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        <title>Justification of Emotional Appeal in Computational Argumentation</title>
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    <abstract>Emotional appeals (EAs) are often treated as fallacious in computational argumentation, yet they may be justified when warranted by the context. We introduce the task of predicting EA justification and present the first human-annotated dataset for this task, covering 1,176 argument–context pairs. Grounded in philosophical literature, our framework evaluates EA justification along logical and emotional dimensions. Human annotations show that justification criteria form a shared latent judgment space, with evidential proportionality and normative acceptability most strongly associated with overall justification. We further find that EAs without logical support are not always judged fully unjustified, and stronger EAs are not necessarily judged less justified. Finally, LLM experiments show that EA justification prediction remains challenging.</abstract>
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%0 Conference Proceedings
%T Justification of Emotional Appeal in Computational Argumentation
%A Chen, Yanran
%A Eger, Steffen
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F chen-eger-2026-justification
%X Emotional appeals (EAs) are often treated as fallacious in computational argumentation, yet they may be justified when warranted by the context. We introduce the task of predicting EA justification and present the first human-annotated dataset for this task, covering 1,176 argument–context pairs. Grounded in philosophical literature, our framework evaluates EA justification along logical and emotional dimensions. Human annotations show that justification criteria form a shared latent judgment space, with evidential proportionality and normative acceptability most strongly associated with overall justification. We further find that EAs without logical support are not always judged fully unjustified, and stronger EAs are not necessarily judged less justified. Finally, LLM experiments show that EA justification prediction remains challenging.
%U https://aclanthology.org/2026.emnlp-main.2209/
%P 50244-50273

Markdown (Informal)

[Justification of Emotional Appeal in Computational Argumentation](https://aclanthology.org/2026.emnlp-main.2209/) (Chen & Eger, EMNLP 2026)

• Justification of Emotional Appeal in Computational Argumentation (Chen & Eger, EMNLP 2026)

ACL

• Yanran Chen and Steffen Eger. 2026. Justification of Emotional Appeal in Computational Argumentation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 50244–50273, Budapest, Hungary. Association for Computational Linguistics.