TruthX - AI Powered Fake News Detection & Content Verification

The rapid growth of social media platforms has significantly increased the spread of fake news, leading to misinformation and serious societal consequences.Traditional fake news detection approaches primarily rely on textual analysis, often neglecting the visual information that accompanies news content and plays a crucial role in deception.To address this limitation, this paper proposes a multimodal deep learning framework that integrates both textual and visual features for accurate fake news detection.The textual modality is processed using a hybrid CNN -network to capture local semantic patterns and long-range contextual dependencies, while the visual modality employs a pre-trained convolutional neural network to extract discriminative image features.The extracted features from both modalities are fused at the feature level and passed through fully connected layers for final classification.Experiments conducted on a publicly available multimodal fake news dataset demonstrate that the proposed multimodal approach outperforms unimodal models in terms of accuracy, precision, recall, and F1-score.The results highlight the effectiveness of combining textual and visual information, making the proposed system a robust and reliable solution for fake news detection on social media platforms.