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        <title>Eyes Don’t Lie: Subjective Hate Annotation and Detection with Gaze</title>
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            <namePart type="given">Mohit</namePart>
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    <abstract>Hate speech is a complex and subjective phenomenon. In this paper, we present a dataset (GAZE4HATE) that provides gaze data collected in a hate speech annotation experiment. We study whether the gaze of an annotator provides predictors of their subjective hatefulness rating, and how gaze features can improve Hate Speech Detection (HSD). We conduct experiments on statistical modeling of subjective hate ratings and gaze and analyze to what extent rationales derived from hate speech models correspond to human gaze and explanations in our data. Finally, we introduce MEANION, a first gaze-integrated HSD model. Our experiments show that particular gaze features like dwell time or fixation counts systematically correlate with annotators’ subjective hate ratings and improve predictions of text-only hate speech models.</abstract>
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%0 Conference Proceedings
%T Eyes Don’t Lie: Subjective Hate Annotation and Detection with Gaze
%A Alacam, Özge
%A Hoeken, Sanne
%A Zarrieß, Sina
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F alacam-etal-2024-eyes
%X Hate speech is a complex and subjective phenomenon. In this paper, we present a dataset (GAZE4HATE) that provides gaze data collected in a hate speech annotation experiment. We study whether the gaze of an annotator provides predictors of their subjective hatefulness rating, and how gaze features can improve Hate Speech Detection (HSD). We conduct experiments on statistical modeling of subjective hate ratings and gaze and analyze to what extent rationales derived from hate speech models correspond to human gaze and explanations in our data. Finally, we introduce MEANION, a first gaze-integrated HSD model. Our experiments show that particular gaze features like dwell time or fixation counts systematically correlate with annotators’ subjective hate ratings and improve predictions of text-only hate speech models.
%U https://aclanthology.org/2024.emnlp-main.11
%P 187-205

Markdown (Informal)

[Eyes Don’t Lie: Subjective Hate Annotation and Detection with Gaze](https://aclanthology.org/2024.emnlp-main.11) (Alacam et al., EMNLP 2024)

• Eyes Don’t Lie: Subjective Hate Annotation and Detection with Gaze (Alacam et al., EMNLP 2024)

ACL

• Özge Alacam, Sanne Hoeken, and Sina Zarrieß. 2024. Eyes Don’t Lie: Subjective Hate Annotation and Detection with Gaze. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 187–205, Miami, Florida, USA. Association for Computational Linguistics.