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arXiv · 2410.15517

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

Swarang Joshi·Siddharth Mavani·Joel Alex·Arnav Negi·Rahul Mishra·Ponnurangam Kumaraguru
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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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Swarang Joshi, Siddharth Mavani, Joel Alex, Arnav Negi, Rahul Mishra, Ponnurangam Kumaraguru. 2024-10-20. SceneGraMMi: Scene Graph-boosted Hybrid-fusion for Multi-Modal Misinformation Veracity Prediction. https://arxiv.org/abs/2410.15517

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