2) analysing the characteristics of COVID-19 related misinformation. However, the study of the social behaviours related to misinformation is often neglected. In this paper, we introduce a fine-grained annotated misinformation tweets dataset including social behaviours annotation (e.g. comment or question to the misinformation). The dataset not only allows social behaviours analysis but also suitable for both evidence-based or non-evidence-based misinformation classification task. In addition, we introduce leave claim out validation in our experiments and demonstrate the misinformation classification performance could be significantly different when applying to real-world unseen misinformation.