dorsal/arxiv
View SchemaAnnotating Dimensions of Social Perception in Text: The First Sentence-Level Dataset of Warmth and Competence
| Authors | Mutaz Ayesh, Saif M. Mohammad, Nedjma Ousidhoum |
|---|---|
| Categories | |
| ArXiv ID | 2601.06316vv1 |
| URL | https://arxiv.org/abs/2601.06316 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
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 completely 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 from social media and often 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.
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"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 completely capture their contextual expression in larger text units and discourse. In this work, we introduce Warmth and Competence Sentences (W\u0026C-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\u0026C-Sent are from social media and often 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\u0026C-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.",
"arxiv_id": "2601.06316",
"authors": [
"Mutaz Ayesh",
"Saif M. Mohammad",
"Nedjma Ousidhoum"
],
"categories": [
"cs.CL"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Annotating Dimensions of Social Perception in Text: The First Sentence-Level Dataset of Warmth and Competence",
"url": "https://arxiv.org/abs/2601.06316",
"version": "v1"
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