<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bokaei-etal-2026-parshate">
    <titleInfo>
        <title>ParsHate: A Benchmark Dataset for Hate and Target Detection in Persian</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Zahra</namePart>
        <namePart type="family">Bokaei</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Walid</namePart>
        <namePart type="family">Magdy</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Bonnie</namePart>
        <namePart type="family">Webber</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Hung-Yi</namePart>
            <namePart type="family">Lee</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sunipa</namePart>
            <namePart type="family">Dev</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jessy</namePart>
            <namePart type="family">Li</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Desmond</namePart>
            <namePart type="family">Elliott</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Budapest, Hungary</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-459-0</identifier>
    </relatedItem>
    <abstract>We introduce ParsHate, a manually annotated dataset of 10,000 Persian tweets spanning 2013–2022, representing the first decade-long benchmark for hate speech detection in Persian. The dataset contains 31% hateful content and supports both hate detection and multi-label fine-grained target identification across seven structured target categories. ParsHate also distinguishes explicit and implicit hate, marks explicit and implicit targets, and provides span-level rationales. Data collection combines random and score-stratified temporal sampling to reduce keyword-driven bias while preserving natural label distributions. Applying SOTA models for Persian hate-speech detection on ParsHate shows moderate performance (79% F1), especially with samples from earlier years, and low performance with target identification (25.5% macro-F1). This emphasizes the diverse sampling of hate speech in ParsHate and its challenging nature that requires more advanced methods for better performance. Dataset is made publicly available.</abstract>
    <identifier type="citekey">bokaei-etal-2026-parshate</identifier>
    <location>
        <url>https://aclanthology.org/2026.emnlp-main.1406/</url>
    </location>
    <part>
        <date>2026-10</date>
        <extent unit="page">
            <start>32132</start>
            <end>32149</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T ParsHate: A Benchmark Dataset for Hate and Target Detection in Persian
%A Bokaei, Zahra
%A Magdy, Walid
%A Webber, Bonnie
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F bokaei-etal-2026-parshate
%X We introduce ParsHate, a manually annotated dataset of 10,000 Persian tweets spanning 2013–2022, representing the first decade-long benchmark for hate speech detection in Persian. The dataset contains 31% hateful content and supports both hate detection and multi-label fine-grained target identification across seven structured target categories. ParsHate also distinguishes explicit and implicit hate, marks explicit and implicit targets, and provides span-level rationales. Data collection combines random and score-stratified temporal sampling to reduce keyword-driven bias while preserving natural label distributions. Applying SOTA models for Persian hate-speech detection on ParsHate shows moderate performance (79% F1), especially with samples from earlier years, and low performance with target identification (25.5% macro-F1). This emphasizes the diverse sampling of hate speech in ParsHate and its challenging nature that requires more advanced methods for better performance. Dataset is made publicly available.
%U https://aclanthology.org/2026.emnlp-main.1406/
%P 32132-32149

Markdown (Informal)

[ParsHate: A Benchmark Dataset for Hate and Target Detection in Persian](https://aclanthology.org/2026.emnlp-main.1406/) (Bokaei et al., EMNLP 2026)

• ParsHate: A Benchmark Dataset for Hate and Target Detection in Persian (Bokaei et al., EMNLP 2026)

ACL

• Zahra Bokaei, Walid Magdy, and Bonnie Webber. 2026. ParsHate: A Benchmark Dataset for Hate and Target Detection in Persian. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 32132–32149, Budapest, Hungary. Association for Computational Linguistics.