Context-aware multimodal reasoning for explainable Bengali fake news detection using vision-language models
In particular, the rapid dissemination of fake news using social media platforms has posed a severe threat to social stability and information reliability in language groups that lack sufficient resources. In addition, it has become difficult to automatically identify fake news in the case of Bengali digital media because fake news is mostly distributed using multimodal data like memes, screenshots, posters, photos with texts, etc. The effectiveness of the existing fake news detection methods is somewhat hindered by their inability to provide explainability and their focus mainly on either textual or visual data. This study proposes a context-aware multimodal reasoning approach for explainable Bengali fake news detection. The proposed model incorporates EasyOCR for Bengali textual information extraction, ResNet-50 and ViT for supplementing visual feature learning, and Qwen2-VL-2B-Instruct for multimodal semantic inference. The proposed approach can detect semantic contradictions and associations between textual assertions and image information through matching textual and visual data based on a context-sensitive fusion technique. This methodology generates interpretable explanations beyond the fake/real binary categorization to enhance user trust in automated outcomes. According to an experiment conducted using a multimodal Bengali fake news dataset, the proposed approach surpasses conventional CNN, transformer, and unimodal baselines on several performance metrics. Results indicate how context-based multimodal reasoning can improve the efficiency and robustness of the model, along with making it more interpretable. The proposed method serves as an encouraging route towards the identification of fake news in multiple low resource languages, as well as in combating misinformation in the Bengali digital ecosystem.