首个希伯来语多标签身份识别数据集，助力分析以色列政治话语中的社会身份。
HebID: Detecting Social Identities in Hebrew-language Political Text
- 构建5536条以色列政客社交媒体文本的多标签标注数据集
- 希伯来语微调大模型表现最佳，宏F1达0.74
- 可对比精英话语与公众身份优先级，适用于非英语政治研究
政治语言与社会身份紧密关联。现有身份检测数据集多为英文、单标签且类别粗略。本文提出HebID，首个希伯来语多标签社会身份检测语料库：包含2018年12月至2021年4月期间5,536条以色列政客的Facebook发文，经人工标注十二种精细社会身份（如右翼、极端正统派、社会导向型），并基于民意调查数据进行验证。我们评估了多标签与单标签编码器，以及参数量20亿至90亿的生成式大模型，发现经过希伯来语微调的大模型效果最佳，宏F1达0.74。将分类器应用于政客的Facebook发文与议会演讲，分析了身份表达在流行度、时间趋势、聚类模式及性别差异上的异同，并结合全国性民意调查，比较精英话语与公众身份关注点的差异。HebID为希伯来语社会身份研究提供全面基础，可为其他非英语政治语境研究提供范例。
原文摘要 · Abstract (English)
Political language is deeply intertwined with social identities. While social identities are often shaped by specific cultural contexts and expressed through particular uses of language, existing datasets for group and identity detection are predominantly English-centric, single-label and focus on coarse identity categories. We introduce HebID, the first multilabel Hebrew corpus for social identity detection: 5,536 sentences from Israeli politicians' Facebook posts (Dec 2018-Apr 2021), manually annotated for twelve nuanced social identities (e.g. Rightist, Ultra-Orthodox, Socially-oriented) grounded by survey data. We benchmark multilabel and single-label encoders alongside 2B-9B-parameter generative LLMs, finding that Hebrew-tuned LLMs provide the best results (macro-$F_1$ = 0.74). We apply our classifier to politicians' Facebook posts and parliamentary speeches, evaluating differences in popularity, temporal trends, clustering patterns, and gender-related variations in identity expression. We utilize identity choices from a national public survey, enabling a comparison between identities portrayed in elite discourse and the public's identity priorities. HebID provides a comprehensive foundation for studying social identities in Hebrew and can serve as a model for similar research in other non-English political contexts.
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