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    <abstract>Safety research often focuses on modelgenerated harms, but users may also direct hostility, coercion, and adversarial pressure at models. Understanding how and when that occurs is essential for accurately interpreting model behaviour, alignment drift, and real-world deployment risks. In this paper, we audit 777K English LMSYS-Chat-1M conversations with two independent detectors: an eight-category lexicon for hostility directed at the model, and the dataset’s moderation signal; and show that they capture different, weakly overlapping phenomena. The lexicon identifies insults, threats, and jailbreak coercion aimed at the assistant, while moderation flags are dominated by toxic-content solicitation rather than hostility at the model. Together, they mark about 5% of user turns; adjusting the narrower lexicon– harassment union (3.0% raw) for measured precision puts mistreatment aimed at the assistant at 0.90%. These absolute rates describe arenastyle evaluation traffic and should not be read as deployment-wide base rates. We find that user hostility varies 13× across models, driven largely by who each model attracts rather than by model behaviour: first-turn hostility spreads far wider than post-response hostility, and more than fifteenfold separates the extremes even after deduplicating opening prompts. Within conversations, assistant apologies are consistently associated with higher odds of next-turn hostility under both detectors; the effect survives restricting to non-refused prior turns and to jailbreak-free conversations, and is positive in 20 of 23 models. Yet across models, more apologetic (better-aligned) models receive less hostility overall. Finally, hostility also shows temporal structure, with coercive openings (jailbreaks) front-loading the first turn while affective hostility (insults, threats) accumulates over a session. We release the lexicon, the detector cross-validation pipeline, and all derived tables.</abstract>
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%0 Conference Proceedings
%T How User-AI Mistreatment Occurs and Matters in Conversational Systems?
%A Zeng, Fanqi
%A Hasan, Sadid
%A He, Chaocheng
%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 zeng-etal-2026-user
%X Safety research often focuses on modelgenerated harms, but users may also direct hostility, coercion, and adversarial pressure at models. Understanding how and when that occurs is essential for accurately interpreting model behaviour, alignment drift, and real-world deployment risks. In this paper, we audit 777K English LMSYS-Chat-1M conversations with two independent detectors: an eight-category lexicon for hostility directed at the model, and the dataset’s moderation signal; and show that they capture different, weakly overlapping phenomena. The lexicon identifies insults, threats, and jailbreak coercion aimed at the assistant, while moderation flags are dominated by toxic-content solicitation rather than hostility at the model. Together, they mark about 5% of user turns; adjusting the narrower lexicon– harassment union (3.0% raw) for measured precision puts mistreatment aimed at the assistant at 0.90%. These absolute rates describe arenastyle evaluation traffic and should not be read as deployment-wide base rates. We find that user hostility varies 13× across models, driven largely by who each model attracts rather than by model behaviour: first-turn hostility spreads far wider than post-response hostility, and more than fifteenfold separates the extremes even after deduplicating opening prompts. Within conversations, assistant apologies are consistently associated with higher odds of next-turn hostility under both detectors; the effect survives restricting to non-refused prior turns and to jailbreak-free conversations, and is positive in 20 of 23 models. Yet across models, more apologetic (better-aligned) models receive less hostility overall. Finally, hostility also shows temporal structure, with coercive openings (jailbreaks) front-loading the first turn while affective hostility (insults, threats) accumulates over a session. We release the lexicon, the detector cross-validation pipeline, and all derived tables.
%U https://aclanthology.org/2026.woah-1.18/
%P 204-218

Markdown (Informal)

[How User-AI Mistreatment Occurs and Matters in Conversational Systems?](https://aclanthology.org/2026.woah-1.18/) (Zeng et al., WOAH 2026)

• How User-AI Mistreatment Occurs and Matters in Conversational Systems? (Zeng et al., WOAH 2026)

ACL

• Fanqi Zeng, Sadid Hasan, and Chaocheng He. 2026. How User-AI Mistreatment Occurs and Matters in Conversational Systems?. In Proceedings of the 10th Workshop on Online Abuse and Harms (WOAH), pages 204–218, Budapest, Hungary. Association for Computational Linguistics.