ChildGuard: A Specialized Dataset for Combatting Child-Targeted Hate Speech
This addresses a serious and growing problem for children's safety on social media, but it is incremental as it focuses on dataset creation rather than a new detection method.
The authors tackled the problem of detecting child-targeted hate speech on social media by introducing ChildGuard, a large-scale English dataset with 351,877 annotated examples, which revealed significant performance drops in state-of-the-art models.
Hate speech targeting children on social media is a serious and growing problem, yet current NLP systems struggle to detect it effectively. This gap exists mainly because existing datasets focus on adults, lack age specific labels, miss nuanced linguistic cues, and are often too small for robust modeling. To address this, we introduce ChildGuard, the first large scale English dataset dedicated to hate speech aimed at children. It contains 351,877 annotated examples from X (formerly Twitter), Reddit, and YouTube, labeled by three age groups: younger children (under 11), pre teens (11--12), and teens (13--17). The dataset is split into two subsets for fine grained analysis: a contextual subset (157K) focusing on discourse level features, and a lexical subset (194K) emphasizing word-level sentiment and vocabulary. Benchmarking state of the art hate speech models on ChildGuard reveals notable drops in performance, highlighting the challenges of detecting child directed hate speech.