---
title: "Words of Warmth — paper by Mohammad (2025)"
description: "Social psychology treats Warmth and Competence as the two main axes along which people judge… 54 references, cited by 5 papers here."
authors: ["Saif M. Mohammad"]
year: 2025
venue: "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2025.acl-long.922/"
doi: "10.18653/v1/2025.acl-long.922"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/mohammad-2025-words-warmth-trust-sociability-norms/"
---
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# Words of Warmth: Trust and Sociability Norms for over 26k English Words

**Authors:** [Saif M. Mohammad](https://md.research.elamur.ai/free/en/authors/saif-mohammad/) · **Year:** 2025 · **Venue:** [Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.acl-long.922/) · [PDF](https://aclanthology.org/2025.acl-long.922.pdf) · [DOI 10.18653/v1/2025.acl-long.922](https://doi.org/10.18653/v1/2025.acl-long.922) · [OpenAlex](https://openalex.org/W4412944917)

**Citations:** 2 citations (OpenAlex, 2026-10-14) · [5](https://md.research.elamur.ai/free/en/papers/mohammad-2025-words-warmth-trust-sociability-norms/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Social psychology treats Warmth and Competence as the two main axes along which people judge others and groups. Newer work splits warmth into Trust (morality, sincerity, integrity) and Sociability (friendliness, gregariousness). Competence-like lexicons such as the NRC VAD Lexicon are large, but manually built warmth resources were tiny: the best-known one has only 341 words. Automatic expansion of such lists has also proven unreliable.

Mohammad addresses this gap with Words of Warmth. He collected crowdsourced trust and sociability association ratings for 26,188 English unigrams, using pilot-refined questionnaires and a set of quality controls. A warmth lexicon is then formed by merging the trust and sociability entries. Repeating the annotation yields very similar scores and rankings, which shows the ratings are highly reliable.

The paper demonstrates two uses. One tracks how quickly children acquire warmth, competence, trust and sociability words as they age. The other examines how these words appear in social media posts that mention various social groups. The resource gives bias, stereotype and discourse research a large, human-grounded tool. It comes with explicit guidance against misuse.

## Key points
- Terms were drawn from NRC VAD Lexicon v2, which has about 44k unigrams. Words with valence between −0.2 and +0.2 were removed, leaving 26,188 unigrams that are mostly emotionally loaded.
- Annotation ran on Amazon Mechanical Turk, mainly with US, Canadian, UK and Indian English speakers.
- Raters judged the predominant sense of each word and reported what most English speakers would think, not their own view.
- There are three lexicons: Trust, Sociability, and Warmth, which is the union of the first two. All are freely available at saifmohammad.com/warmth.html.
- The ethics section offers specific cautions:
  - Coverage is skewed toward US vocabulary.
  - The associations reflect the time of annotation.
  - Users should not infer anything from a single utterance.
  - Claims should concern word usage, not speakers' traits.
  - Comparative analyses over large token counts are preferred.

## Interesting ideas
- Before annotating, the term list was filtered by an existing valence score to drop near-neutral words, which focuses the human budget on items likely to carry signal.
- Near-synonyms and distributional neighbours can differ sharply in connotation (slip vs. fault, skinny vs. slender), and automatically expanded warmth lists did not hold up.
- Raters were asked for the common perception among English speakers rather than their personal opinion, which reduces idiosyncratic bias in labels.
- Warmth is decomposed into two finer, separately rated dimensions (trust and sociability), and the coarse one is rebuilt from them.
- The author insists that lexicon-based conclusions be comparative and drawn from many tokens, never from one sentence.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (world): When compiling value or attitude attributes into the knowledge layer, a cheap existing score could pre-filter which lemmas deserve costly manual or LLM labelling.
- **I2** (world): Evaluative attributes should not be copied blindly across WordNet synsets or embedding neighbours in the ontology; each lemma needs its own value.
- **I3** (learn): LLM teacher prompts for connotation or role labels could be framed as consensus judgments in the same way, giving more stable silver data.
- **I4** (frames): Character evaluation in fables could record trust-type cues (honesty, deceit) apart from sociability cues (friendliness), letting explanations say which kind of warmth an action signals.
- **I5** (answer): Lexicon warmth scores should serve only as weak, aggregated evidence about a character across a whole tale, always checked against a comparison baseline rather than used per sentence.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [National Research Council Canada](https://md.research.elamur.ai/free/en/government/national-research-council-canada/) — Government body, Ottawa, Canada · 60 works · 21,790 citations

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🧭 Topics (1): [English](https://md.research.elamur.ai/free/en/topics/english/) · 2,379 works

## Cite

Cite as: Saif M. Mohammad. 2025. Words of Warmth: Trust and Sociability Norms for over 26k English Words. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{mohammad2025words,
  title = {{Words of Warmth: Trust and Sociability Norms for over 26k English Words}},
  author = {Saif M. Mohammad},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.922},
  url = {https://doi.org/10.18653/v1/2025.acl-long.922}
}
```

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## References

54 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 2 November 2026*