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        <title>Emotional Labor Strategy Preferences in LLM Personas</title>
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            <namePart type="given">Hung-Yi</namePart>
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    <abstract>Emotional labor is the effortful management of emotional displays to meet social or professional expectations. Personality traits have been correlated with emotional labor strategies, yet research on this link relies almost exclusively on self-report scales administered in only occupational settings. We investigate whether large language models injected with psychometrically grounded personas reproduce these personality-driven selection patterns across everyday social scenarios. We construct the first emotional labor strategy dataset of 500 socially situated events, each offering three behavioral choices corresponding to surface acting, deep acting, and genuine expression. We source 50 fictional characters from a large-scale personality repository and profile each through two parallel tracks: observer-rated bipolar adjective composites and in-character self-report items. Five LLMs evaluate all scenarios under both persona conditions. We find that models align more towards deep acting, and that Conscientiousness and Emotional Stability consistently predict this preference. Entropy analysis confirms that persona reliably influences the output and varies across models and emotions.</abstract>
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%0 Conference Proceedings
%T Emotional Labor Strategy Preferences in LLM Personas
%A Saim, Mohammad
%A Jiang, Tianyu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F saim-jiang-2026-emotional
%X Emotional labor is the effortful management of emotional displays to meet social or professional expectations. Personality traits have been correlated with emotional labor strategies, yet research on this link relies almost exclusively on self-report scales administered in only occupational settings. We investigate whether large language models injected with psychometrically grounded personas reproduce these personality-driven selection patterns across everyday social scenarios. We construct the first emotional labor strategy dataset of 500 socially situated events, each offering three behavioral choices corresponding to surface acting, deep acting, and genuine expression. We source 50 fictional characters from a large-scale personality repository and profile each through two parallel tracks: observer-rated bipolar adjective composites and in-character self-report items. Five LLMs evaluate all scenarios under both persona conditions. We find that models align more towards deep acting, and that Conscientiousness and Emotional Stability consistently predict this preference. Entropy analysis confirms that persona reliably influences the output and varies across models and emotions.
%U https://aclanthology.org/2026.findings-emnlp.595/
%P 12559-12576

Markdown (Informal)

[Emotional Labor Strategy Preferences in LLM Personas](https://aclanthology.org/2026.findings-emnlp.595/) (Saim & Jiang, Findings 2026)

• Emotional Labor Strategy Preferences in LLM Personas (Saim & Jiang, Findings 2026)

ACL

• Mohammad Saim and Tianyu Jiang. 2026. Emotional Labor Strategy Preferences in LLM Personas. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 12559–12576, Budapest, Hungary. Association for Computational Linguistics.