Identification and Analysis of COVID-19-related Misinformation Tweets via Kullback-Leibler Divergence for Informativeness and Phraseness and Biterm Topic Modeling

The interaction of Filipinos transitioned to a virtual setting making social media, like Twitter, their source of information since the pandemic started. The infodemic it caused has opened up avenues to understand the characteristics of misinformation tweets regarding COVID-19. In this paper, we present the classification and analysis of misinformation tweets related to COVID-19 towards identifying themes. We used pointwise KL divergence in scoring "informativeness" and "phraseness" to extract misinformation tweets and BTM for topic modeling. With a testbed of 7,711 tweets, the classifier model identified 3,533 misinformation tweets with an accuracy of 74.25%. The results of the topic modeling were analyzed and clustered to expose possible narratives in the data set. The three narratives showed that most Filipinos use Twitter to share jokes, spread information and awareness about the virus, express opinions about the government’s response, and share tips to prevent the disease. A wider date coverage could be included in future works.