SceneGraMMi: Scene Graph-boosted Hybrid-fusion for Multi-Modal Misinformation Veracity Prediction

• URL: http://arxiv.org/abs/2410.15517v1
• Date: Sun, 20 Oct 2024 21:55:13 GMT
• Title: SceneGraMMi: Scene Graph-boosted Hybrid-fusion for Multi-Modal Misinformation Veracity Prediction
• Authors: Swarang Joshi, Siddharth Mavani, Joel Alex, Arnav Negi, Rahul Mishra, Ponnurangam Kumaraguru,
• Abstract summary: We propose SceneGraMMi, a Scene Graph-boosted Hybrid-fusion approach for Multi-modal Misinformation veracity prediction. Experimental results across four benchmark datasets show that SceneGraMMi consistently outperforms state-of-the-art methods.
• Score: 10.909813689420602
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Misinformation undermines individual knowledge and affects broader societal narratives. Despite growing interest in the research community in multi-modal misinformation detection, existing methods exhibit limitations in capturing semantic cues, key regions, and cross-modal similarities within multi-modal datasets. We propose SceneGraMMi, a Scene Graph-boosted Hybrid-fusion approach for Multi-modal Misinformation veracity prediction, which integrates scene graphs across different modalities to improve detection performance. Experimental results across four benchmark datasets show that SceneGraMMi consistently outperforms state-of-the-art methods. In a comprehensive ablation study, we highlight the contribution of each component, while Shapley values are employed to examine the explainability of the model's decision-making process.

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