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    <abstract>Human moderators in online discussions face a heterogeneous range of tasks, which go beyond content moderation, or policing. They also support and improve discussion quality, which is challenging to model (and evaluate) in NLP due to its inherent subjectivity and the scarcity of annotated resources. We address this gap by introducing PerspectiveMod, a dataset of online comments annotated for the question: *“Does this comment require moderation, and why?”* Annotations were collected from both expert moderators and trained non-experts. **PerspectiveMod** is unique in its intentional variation across (a) the level of moderation experience embedded in the source data (professional vs. non-professional moderation environments), (b) the annotator profiles (experts vs. trained crowdworkers), and (c) the richness of each moderation judgment, both in terms on fine-grained comment properties (drawn from argumentation and deliberative theory) and in the representation of the individuality of the annotator (socio-demographics and attitudes towards the task). We advance understanding of the task’s complexity by providing interpretation layers that account for its subjectivity. Our statistical analysis highlights the value of collecting annotator perspectives, including their experiences, attitudes, and views on AI, as a foundation for developing more context-aware and interpretively robust moderation tools.</abstract>
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%0 Conference Proceedings
%T PerspectiveMod: A Perspectivist Resource for Deliberative Moderation
%A Vecchi, Eva Maria
%A Falk, Neele
%A Quensel, Carlotta
%A Jundi, Iman
%A Lapesa, Gabriella
%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 vecchi-etal-2025-perspectivemod
%X Human moderators in online discussions face a heterogeneous range of tasks, which go beyond content moderation, or policing. They also support and improve discussion quality, which is challenging to model (and evaluate) in NLP due to its inherent subjectivity and the scarcity of annotated resources. We address this gap by introducing PerspectiveMod, a dataset of online comments annotated for the question: *“Does this comment require moderation, and why?”* Annotations were collected from both expert moderators and trained non-experts. **PerspectiveMod** is unique in its intentional variation across (a) the level of moderation experience embedded in the source data (professional vs. non-professional moderation environments), (b) the annotator profiles (experts vs. trained crowdworkers), and (c) the richness of each moderation judgment, both in terms on fine-grained comment properties (drawn from argumentation and deliberative theory) and in the representation of the individuality of the annotator (socio-demographics and attitudes towards the task). We advance understanding of the task’s complexity by providing interpretation layers that account for its subjectivity. Our statistical analysis highlights the value of collecting annotator perspectives, including their experiences, attitudes, and views on AI, as a foundation for developing more context-aware and interpretively robust moderation tools.
%U https://aclanthology.org/2025.emnlp-main.1733/
%P 34163-34186

Markdown (Informal)

[PerspectiveMod: A Perspectivist Resource for Deliberative Moderation](https://aclanthology.org/2025.emnlp-main.1733/) (Vecchi et al., EMNLP 2025)

• PerspectiveMod: A Perspectivist Resource for Deliberative Moderation (Vecchi et al., EMNLP 2025)

ACL

• Eva Maria Vecchi, Neele Falk, Carlotta Quensel, Iman Jundi, and Gabriella Lapesa. 2025. PerspectiveMod: A Perspectivist Resource for Deliberative Moderation. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 34163–34186, Suzhou, China. Association for Computational Linguistics.