LAUSR

During the onset of COVID-19 pandemic, the social media was flooded with misinformation. Irrespective of the type of the misinformation, such contents played a significant role in increasing confusion among people in the middle of an ongoing crisis. The purpose of the study is to investigate the nature of a specific type of misinformation, i.e., rumors, surrounding COVID-19. The study utilizes a publicly available and labelled Twitter dataset and proposes a novel feature space, which can detect rumor instances with high accuracy. The proposed feature space not only includes content-based features, but also includes psycho-linguistic features to further study the characteristics of the content from the perspectives of linguistics and psychology. The use of psycho-linguistic features has been utilised to understand certain dramatisation of text in the domain of conspiracy propagation and fake news detection. However, the use of such dramatisation detection approach has never been used for the purposes of rumor detection. Our study first outlines the differences between these categories of misinformation propagation and clarifies where rumor fits-in under the broader umbrella of misinformation. It further outlines how the use of psycho-linguistic features can also improve the detection accuracy of rumors on social media. The study demonstrates through multiple experimental setups that psycho-linguistic features improves the detection accuracy and associated performance measures, such as precision and recall, for COVID-19 rumors on Twitter. The observed improvements are consistent across multiple machine learning models.