@inproceedings{carvallo-etal-2025-hate,
    title = "Hate Explained: Evaluating {NER}-Enriched Text in Human and Machine Moderation of Hate Speech",
    author = "Carvallo, Andres  and
      Mendoza, Marcelo  and
      Fernandez, Miguel  and
      Ojeda, Maximiliano  and
      Guevara, Lilly  and
      Varela, Diego  and
      Borquez, Martin  and
      Buzeta, Nicolas  and
      Ayala, Felipe",
    editor = "Calabrese, Agostina  and
      de Kock, Christine  and
      Nozza, Debora  and
      Plaza-del-Arco, Flor Miriam  and
      Talat, Zeerak  and
      Vargas, Francielle",
    booktitle = "Proceedings of the The 9th Workshop on Online Abuse and Harms (WOAH)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.woah-1.42/",
    pages = "458--467",
    ISBN = "979-8-89176-105-6",
    abstract = "Hate speech detection is vital for creating safe online environments, as harmful content can drive social polarization. This study explores the impact of enriching text with intent and group tags on machine performance and human moderation workflows. For machine performance, we enriched text with intent and group tags to train hate speech classifiers. Intent tags were the most effective, achieving state-of-the-art F1-score improvements on the IHC, SBIC, and DH datasets, respectively. Cross-dataset evaluations further demonstrated the superior generalization of intent-tagged models compared to other pre-trained approaches. Then, through a user study (N=100), we evaluated seven moderation settings, including intent tags, group tags, model probabilities, and randomized counterparts. Intent annotations significantly improved the accuracy of the moderators, allowing them to outperform machine classifiers by 12.9{\%}. Moderators also rated intent tags as the most useful explanation tool, with a 41{\%} increase in perceived helpfulness over the control group. Our findings demonstrate that intent-based annotations enhance both machine classification performance and human moderation workflows."
}