TikGuard: A Deep Learning Transformer-Based Solution for Detecting Unsuitable TikTok Content for Kids

TL;DR

TikGuard is a transformer-based deep learning system designed to detect inappropriate TikTok videos for children, significantly improving content moderation accuracy and safety for young viewers.

Contribution

The paper introduces TikGuard, a novel transformer-based approach utilizing a new dataset, TikHarm, to enhance detection of unsuitable content for kids on TikTok.

Findings

Achieved 86.7% accuracy in detecting inappropriate content.

Outperformed existing methods in similar video classification tasks.

Demonstrated the effectiveness of transformer models in video moderation.

Abstract

The rise of short-form videos on platforms like TikTok has brought new challenges in safeguarding young viewers from inappropriate content. Traditional moderation methods often fall short in handling the vast and rapidly changing landscape of user-generated videos, increasing the risk of children encountering harmful material. This paper introduces TikGuard, a transformer-based deep learning approach aimed at detecting and flagging content unsuitable for children on TikTok. By using a specially curated dataset, TikHarm, and leveraging advanced video classification techniques, TikGuard achieves an accuracy of 86.7%, showing a notable improvement over existing methods in similar contexts. While direct comparisons are limited by the uniqueness of the TikHarm dataset, TikGuard's performance highlights its potential in enhancing content moderation, contributing to a safer online experience for minors. This study underscores the effectiveness of transformer models in video classification and sets a foundation for future research in this area.