A New Hybrid Intelligent Approach for Multimodal Detection of Suspected Disinformation on TikTok
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arXiv
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| Format: | Preprint |
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2025
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| author | Guerrero-Sosa, Jared D. T. Montoro-Montarroso, Andres Romero, Francisco P. Serrano-Guerrero, Jesus Olivas, Jose A. |
| author_facet | Guerrero-Sosa, Jared D. T. Montoro-Montarroso, Andres Romero, Francisco P. Serrano-Guerrero, Jesus Olivas, Jose A. |
| contents | In the context of the rapid dissemination of multimedia content, identifying disinformation on social media platforms such as TikTok represents a significant challenge. This study introduces a hybrid framework that combines the computational power of deep learning with the interpretability of fuzzy logic to detect suspected disinformation in TikTok videos. The methodology is comprised of two core components: a multimodal feature analyser that extracts and evaluates data from text, audio, and video; and a multimodal disinformation detector based on fuzzy logic. These systems operate in conjunction to evaluate the suspicion of spreading disinformation, drawing on human behavioural cues such as body language, speech patterns, and text coherence. Two experiments were conducted: one focusing on context-specific disinformation and the other on the scalability of the model across broader topics. For each video evaluated, high-quality, comprehensive, well-structured reports are generated, providing a detailed view of the disinformation behaviours. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_06893 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A New Hybrid Intelligent Approach for Multimodal Detection of Suspected Disinformation on TikTok Guerrero-Sosa, Jared D. T. Montoro-Montarroso, Andres Romero, Francisco P. Serrano-Guerrero, Jesus Olivas, Jose A. Computer Vision and Pattern Recognition Computation and Language Multimedia Symbolic Computation In the context of the rapid dissemination of multimedia content, identifying disinformation on social media platforms such as TikTok represents a significant challenge. This study introduces a hybrid framework that combines the computational power of deep learning with the interpretability of fuzzy logic to detect suspected disinformation in TikTok videos. The methodology is comprised of two core components: a multimodal feature analyser that extracts and evaluates data from text, audio, and video; and a multimodal disinformation detector based on fuzzy logic. These systems operate in conjunction to evaluate the suspicion of spreading disinformation, drawing on human behavioural cues such as body language, speech patterns, and text coherence. Two experiments were conducted: one focusing on context-specific disinformation and the other on the scalability of the model across broader topics. For each video evaluated, high-quality, comprehensive, well-structured reports are generated, providing a detailed view of the disinformation behaviours. |
| title | A New Hybrid Intelligent Approach for Multimodal Detection of Suspected Disinformation on TikTok |
| topic | Computer Vision and Pattern Recognition Computation and Language Multimedia Symbolic Computation |
| url | https://arxiv.org/abs/2502.06893 |