Clock against Chaos: Dynamic Assessment and Temporal Intervention in Reducing Misinformation Propagation

Abstract

As social networks become the primary sources of information, the rise of misinformation poses a significant threat to the information ecosystem. Here, we address this challenge by proposing a dynamic system for real-time evaluation and assignment of misinformation scores to tweets, which can support the ongoing efforts to counteract the impact of misinformation public health, public opinion, and society. We use a unique combination of Temporal Graph Network (TGN) and Recurrent Neural Networks (RNNs) to capture both structural and temporal characteristics of misinformation propagation. We further use active learning to refine the understanding of misinformation, and a dual model system to ensure the accurate grading of tweets. Our system also incorporates a temporal embargo strategy based on belief scores, allowing for comprehensive assessment of information over time. We further outline a retraining strategy to keep the model current and robust in the dynamic misinformation landscape. The evaluation results across five social media misinformation datasets show promising accuracy in identifying false information and reducing propagation by a significant margin.