@inproceedings{pachinger-muti-2026-never,
title = "You Never Can Tell: A Survey of Preferences on {LLM}-Generated Counterspeech in an {A}ustrian Context",
author = "Pachinger, Pia and
Muti, Arianna",
editor = "Calabrese, Agostina and
Davidson, Thomas and
de Kock, Christine and
Khurana, Urja and
Marchiori Manerba, Marta and
Piot, Paloma and
Talat, Zeerak",
booktitle = "Proceedings of the 10th Workshop on Online Abuse and Harms ({WOAH})",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.woah-1.39/",
pages = "477--489",
ISBN = "979-8-89176-487-3",
abstract = "We present the first survey study of German-language counterspeech in an Austrian context, where participants ranked LLM-generated responses to offensive comments involving xenophobia, sexual objectification, and political incivility. We compare generations from five state-of-the-art LLMs and additionally examine whether participants perceive them as human- or machine-written. Our results show substantial disagreement in rankings, indicating that counterspeech preferences are highly subjective. Although participants express a clear preference for human-written counterspeech in real-world settings, they fail to reliably identify LLM-generated responses as machine-written. These findings point to the need for personalized counterspeech systems that account for users' diverse preferences, rather than assuming a single universally effective response. Such systems could combine LLMs' capacity to generate high-quality counterspeech at scale with human involvement."
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%0 Conference Proceedings
%T You Never Can Tell: A Survey of Preferences on LLM-Generated Counterspeech in an Austrian Context
%A Pachinger, Pia
%A Muti, Arianna
%Y Calabrese, Agostina
%Y Davidson, Thomas
%Y de Kock, Christine
%Y Khurana, Urja
%Y Marchiori Manerba, Marta
%Y Piot, Paloma
%Y Talat, Zeerak
%S Proceedings of the 10th Workshop on Online Abuse and Harms (WOAH)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-487-3
%F pachinger-muti-2026-never
%X We present the first survey study of German-language counterspeech in an Austrian context, where participants ranked LLM-generated responses to offensive comments involving xenophobia, sexual objectification, and political incivility. We compare generations from five state-of-the-art LLMs and additionally examine whether participants perceive them as human- or machine-written. Our results show substantial disagreement in rankings, indicating that counterspeech preferences are highly subjective. Although participants express a clear preference for human-written counterspeech in real-world settings, they fail to reliably identify LLM-generated responses as machine-written. These findings point to the need for personalized counterspeech systems that account for users’ diverse preferences, rather than assuming a single universally effective response. Such systems could combine LLMs’ capacity to generate high-quality counterspeech at scale with human involvement.
%U https://aclanthology.org/2026.woah-1.39/
%P 477-489
Markdown (Informal)
[You Never Can Tell: A Survey of Preferences on LLM-Generated Counterspeech in an Austrian Context](https://aclanthology.org/2026.woah-1.39/) (Pachinger & Muti, WOAH 2026)
ACL