A New Hybrid Intelligent Approach for Multimodal Detection of Suspected Disinformation on TikTok
The study addresses the significant challenge of identifying disinformation on social media platforms, particularly for users and content moderators on TikTok.
This study tackled the problem of detecting disinformation on TikTok, achieving a comprehensive detection framework that evaluates multimodal data. The framework generates detailed reports on disinformation behaviors for each evaluated video.
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.