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

arXiv: https://arxiv.org/abs/2601.06316 · HTML: https://mlsanity.com/paper/2601.06316/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 59 references that are in the map.
- First posted on arXiv 9 Jan 2026.

## Builds on (most cited first, 12 of 12)

- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems](https://mlsanity.com/paper/1805.04508/) (2018)
- [Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models](https://mlsanity.com/paper/2305.18189/) (2023)
- [Best Practices in the Creation and Use of Emotion Lexicons](https://mlsanity.com/paper/2210.07206/) (2022)
- [NRC VAD Lexicon v2: Norms for Valence, Arousal, and Dominance for over 55k English Terms](https://mlsanity.com/paper/2503.23547/) (2025)
- [Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis](https://mlsanity.com/paper/2306.02213/) (2023)
- [Words of Warmth: Trust and Sociability Norms for over 26k English Words](https://mlsanity.com/paper/2506.03993/) (2025)
- [Affect, Body, Cognition, Demographics, and Emotion: The ABCDE of Text Features for Computational Affective Science](https://mlsanity.com/paper/2512.17752/) (2025)

## Abstract (arXiv)

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.