@inproceedings{alacam-etal-2025-disentangling,
title = "Disentangling Subjectivity and Uncertainty for Hate Speech Annotation and Modeling using Gaze",
author = {Alacam, {\"O}zge and
Hoeken, Sanne and
S{\"a}uberli, Andreas and
Gr{\"o}ner, Hannes and
Frassinelli, Diego and
Zarrie{\ss}, Sina and
Plank, Barbara},
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1460/",
doi = "10.18653/v1/2025.emnlp-main.1460",
pages = "28707--28724",
ISBN = "979-8-89176-332-6",
abstract = "Variation is inherent in opinion-based annotation tasks like sentiment or hate speech analysis. It does not only arise from errors, fatigue, or sentence ambiguity but also from genuine differences in opinion shaped by background, experience, and culture. In this paper, first, we show how annotators' confidence ratings can be great use for disentangling subjective variation from uncertainty, without relying on specific features present in the data (text, gaze, etc.). Our goal is to establish distinctive dimensions of variation which are often not clearly separated in existing work on modeling annotator variation. We illustrate our approach through a hate speech detection task, demonstrating that models are affected differently by instances of uncertainty and subjectivity. In addition, we show that human gaze patterns offer valuable indicators of subjective evaluation and uncertainty. Disclaimer: This paper contains sentences that may be offensive."
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%0 Conference Proceedings
%T Disentangling Subjectivity and Uncertainty for Hate Speech Annotation and Modeling using Gaze
%A Alacam, Özge
%A Hoeken, Sanne
%A Säuberli, Andreas
%A Gröner, Hannes
%A Frassinelli, Diego
%A Zarrieß, Sina
%A Plank, Barbara
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F alacam-etal-2025-disentangling
%X Variation is inherent in opinion-based annotation tasks like sentiment or hate speech analysis. It does not only arise from errors, fatigue, or sentence ambiguity but also from genuine differences in opinion shaped by background, experience, and culture. In this paper, first, we show how annotators’ confidence ratings can be great use for disentangling subjective variation from uncertainty, without relying on specific features present in the data (text, gaze, etc.). Our goal is to establish distinctive dimensions of variation which are often not clearly separated in existing work on modeling annotator variation. We illustrate our approach through a hate speech detection task, demonstrating that models are affected differently by instances of uncertainty and subjectivity. In addition, we show that human gaze patterns offer valuable indicators of subjective evaluation and uncertainty. Disclaimer: This paper contains sentences that may be offensive.
%R 10.18653/v1/2025.emnlp-main.1460
%U https://aclanthology.org/2025.emnlp-main.1460/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1460
%P 28707-28724
Markdown (Informal)
[Disentangling Subjectivity and Uncertainty for Hate Speech Annotation and Modeling using Gaze](https://aclanthology.org/2025.emnlp-main.1460/) (Alacam et al., EMNLP 2025)
ACL