# Quantifying Gender Biases Towards Politicians on Reddit

arXiv: https://arxiv.org/abs/2112.12014 · HTML: https://mlsanity.com/paper/2112.12014/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 93 references that are in the map.
- First posted on arXiv 22 Dec 2021.

## Builds on (most cited first, 22 of 22)

- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Social Bias Frames: Reasoning about Social and Power Implications of Language](https://mlsanity.com/paper/1911.03891/) (2019)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [Multi-Dimensional Gender Bias Classification](https://mlsanity.com/paper/2005.00614/) (2020)
- [What are higher-order networks?](https://mlsanity.com/paper/2104.11329/) (2021)
- [REL: An Entity Linker Standing on the Shoulders of Giants](https://mlsanity.com/paper/2006.01969/) (2020)
- [Unsupervised Discovery of Implicit Gender Bias](https://mlsanity.com/paper/2004.08361/) (2020)
- [A Framework for the Computational Linguistic Analysis of Dehumanization](https://mlsanity.com/paper/2003.03014/) (2020)
- [It's a Man's Wikipedia? Assessing Gender Inequality in an Online Encyclopedia](https://mlsanity.com/paper/1501.06307/) (2015)
- [Shirtless and Dangerous: Quantifying Linguistic Signals of Gender Bias in an Online Fiction Writing Community](https://mlsanity.com/paper/1603.08832/) (2016)
- [TalkDown: A Corpus for Condescension Detection in Context](https://mlsanity.com/paper/1909.11272/) (2019)
- [Unsupervised Discovery of Gendered Language through Latent-Variable Modeling](https://mlsanity.com/paper/1906.04760/) (2019)
- [Women Through the Glass Ceiling: Gender Asymmetries in Wikipedia](https://mlsanity.com/paper/1601.04890/) (2016)
- [Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models](https://mlsanity.com/paper/2104.07505/) (2021)
- [Controlled Analyses of Social Biases in Wikipedia Bios](https://mlsanity.com/paper/2101.00078/) (2020)
- [First Women, Second Sex: Gender Bias in Wikipedia](https://mlsanity.com/paper/1502.02341/) (2015)
- [Tie-breaker: Using language models to quantify gender bias in sports journalism](https://mlsanity.com/paper/1607.03895/) (2016)
- [Improving Pointwise Mutual Information (PMI) by Incorporating Significant Co-occurrence](https://mlsanity.com/paper/1307.0596/) (2013)
- [PoKi: A Large Dataset of Poems by Children](https://mlsanity.com/paper/2004.06188/) (2020)
- [On the Interpretability and Significance of Bias Metrics in Texts: a PMI-based Approach](https://mlsanity.com/paper/2104.06474/) (2021)

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

- [A Survey on Gender Bias in Natural Language Processing](https://mlsanity.com/paper/2112.14168/) (2021)
- [Measuring Gender Bias in West Slavic Language Models](https://mlsanity.com/paper/2304.05783/) (2023)
- [Soft-prompt Tuning for Large Language Models to Evaluate Bias](https://mlsanity.com/paper/2306.04735/) (2023)
- [Measuring Intersectional Biases in Historical Documents](https://mlsanity.com/paper/2305.12376/) (2023)
- [Causal Effects of Trigger Words in Social Media Discussions: A Large-Scale Case Study about UK Politics on Reddit](https://mlsanity.com/paper/2405.10213/) (2024)

## Abstract (arXiv)

Despite attempts to increase gender parity in politics, global efforts have struggled to ensure equal female representation. This is likely tied to implicit gender biases against women in authority. In this work, we present a comprehensive study of gender biases that appear in online political discussion. To this end, we collect 10 million comments on Reddit in conversations about male and female politicians, which enables an exhaustive study of automatic gender bias detection. We address not only misogynistic language, but also other manifestations of bias, like benevolent sexism in the form of seemingly positive sentiment and dominance attributed to female politicians, or differences in descriptor attribution. Finally, we conduct a multi-faceted study of gender bias towards politicians investigating both linguistic and extra-linguistic cues. We assess 5 different types of gender bias, evaluating coverage, combinatorial, nominal, sentimental, and lexical biases extant in social media language and discourse. Overall, we find that, contrary to previous research, coverage and sentiment biases suggest equal public interest in female politicians. Rather than overt hostile or benevolent sexism, the results of the nominal and lexical analyses suggest this interest is not as professional or respectful as that expressed about male politicians. Female politicians are often named by their first names and are described in relation to their body, clothing, or family; this is a treatment that is not similarly extended to men. On the now banned far-right subreddits, this disparity is greatest, though differences in gender biases still appear in the right and left-leaning subreddits. We release the curated dataset to the public for future studies.