Impact of Misinformation on Reddit User Behavior: A Multimodal Transformer-Based Detection Model

The proliferation of misinformation poses significant challenges to information integrity in the digital age. This research work presents a detailed analysis of how misinformation affects user behaviour on social networking sites like Reddit, and proposes a unified multimodal approach for detecting misinformation using both fake news texts and tampered images, by combining state-of-the-art deep learning models such as DistilBERT for text classification and Vision Transformer (ViT) for image classification. The analysis suggested that posts containing fake news encourage users to engage superficially, without promoting critical thinking. The proposed method integrates the transformer-based models to create a robust system capable of addressing the dual challenges of content verification. The models were trained and evaluated on a comprehensive dataset containing paired news articles and images, achieving an impressive accuracy of $\mathbf{9 2. 5 \%}$ in detecting fake news texts and 88.1% in identifying tampered images. The proposed unified model serves as a scalable solution for mitigating the spread of misinformation and manipulated visuals, contributing to the broader effort of maintaining the integrity of online information.