--- title: "HeBID: Detecting Social Identities in Hebrew-language Political Text — Mor-Lan 2025" description: "HeBID is a multi-label Hebrew corpus of 5,536 sentences from Israeli politicians' Facebook posts… 19 references, cited by 1 paper here." authors: ["Guy Mor-Lan", "Naama Rivlin-Angert", "Yael R. Kaplan", "Tamir Sheafer", "Shaul R. Shenhav"] year: 2025 venue: "Findings of the Association for Computational Linguistics: EMNLP 2025" source: "https://aclanthology.org/2025.findings-emnlp.523/" doi: "10.18653/v1/2025.findings-emnlp.523" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/lan-2025-hebid-detecting-social-identities-hebrew/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/lan-2025-hebid-detecting-social-identities-hebrew/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2025](https://md.research.elamur.ai/free/en/papers/2025/page/3/) / HeBID: Detecting Social Identities in Hebrew-language Political Text # HeBID: Detecting Social Identities in Hebrew-language Political Text **Authors:** [Guy Mor-Lan](https://md.research.elamur.ai/free/en/authors/guy-mor-lan/); Naama Rivlin-Angert; Yael R. Kaplan; [Tamir Sheafer](https://md.research.elamur.ai/free/en/authors/tamir-sheafer/); [Shaul R. Shenhav](https://md.research.elamur.ai/free/en/authors/shaul-r-shenhav/) · **Year:** 2025 · **Venue:** Findings of EMNLP 2025, pp. 9850–9870 **Link:** [aclanthology.org/2025.findings-emnlp.523](https://aclanthology.org/2025.findings-emnlp.523/) · library `findings-2025-lan-hebid-detecting-social-identities-hebrew` · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (ACL Anthology) [**0** citations](https://openalex.org/works?filter=cites:W4416034905) OpenAlex, 2026-10-14 · **1** citations in our library of the 140,884 papers whose bibliographies we read · [Findings of the Association for Computational Linguistics: EMNLP 2025](https://aclanthology.org/volumes/2025.findings-emnlp/) · [licence](https://creativecommons.org/licenses/by/4.0/) - [Publisher's page](https://aclanthology.org/2025.findings-emnlp.523/) - [PDF](https://aclanthology.org/2025.findings-emnlp.523.pdf) - [DOI 10.18653/v1/2025.findings-emnlp.523](https://doi.org/10.18653/v1/2025.findings-emnlp.523) - [OpenAlex](https://openalex.org/W4416034905) **Authors (5)** 1. Guy Mor-Lan 2. Naama Rivlin-Angert 3. [Yael R. Kaplan](https://openalex.org/A5013877300) [ORCID](https://orcid.org/0000-0002-6172-0744) · h 3 · 22 citations · 11 works 4. [Tamir Sheafer](https://openalex.org/A5075955866) [ORCID](https://orcid.org/0000-0002-9242-2264) · h 32 · 6,278 citations · 144 works 5. [Shaul R. Shenhav](https://openalex.org/A5056845000) [ORCID](https://orcid.org/0000-0002-8820-3579) · h 19 · 1,558 citations · 99 works Citations and author metrics: OpenAlex, 2026-10-14; authors with the same name are not always told apart. ## What it is about HeBID is a multi-label Hebrew corpus of 5,536 sentences from Israeli politicians' Facebook posts (December 2018 to April 2021). Each sentence is marked for which of twelve social identities (for example Rightist, Leftist, Ultra-Orthodox, Zionist, Honest) it positively expresses; oppositional mentions are not labelled. The twelve were chosen by salience in a twelve-wave panel survey (N = 1,769) with a 5% threshold, so the labels follow what the public recognises. Two authors annotated, with mean Cohen's κ of 0.77. The best model is a Hebrew-tuned decoder LLM (DictaLM 2.0) at macro-F1 0.743, more than six points above the encoder models. It transfers to 500 Knesset speech excerpts at 0.72. The authors then trace prevalence, election-time surges, co-occurrence bundles and gender differences across posts, speeches and the survey. ## Ideas - I2. Choose the domain set by measurement: take a usage sample, count how often each candidate register or domain (slang, formal, humour) occurs, and keep those above a fixed threshold, as the paper does with the survey. ## For Mova - I1. A positive-only rule for `prag` labels: a pragmatic marker fires only on positive use, and oppositional uses are recorded in a separate field. This keeps "they are corrupt" from counting as a clean-hands claim. - I3. Measure co-occurrence bundles: count which labels co-occur in one sentence (21% of its sentences carry two or more), and compare them with the frames `prag` produces, as a test of multi-label output. I1 is a rule for `prag`, and it belongs with the register notes in [languages/he/09-pragmatics-politeness.md](https://mova.elamur.ai/free/en/docs/languages/he/09-pragmatics-politeness/). I2 is a selection step for the domains of `prag` and the register classes of [languages/he/08-lexicon-register.md](https://mova.elamur.ai/free/en/docs/languages/he/08-lexicon-register/). I3 is a measurement for `prag`. ## Institutions [🎓**Hebrew University of Jerusalem** Jerusalem · Israel 42 works · 8,354 citations](https://md.research.elamur.ai/free/en/universities/hebrew-university-of-jerusalem/) · [🎓**Open University of Israel** Raanana · Israel 13 works · 2,012 citations](https://md.research.elamur.ai/free/en/universities/open-university-of-israel/) Topics[Hebrew & Aramaic](https://md.research.elamur.ai/free/en/topics/hebrew-aramaic/) · [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) ## Cite Cite as Guy Mor-Lan, Naama Rivlin-Angert, Yael R. Kaplan, Tamir Sheafer and Shaul R. Shenhav. 2025. HeBID: Detecting Social Identities in Hebrew-language Political Text. Findings of the Association for Computational Linguistics: EMNLP 2025. The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2025.findings-emnlp.523](https://doi.org/10.18653/v1/2025.findings-emnlp.523) ```bibtex @inproceedings{morlan2025hebid, title = {{HeBID: Detecting Social Identities in Hebrew-language Political Text}}, author = {Guy Mor-Lan, Naama Rivlin-Angert, Yael R. Kaplan, Tamir Sheafer and Shaul R. Shenhav}, booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2025}, year = {2025}, doi = {10.18653/v1/2025.findings-emnlp.523}, url = {https://doi.org/10.18653/v1/2025.findings-emnlp.523} } ``` - [← previous Harmonizing Annotation of Turkic Postverbial Constructions: A Comparative Study of UD Treebanks](https://md.research.elamur.ai/free/en/papers/akhundjanova-2025-harmonizing-annotation-turkic-postverbial-constructions/) - [next → Integrating Neural and Symbolic Components in a Model of Pragmatic Question-Answering](https://md.research.elamur.ai/free/en/papers/tsvilodub-2025-integrating-neural-symbolic-components-model/) - [↑ back to papers of 2025](https://md.research.elamur.ai/free/en/papers/2025/page/3/) ## References 19 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Dallas Card, Amber Boydstun, Justin Gross, Philip Resnik, and Noah Smith. 2015. 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