Misinformation detection on online social networks using pretrained language models

Abstract

The growing prevalence of online misinformation poses substantial threats, with notable examples including the undermined integrity of democratic processes and decreased effectiveness of public health efforts. The effectiveness of existing solutions, such as user education and content removal, remains unclear, primarily because confirmation bias and peer pressure hinder the identification of noncredible information by users. To address these challenges posed by online misinformation, this study proposes a state-of-the-art approach that leverages transformer-based models, including bidirectional encoder representation from transformers (BERT), GPT-2, and XLNet. These models leverage attention mechanisms to simultaneously process and capture contextual subtleties in documents, enabling highly accurate misinformation detection and classification in dynamic and complex online narratives. A transformer-based pretrained language model is used to analyze, a large corpus of tweets related to misinformation events concerning the 2020 U.S. election. Although isolated interventions are found to be ineffective, a synergistic approach is shown to reduce misinformation prevalence by 87.9 % within a 40-min delay based on a credibility interval of 80 %. These findings highlight the potential of empirical models to inform policies, enhance content moderation practices, and strengthen public resilience against misinformation.