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

arXiv: https://arxiv.org/abs/2502.06893 · HTML: https://mlsanity.com/paper/2502.06893/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 73 references that are in the map.
- First posted on arXiv 9 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [Deep Learning for Deepfakes Creation and Detection: A Survey](https://mlsanity.com/paper/1909.11573/) (2019)
- [The Limitations of Stylometry for Detecting Machine-Generated Fake News](https://mlsanity.com/paper/1908.09805/) (2019)
- [Dynamic texture analysis for detecting fake faces in video sequences](https://mlsanity.com/paper/2007.15271/) (2020)
- [ETMA: Efficient Transformer Based Multilevel Attention framework for Multimodal Fake News Detection](https://mlsanity.com/paper/2206.07331/) (2022)
- [Multi-modal Fusion using Fine-tuned Self-attention and Transfer Learning for Veracity Analysis of Web Information](https://mlsanity.com/paper/2109.12547/) (2021)
- [Is Multi-Modal Necessarily Better? Robustness Evaluation of Multi-modal Fake News Detection](https://mlsanity.com/paper/2206.08788/) (2022)
- [The Invasion of Ukraine Viewed through TikTok: A Dataset](https://mlsanity.com/paper/2301.08305/) (2023)

## Abstract (arXiv)

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.