UNITE-FND: Reframing Multimodal Fake News Detection through Unimodal Scene Translation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909496163434496 |
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| author | Mukherjee, Arka Ghosh, Shreya |
| author_facet | Mukherjee, Arka Ghosh, Shreya |
| contents | Multimodal fake news detection typically demands complex architectures and substantial computational resources, posing deployment challenges in real-world settings. We introduce UNITE-FND, a novel framework that reframes multimodal fake news detection as a unimodal text classification task. We propose six specialized prompting strategies with Gemini 1.5 Pro, converting visual content into structured textual descriptions, and enabling efficient text-only models to preserve critical visual information. To benchmark our approach, we introduce Uni-Fakeddit-55k, a curated dataset family of 55,000 samples each, each processed through our multimodal-to-unimodal translation framework. Experimental results demonstrate that UNITE-FND achieves 92.52% accuracy in binary classification, surpassing prior multimodal models while reducing computational costs by over 10x (TinyBERT variant: 14.5M parameters vs. 250M+ in SOTA models). Additionally, we propose a comprehensive suite of five novel metrics to evaluate image-to-text conversion quality, ensuring optimal information preservation. Our results demonstrate that structured text-based representations can replace direct multimodal processing with minimal loss of accuracy, making UNITE-FND a practical and scalable alternative for resource-constrained environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11132 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UNITE-FND: Reframing Multimodal Fake News Detection through Unimodal Scene Translation Mukherjee, Arka Ghosh, Shreya Machine Learning Artificial Intelligence Multimodal fake news detection typically demands complex architectures and substantial computational resources, posing deployment challenges in real-world settings. We introduce UNITE-FND, a novel framework that reframes multimodal fake news detection as a unimodal text classification task. We propose six specialized prompting strategies with Gemini 1.5 Pro, converting visual content into structured textual descriptions, and enabling efficient text-only models to preserve critical visual information. To benchmark our approach, we introduce Uni-Fakeddit-55k, a curated dataset family of 55,000 samples each, each processed through our multimodal-to-unimodal translation framework. Experimental results demonstrate that UNITE-FND achieves 92.52% accuracy in binary classification, surpassing prior multimodal models while reducing computational costs by over 10x (TinyBERT variant: 14.5M parameters vs. 250M+ in SOTA models). Additionally, we propose a comprehensive suite of five novel metrics to evaluate image-to-text conversion quality, ensuring optimal information preservation. Our results demonstrate that structured text-based representations can replace direct multimodal processing with minimal loss of accuracy, making UNITE-FND a practical and scalable alternative for resource-constrained environments. |
| title | UNITE-FND: Reframing Multimodal Fake News Detection through Unimodal Scene Translation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.11132 |