构建首个针对儿童的仇恨言论数据集，助力精准识别网络儿童伤害内容。
ChildGuard: A Specialized Dataset for Combatting Child-Targeted Hate Speech
- 聚焦儿童群体，构建覆盖三类年龄的仇恨言论标注数据集。
- 使用大模型测试达82.07%的宏平均F1，但对隐性仇恨检测效果下降明显。
- 适合从事未成年人网络保护、内容安全与社会计算的研究者使用。
心理健康领域日益关注社交媒体中针对儿童的仇恨言论，因其可能在关键发育阶段引发不良心理影响。现有仇恨言论数据集和检测系统多针对成人设计，缺乏对儿童特有特征的专门刻画。为此，我们提出ChildGuard，一个大规模英文儿童目标仇恨言论数据集，涵盖351,877个标注样本，来自X（原Twitter）、Reddit和YouTube。数据集分为三个年龄段：幼龄儿童（<11岁）、学龄前青少年（11-12岁）和青少年（13-17岁）。包含两个子集：上下文子集（15.7万条）与词汇子集（19.4万条）。采用最新Transformer模型与大语言模型评估，最佳宏观F1达82.07%，但在幼龄儿童、上下文、隐性仇恨及跨子集场景下分别降至79.41%、79.24%、76.04%和74.88%。
原文摘要 · Abstract (English)
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.
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