Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemic

• URL: http://arxiv.org/abs/2106.11702v2
• Date: Wed, 23 Jun 2021 14:24:37 GMT
• Title: Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemic
• Authors: Ye Jiang, Xingyi Song, Carolina Scarton, Ahmet Aker, Kalina Bontcheva
• Abstract summary: We introduce a fine-grained annotated misinformation tweets dataset including social behaviours annotation. The dataset not only allows social behaviours analysis but also suitable for both evidence-based or non-evidence-based misinformation classification task.
• Score: 6.137022734902771
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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