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        <title>When Words Wear Masks: Detecting Malicious Intents and Hostile Impacts of Online Hate Speech</title>
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    <name type="personal">
        <namePart type="given">Priyansh</namePart>
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            <namePart type="given">Vera</namePart>
            <namePart type="family">Demberg</namePart>
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        <name type="personal">
            <namePart type="given">Lluís</namePart>
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    <abstract>Hate speech on social media poses significant challenges for content moderation and user safety. While various datasets exist for hate speech detection, existing approaches treat hate speech as a monolithic phenomenon, detecting hateful content by using simple categorical labels such as hate, offensive, or toxic. This approach fails to distinguish between the speaker’s underlying motivations and the content’s potential societal consequences. This paper introduces I2-Hate, a novel dataset with a dual taxonomy that separately captures Intent (why the speaker produced hate speech) and Impact (what harm it may cause to individuals and communities) of online hateful posts. This dual-taxonomy approach enables moderation systems to differentiate hateful content based on underlying motivation and potential harm, supporting more nuanced intervention strategies. We release the I2-Hate dataset and code publicly.</abstract>
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%0 Conference Proceedings
%T When Words Wear Masks: Detecting Malicious Intents and Hostile Impacts of Online Hate Speech
%A Singhal, Priyansh
%A Joshi, Piyush
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-381-4
%F singhal-joshi-2026-words
%X Hate speech on social media poses significant challenges for content moderation and user safety. While various datasets exist for hate speech detection, existing approaches treat hate speech as a monolithic phenomenon, detecting hateful content by using simple categorical labels such as hate, offensive, or toxic. This approach fails to distinguish between the speaker’s underlying motivations and the content’s potential societal consequences. This paper introduces I2-Hate, a novel dataset with a dual taxonomy that separately captures Intent (why the speaker produced hate speech) and Impact (what harm it may cause to individuals and communities) of online hateful posts. This dual-taxonomy approach enables moderation systems to differentiate hateful content based on underlying motivation and potential harm, supporting more nuanced intervention strategies. We release the I2-Hate dataset and code publicly.
%U https://aclanthology.org/2026.eacl-short.8/
%P 136-153

Markdown (Informal)

[When Words Wear Masks: Detecting Malicious Intents and Hostile Impacts of Online Hate Speech](https://aclanthology.org/2026.eacl-short.8/) (Singhal & Joshi, EACL 2026)

• When Words Wear Masks: Detecting Malicious Intents and Hostile Impacts of Online Hate Speech (Singhal & Joshi, EACL 2026)

ACL

• Priyansh Singhal and Piyush Joshi. 2026. When Words Wear Masks: Detecting Malicious Intents and Hostile Impacts of Online Hate Speech. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 136–153, Rabat, Morocco. Association for Computational Linguistics.