H eb ID : Detecting Social Identities in H ebrew-language Political Text (2025)

Abstract: Political language is deeply intertwined with social identities. While social identities are often shaped by specific cultural contexts, existing NLP datasets are predominantly English-centric 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 single-label 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

## introduction social identities-such as political ideology, religious affiliation, or demographic group membership-are powerful drivers of political behavior and public discourse @xcite ). yet existing nlp resources for identity detection remain almost entirely english-focused, single-label, and rely on coarse-grained categories (e.g., party or ethnicity). in this work, we introduce hebid, the first publicly released hebrew dataset for fine-grained, multilabel social identity detection in political text, 1 https://github.com/guymorlan/hebid/ grounded in both domain expertise and large-scale survey evidence. we frame the task as a sentence-level classification problem: the goal is to determine which, if any, of twelve social identities are being positively expressed or invoked within a given sentence. the choice of social identities is empirically grounded in survey data. we utilized 12 waves of a national survey (@xmath0,769) each of which included questions designed to identify the social identities most salient to israeli citizens (e.g., rightist, ultra-orthodox, socially-oriented). these surveyderived categories were then used to guide the annotation of 5,536 sentences sampled from facebook posts by israeli politicians @xcite , ensuring that our labels reflect real-world identity salience. we benchmark a suite of modern architectures on the facebook data-(i) multilabel encoder models; (ii) single-label encoder models; and (iii) 2b-9b-parameter decoder llms, finding that hebrewtuned decoder models (specifically, dictalm2.0) achieve the highest macro-f 1 (0.743), outperforming encoder-only baselines by over six points. we then assess cross-genre generalization by applying our best model to 500 parliamentary speech excerpts, achieving a comparable macro-f 1 of 0.72. finally, we link three complementary sources in our analysis of how identities behave: politicians' facebook posts, parliamentary speeches from the israeli parliament (knesset), and survey responses that reflect public identification. we (1) compare identity prevalence and correlations between social media, parliamentary speech, and the public; (2) document how identity discourse surges around election cycles; (3) uncover coherent "bundles" of co-occurring identities; and (4) quantify genderrelated variation in identity expression. our contributions are: we introduce a framework for creating identity corpora by linking survey data to text annotation, and release hebid, the resulting hebrew dataset. taken together, these contributions offer value on three fronts. for computational social scientists, hebid provides a novel tool for analyzing political discourse. for the nlp community, our work establishes a challenging new benchmark for multilabel classification in a low-resource language. finally, our survey-grounded annotation framework serves as a methodological template for developing similar culturally-aware resources in other non-english contexts. ## related work automatic analysis of social identity language in political text intersects several nlp subfields: group reference detection, framing and stance classification, and ideological position inference. however, existing resources remain limited in granularity, language coverage, and multilabel capacity, leaving a clear gap that our hebrew dataset fills. ## annotating social identities 3.1 panel survey to select social identities for annotation, we utilized a combination of experts and survey instruments. we utilized a representative 12-wave panel survey of the jewish population in israel (@xmath1,769), conducted between january 2019 and april 2021 @xcite ; see also @xcite ). 2 this time period contains four israeli elections held in quick succession, providing a unique opportunity to investigate political dynamics in a condensed time frame. in 12 survey waves, respondents were asked to select up to three identities they identify with, out of a list of 28. these 28 identities were chosen by a panel of experts in israeli politics as reflecting a broad spectrum of prevalent social identities, spanning ideological, value-based, religious, national, sociodemographic, and economic dimensions (e.g., conservative, leftist, nationalist, lgbtq+. see appendix a for a full list). for inclusion in the textual annotation, we selected the 12 identities that emerged as most salient in the panel survey, each consistently surpassing a 5% selection threshold in the first five waves. these identities are rightist, leftist, conservative, liberal, socially-oriented, capitalist, zionist, palestinian, honest, security-oriented, ultra-orthodox, and democrat. 3 for more information on the survey methodology, see appendix b. ## dataset to sample sentences for annotation, we compiled a corpus of facebook posts by israeli members of parliament, political parties, and viable party candidates, posted during the same timeframe as the survey (dec. 2018 to apr. 2021). this corpus comprises 64k posts containing 375k sentences. our annotated dataset contains 5,536 hebrew-language sentences sampled from 4.8k unique facebook posts. each sentence is annotated with 12 binary annotations according to whether it expresses a given identity. the dataset is split into a training set (70%), validation set (15%) and test set (15%). ## modeling and experiments we model the task of identifying social identities from text using four types of training setups: first, we establish baseline models by training separate logistic regression and linearsvc for decoder llms, we use gemma 2 in the 2b and 9b variants (gemma team, 2024), qwen3-8b (qwen team, 2025), and the hebrew-targeted dic-talm2.0 @xcite . all decoders are fine-tuned with qlora @xcite for up to 5 epochs with two learning rates (1e-5, 1e-4). we also note that two of the multilabel encoders, dictabert large and dictabert base , perform better on average than the singlelabel encoders fine-tuned for each identity separately using dictabert large as a base model. dictabert large achieved an f 1 score of 0.678 in a multi-label setting, compared to a mean f 1 of 0.659 when combining the single-label models. this indicates the benefit of joint learning and the interrelated nature of identities in this task. ## conclusion in this work, we have introduced hebid, the first publicly available multilabel hebrew corpus for fine-grained social identity detection in political text, grounded in large-scale survey evidence and expert consultation. our dataset of 5,536 sentences from israeli politicians' facebook posts is annotated for twelve empirically salient identities, enfigure 5: gender difference per identity figure 6: gender difference in identity mentions by linking three complementary sources -facebook posts, parliamentary speeches, and a national survey -we revealed systematic differences in identity prevalence, temporal surges around elections, bundles of co-occurring identities, and gendered patterns of identity expression. these findings showcase the potential of our framework to uncover nuanced political dynamics and address substantive questions in the social sciences. the hebid annotation scheme and corpus provides a comprehensive foundation for future research on identity discourse in non-english political contexts and paves the way for comparative studies across political systems. specifically, this work provides a new tool for social scientists studying political communication, a new benchmark for nlp researchers working on hebrew and multilabel classification, and a methodological template for creating similar resources cross-culturally.