Annotating Dimensions of Social Perception in Text: A Sentence-Level Dataset of Warmth and Competence

TL;DR

This paper introduces W&C-Sent, a new sentence-level dataset with annotations for warmth and competence, enabling better NLP analysis of social perceptions in text.

Contribution

It presents the first dataset annotated for warmth and competence at the sentence level, including data collection, annotation procedures, and model evaluations.

Findings

Large language models can identify trust, sociability, and competence in social media text.

W&C-Sent enables nuanced analysis of social perception in NLP.

The dataset supports future research at the intersection of NLP and social science.

Abstract

Warmth (W) (often further broken down intoTrust (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.