Identifying hidden patterns of fake COVID-19 news: An in-depth sentiment analysis and topic modeling approach

Spreading misinformation and fake news about COVID-19 has become a critical concern. It contributes to a lack of trust in public health authorities, hinders actions from controlling the virus’s spread, and risks people’s lives. This study aims to gain insights into the types of misinformation spread and develop an in-depth analytical approach for analyzing COVID-19 fake news. It combines the idea of Sentiment Analysis (SA) and Topic Modeling (TM) to improve the accuracy of topic extraction from a large volume of unstructured texts by considering the sentiment of the words. A dataset containing 10,254 news headlines from various sources was collected and prepared, and rule-based SA was applied to label the dataset with three sentiment tags. Among the TM models evaluated, Latent Dirichlet Allocation (LDA) demonstrated the highest coherence score of 0.66 for 20 coherent negative sentiment-based topics and 0.573 for 18 coherent positive fake news topics, outperforming Non-negative Matrix Factorization (NMF) (coherence: 0.43) and Latent Semantic Analysis (LSA) (coherence: 0.40). The topics extracted from the experiments highlight that misinformation primarily revolves around the COVID vaccine, crime, quarantine, medicine, and political and social aspects. This research offers insight into the effects of COVID-19 fake news, provides a valuable method for detecting and analyzing misinformation, and emphasizes the importance of understanding the patterns and themes of fake news for protecting public health and promoting scientific accuracy. Moreover, it can aid in developing real-time monitoring systems to combat misinformation, extending beyond COVID-19-related fake news and enhancing the applicability of the findings.