@inproceedings{markiewicz-etal-2026-detection,
    title = "From Detection to Explanation: Modeling Fine-Grained Emotional Social Influence Techniques with {LLM}s and Human Preferences",
    author = "Markiewicz, Maciej  and
      Mieleszczenko-Kowszewicz, Wiktoria  and
      Bajcar, Beata  and
      Adamczyk, Tomasz  and
      Szcz{\k{e}}sny, Aleksander  and
      Babiak, Jolanta  and
      Kazienko, Przemyslaw",
    editor = "Baez Santamaria, Selene  and
      Somayajula, Sai Ashish  and
      Yamaguchi, Atsuki",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 4: Student Research Workshop)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-srw.53/",
    pages = "715--734",
    ISBN = "979-8-89176-383-8",
    abstract = "This paper investigates the capabilities of LLMs to detect and explain fine-grained emotional social influence techniques in textual dialogues, as well as human preferences for technique explanations. We present findings from our two studies. In Study 1, a dataset of 238 Polish dialogues is introduced, each annotated with detailed span-level labels. On this data, we evaluate the performance of LLMs on two tasks: detecting 11 emotional social influence techniques and identifying text spans corresponding to specific techniques. The results indicate that current LLMs demonstrate limited effectiveness in accurately detecting fine-grained emotional social influence.In Study 2, we examine various LLM-generated explanations through human pairwise preferences and four criteria: comprehensibility, cognitive coherence, completeness, and soundness, with the latter two emerging as the most influential on general human preference. All data, including human annotations, are publicly available as the EmoSocInflu dataset (https://github.com/social-influence/emo-soc-influ). Our findings highlight a critical need for further advancement in the field. As LLM-supported manipulation grows, it is essential to promote public understanding of social influence mechanisms, enabling individuals to critically recognize and interpret the subtle forms of manipulation that shape public opinion."
}