Paraphrase-Augmented Multi-Modal Fake News Detection
Misinformation spreads rapidly online, making it challenging to separate fact from falsehood. Many existing detection models focus only on text or images, limiting their effectiveness. Text-based models may overlook visual cues, while image-based models may miss subtle linguistic patterns. In this work, we explore whether paraphrased text can improve fake news detection accuracy by helping the model focus on meaning rather than wording. We propose, a multi-modal fake news detection framework that combines DistilBERT for text encoding, FLAN-T5 for generating paraphrased variants, and visual features extracted using either ResNet-34 or CLIP ViT. By evaluating our method across both visual backbones, we analyze the impact of paraphrasing on different model architectures. Experiments on the Fakeddit dataset show that paraphrasing increases overall accuracy by 2–4%, reaching up to 79–82%, and improves F1-score to around 78–80%. Results confirm that text augmentation through paraphrasing consistently improves detection performance across both types of visual models, making the system more robust against reworded misinformation. Overall, the proposed architecture demonstrates an efficient and scalable approach to detecting fake news by combining semantic understanding, visual context, and paraphrased text.