@inproceedings{ayesh-etal-2026-annotating,
    title = "Annotating Dimensions of Social Perception in Text: A Sentence-Level Dataset of Warmth and Competence",
    author = "Ayesh, Mutaz  and
      Mohammad, Saif M.  and
      Ousidhoum, Nedjma",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.289/",
    pages = "6374--6412",
    ISBN = "979-8-89176-390-6",
    abstract = "*Warmth* (W) (often further broken down into*Trust* (T) and *Sociability* (S)) and *Competence* (C) are central dimensions along which people evaluate individuals and social groups (Fiske, 2018). While these constructs are well established in social psychology, they are only starting to get attention in NLP research through word-level lexicons, which do not fully capture their contextual expression in larger text units and discourse. In this work, we introduce*Warmth and Competence Sentences (W C-Sent)*, the first sentence-level dataset annotated for warmth and competence. The dataset includes over 1,600 English sentence{--}target pairs annotated along three dimensions: *trust* and *sociability* (components of *warmth*), and *competence*. The sentences in W C-Sent are social media posts that express attitudes and opinions about specific individuals or social groups (the targets of our annotations). We describe the data collection, annotation, and quality-control procedures in detail, and evaluate a range of large language models (LLMs) on their ability to identify trust, sociability, and competence in text. W C-Sent provides a new resource for analyzing warmth and competence in language and supports future research at the intersection of NLP and computational social science."
}