Year: 2025
Type: conference-paper
Abstract: Political language is deeply intertwined with social identities.While social identities are often shaped by specific cultural contexts, existing NLP datasets are predominantly Englishcentric and focus on coarse-grained identity categories.We introduce HEBID, the first multilabel Hebrew corpus for social identity detection.The corpus contains 5,536 sentences from Israeli politicians' Facebook posts (Dec 2018-Apr 2021), with each sentence manually annotated for twelve nuanced social identities (e.g., Rightist, Ultra-Orthodox, Socially-oriented) selected based on their salience in national survey data.We benchmark multilabel and singlelabel encoders alongside 2B-9B-parameter decoder 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, comparing the identities portrayed in elite discourse with those prioritized by the public.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. 1
Institutions: Hebrew University of Jerusalem, Open University of Israel
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4416034905
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