Gated Transformer with Dual Attention for Rumour Category Detection on Social Platforms (International Journal of Scientific Research in Science and Technology, 2025)
The spread of misinformation and unverified content on social media has become a major concern in the digital age. Accurate categorization of rumours—into support, denial, query, or comment—is essential to assess the credibility and impact of online discourse. In this work, we propose a simplified yet effective deep learning framework that combines a Transformer-based encoder with a dual attention mechanism. The model captures fine-grained word-level semantics and post-level contextual relevance within conversation threads. By integrating gated recurrent units (GRUs) with word and post-level attention, the framework enhances the ability to distinguish rumour types based on linguistic and discourse cues. Experimental evaluations on two benchmark datasets, PHEME and RumourEval-19, demonstrate strong performance in terms of accuracy and F1-score. Furthermore, attention visualizations provide interpretability, making the model’s predictions more transparent and trustworthy.