@inproceedings{singhal-joshi-2026-words,
    title = "When Words Wear Masks: Detecting Malicious Intents and Hostile Impacts of Online Hate Speech",
    author = "Singhal, Priyansh  and
      Joshi, Piyush",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-short.8/",
    pages = "136--153",
    ISBN = "979-8-89176-381-4",
    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."
}