ChildGuard: A Specialized Dataset for Combatting Child-Targeted Hate Speech

TL;DR

ChildGuard is a large, annotated dataset specifically designed to improve detection of hate speech targeting children across social media platforms, addressing limitations of previous datasets focused on adults.

Contribution

The paper introduces ChildGuard, the first extensive dataset for child-targeted hate speech, with detailed age-specific labels and analysis of model performance challenges.

Findings

State-of-the-art models perform poorly on ChildGuard

Dataset includes 351,877 examples across multiple platforms

Two subsets enable nuanced linguistic and contextual analysis

Abstract

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.