@inproceedings{malik-etal-2025-speaks,
    title = "Who Speaks Matters: Analysing the Influence of the Speaker{'}s Linguistic Identity on Hate Classification",
    author = "Malik, Ananya  and
      Sharma, Kartik  and
      Bhatt, Shaily  and
      Ng, Lynnette Hui Xian",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1357/",
    doi = "10.18653/v1/2025.findings-emnlp.1357",
    pages = "24927--24937",
    ISBN = "979-8-89176-335-7",
    abstract = "Large Language Models (LLMs) offer a lucrative promise for scalable content moderation, including hate speech detection. However, they are also known to be brittle and biased against marginalised communities and dialects. This requires their applications to high-stakes tasks like hate speech detection to be critically scrutinized. In this work, we investigate the robustness of hate speech classification using LLMs particularly when explicit and implicit markers of the speaker{'}s ethnicity are injected into the input. For explicit markers, we inject a phrase that mentions the speaker{'}s linguistic identity. For the implicit markers, we inject dialectal features. By analysing how frequently model outputs flip in the presence of these markers, we reveal varying degrees of brittleness across 3 LLMs and 1 LM and 5 linguistic identities. We find that the presence of implicit dialect markers in inputs causes model outputs to flip more than the presence of explicit markers. Further, the percentage of flips varies across ethnicities. Finally, we find that larger models are more robust. Our findings indicate the need for exercising caution in deploying LLMs for high-stakes tasks like hate speech detection."
}