A New Hybrid Intelligent Approach for Multimodal Detection of Suspected Disinformation on TikTok

TL;DR

This paper presents a hybrid deep learning and fuzzy logic framework for detecting suspected disinformation in TikTok videos by analyzing multimodal data including text, audio, and video cues.

Contribution

It introduces a novel hybrid approach combining deep learning and fuzzy logic for multimodal disinformation detection on social media videos.

Findings

Effective detection of disinformation in TikTok videos.

High-quality reports generated for each evaluated video.

Model scalable across various topics.

Abstract

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.