MultiTec: A Data-Driven Multimodal Short Video Detection Framework for Healthcare Misinformation on TikTok

Abstract

With the prevalence of social media and short video sharing platforms (e.g., TikTok, YouTube Shorts), the proliferation of healthcare misinformation has become a widespread and concerning issue that threatens public health and undermines trust in mass media. This paper focuses on an important problem of detecting multimodal healthcare misinformation in short videos on TikTok. Our objective is to accurately identify misleading healthcare information that is jointly conveyed by the visual, audio, and textual content within the TikTok short videos. Three critical challenges exist in solving our problem: i) how to effectively extract information from distractive and manipulated visual content in short videos? ii) How to efficiently identify the interrelation of the heterogeneous visual and speech content in short videos? iii) How to accurately capture the complex dependency of the densely connected sequential content in short videos? To address the above challenges, we develop MultiTec, a multimodal detector that explicitly explores the audio and visual content in short videos to investigate both the sequential relation of video elements and their inter-modality dependencies to jointly detect misinformation in healthcare videos on TikTok. To the best of our knowledge, MultiTec is the first modality-aware dual-attentive short video detection model for multimodal healthcare misinformation on TikTok. We evaluate MultiTec on two real-world healthcare video datasets collected from TikTok. Evaluation results show that MultiTec achieves substantial performance gains compared to state-of-the-art baselines in accurately detecting misleading healthcare short videos.