MTikGuard System: A Transformer-Based Multimodal System for Child-Safe Content Moderation on TikTok
This addresses the challenge of real-time content moderation for children on social media, though it is incremental with improvements in dataset and architecture.
The paper tackles the problem of detecting harmful content on TikTok for child safety by presenting MTikGuard, a multimodal system that achieves 89.37% accuracy and 89.45% F1-score on an expanded dataset.
With the rapid rise of short-form videos, TikTok has become one of the most influential platforms among children and teenagers, but also a source of harmful content that can affect their perception and behavior. Such content, often subtle or deceptive, challenges traditional moderation methods due to the massive volume and real-time nature of uploads. This paper presents MTikGuard, a real-time multimodal harmful content detection system for TikTok, with three key contributions: (1) an extended TikHarm dataset expanded to 4,723 labeled videos by adding diverse real-world samples, (2) a multimodal classification framework integrating visual, audio, and textual features to achieve state-of-the-art performance with 89.37% accuracy and 89.45% F1-score, and (3) a scalable streaming architecture built on Apache Kafka and Apache Spark for real-time deployment. The results demonstrate the effectiveness of combining dataset expansion, advanced multimodal fusion, and robust deployment for practical large-scale social media content moderation. The dataset is available at https://github.com/ntdat-8324/MTikGuard-System.git.