# SWEAT: Scoring Polarization of Topics across Different Corpora

arXiv: https://arxiv.org/abs/2109.07231 · HTML: https://mlsanity.com/paper/2109.07231/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 15 Sep 2021.

## Builds on (most cited first, 8 of 8)

- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [A Survey Of Cross-lingual Word Embedding Models](https://mlsanity.com/paper/1706.04902/) (2017)
- [Examining Gender Bias in Languages with Grammatical Gender](https://mlsanity.com/paper/1909.02224/) (2019)
- [Are We Consistently Biased? Multidimensional Analysis of Biases in Distributional Word Vectors](https://mlsanity.com/paper/1904.11783/) (2019)
- [Training Temporal Word Embeddings with a Compass](https://mlsanity.com/paper/1906.02376/) (2019)
- [Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora](https://mlsanity.com/paper/1606.02820/) (2016)

## Built on by (most cited first, 5 of 5)

- [World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges](https://mlsanity.com/paper/2301.05832/) (2023)
- [Large scale analysis of gender bias and sexism in song lyrics](https://mlsanity.com/paper/2208.02052/) (2022)
- [Language Invariant Properties in Natural Language Processing](https://mlsanity.com/paper/2109.13037/) (2021)
- [Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology](https://mlsanity.com/paper/2212.07547/) (2022)
- [Polarity Detection of Sustainable Development Goals in News Text](https://mlsanity.com/paper/2509.19833/) (2025)

## Abstract (arXiv)

Understanding differences of viewpoints across corpora is a fundamental task for computational social sciences. In this paper, we propose the Sliced Word Embedding Association Test (SWEAT), a novel statistical measure to compute the relative polarization of a topical wordset across two distributional representations. To this end, SWEAT uses two additional wordsets, deemed to have opposite valence, to represent two different poles. We validate our approach and illustrate a case study to show the usefulness of the introduced measure.