Mental health industry faces growing concerns regarding hate speech directed at children's on social media, as exposure to such content can contribute to adverse psychological outcomes during critical stages of development. Current hate speech datasets and detection systems provide limited support for child-focused applications because they are primarily designed for adults and lack dedicated representations of age-specific characteristics associated with hate speech directed at children's. To address this gap, we introduce ChildGuard, a large-scale English dataset for child-targeted hate speech containing 351,877 annotated instances collected from X (formerly Twitter), Reddit, and YouTube. The dataset covers three age groups such as younger children's (under 11), pre-teens (11-12), and teens (13-17). ChildGuard contains two subsets such as a contextual subset (157K) and a lexical subset (194K). Evaluation using recent transformer-based models and LLMs achieves a best Macro-F1 of 82.07%, decreasing to 79.41%, 79.24%, 76.04%, and 74.88% on younger children's, contextual, implicit hate, and cross-subset settings, respectively.

翻译：心理健康行业日益关注社交媒体上针对儿童的仇恨言论，因为接触此类内容可能在儿童关键发育阶段导致不良心理影响。现有仇恨言论数据集和检测系统主要为成年人设计，缺乏针对儿童特征的年龄特异性表征，因此对面向儿童的应用场景支持有限。为填补这一空白，我们提出了ChildGuard——一个大规模面向儿童仇恨言论的英文数据集，包含从X（原Twitter）、Reddit和YouTube收集的351,877条标注实例。数据集涵盖三个年龄组：低龄儿童（11岁以下）、青春期前儿童（11-12岁）和青少年（13-17岁）。ChildGuard包含两个子集：语境子集（15.7万条）和词汇子集（19.4万条）。基于近期Transformer模型和大语言模型的评估结果显示，最佳宏F1值达到82.07%，而在低龄儿童组、语境子集、隐式仇恨和跨子集设置中分别下降至79.41%、79.24%、76.04%和74.88%。