Two Centuries of Sexism in British Parliament: A Computational Analysis of Women's Representation in the Hansard Corpus

• URL: http://arxiv.org/abs/2608.30485v1
• Date: Mon, 31 Aug 2026 09:10:05 GMT
• Title: Two Centuries of Sexism in British Parliament: A Computational Analysis of Women's Representation in the Hansard Corpus
• Abstract summary: We analyse 6,531 speeches over 200 years of UK parliamentary debate (Hansard, 1803-2005)<n>We find that 54% of speeches opposing women's representation contain sexist content, compared to 21% of speeches that are for the cause.<n>We find that anti-suffrage rhetoric combines hostile and benevolent framing, while pro-suffrage sexism is overwhelmingly benevolent.
• Score: 18.451038946741367
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The language a legislature uses to debate women's rights, even in favour of them, encodes systematic patterns of sexism that persist across two centuries. In this work, we analyse 6,531 speeches over 200 years of UK parliamentary debate (Hansard, 1803-2005) by using large language models to classify a speaker's perspective towards women's suffrage and political representation, as well as analyse sexist speech in parliament from the lens of the Ambivalent Sexism Inventory. We also release this parliamentary dataset, an organized and metadata-enriched version of the publicly available Hansard Corpus optimized for computational social science research, with 6.7 million speeches across 1.2 million debates, with 89% gender-matching for speeches by MPs from the House of Commons. We find that 54% of speeches opposing women's representation contain sexist content, compared to 21% of speeches that are for the cause, and that the two sides use fundamentally different types of sexism: anti-suffrage rhetoric combines hostile and benevolent framing, while pro-suffrage sexism is overwhelmingly benevolent. Female MPs support women's political rights at 93% compared to 70% for male MPs, a gap that closes only after enfranchisement. Our findings are evidence that benevolent and hostile sexism are used in different rhetorical contexts in a manner consistent with the theory of Ambivalent Sexism.

Related papers

• Latent Topic Synthesis: Leveraging LLMs for Electoral Ad Analysis [51.95395936342771]
  We introduce an end-to-end framework for automatically generating an interpretable topic taxonomy from an unlabeled corpus.<n>We apply this framework to a large corpus of Meta political ads from the month ahead of the 2024 U.S. Presidential election.<n>Our approach uncovers latent discourse structures, synthesizes semantically rich topic labels, and annotates topics with moral framing dimensions.
  arXiv  Detail & Related papers  (2025-10-16T20:30:20Z)
• Benchmarking Gender and Political Bias in Large Language Models [37.192287982246526]
  We introduce EuroParlVote, a novel benchmark for evaluating large language models (LLMs) in politically sensitive contexts.<n>It links European Parliament debate speeches to roll-call vote outcomes and includes rich demographic metadata for each Member of the European Parliament (MEP)<n>Using EuroParlVote, we evaluate state-of-the-art LLMs on two tasks -- gender classification and vote prediction -- revealing consistent patterns of bias.
  arXiv  Detail & Related papers  (2025-09-07T18:23:30Z)
• Online Anti-sexist Speech: Identifying Resistance to Gender Bias in Political Discourse [0.7209758868768354]
  This study examines how five large language models classify sexist, anti-sexist, and neutral political tweets from the UK.<n>Our analysis show that models frequently misclassify anti-sexist speech as harmful, particularly during politically charged events.<n>We argue that moderation design must move beyond binary harmful/not-harmful schemas, integrate human-in-the-loop review during sensitive events, and explicitly include counter-speech in training data.
  arXiv  Detail & Related papers  (2025-08-15T12:24:22Z)
• Gender Bias and Property Taxes [50.18156030818883]
  We analyze records of more than 100,000 property tax appeal hearings and more than 2.7 years of associated audio recordings.<n>Female appellants fare systematically worse than male appellants in their hearings.<n>Our results are consistent with the idea that gender biases are driven, at least in part, by unvoiced beliefs and perceptions on the part of ARB panelists.
  arXiv  Detail & Related papers  (2024-12-17T07:14:23Z)
• A Structural Text-Based Scaling Model for Analyzing Political Discourse [0.0]
  We introduce the Structural Text-Based Scaling model to infer ideological positions of speakers for latent topics from text data. Applying STBS to U.S. Senate speeches, we identify immigration and gun violence as the most polarizing topics between the two major parties in Congress.
  arXiv  Detail & Related papers  (2024-10-14T14:05:35Z)
• Beyond Binary Gender: Evaluating Gender-Inclusive Machine Translation with Ambiguous Attitude Words [85.48043537327258]
  Existing machine translation gender bias evaluations are primarily focused on male and female genders. This study presents a benchmark AmbGIMT (Gender-Inclusive Machine Translation with Ambiguous attitude words) We propose a novel process to evaluate gender bias based on the Emotional Attitude Score (EAS), which is used to quantify ambiguous attitude words.
  arXiv  Detail & Related papers  (2024-07-23T08:13:51Z)
• VisoGender: A dataset for benchmarking gender bias in image-text pronoun resolution [80.57383975987676]
  VisoGender is a novel dataset for benchmarking gender bias in vision-language models. We focus on occupation-related biases within a hegemonic system of binary gender, inspired by Winograd and Winogender schemas. We benchmark several state-of-the-art vision-language models and find that they demonstrate bias in resolving binary gender in complex scenes.
  arXiv  Detail & Related papers  (2023-06-21T17:59:51Z)
• Quantifying Gender Biases Towards Politicians on Reddit [36.548136114372]
  Despite attempts to increase gender parity in politics, global efforts have struggled to ensure equal female representation.<n>This is likely tied to implicit gender biases against women in authority.<n>We present a comprehensive study of gender biases that appear in online political discussion.
  arXiv  Detail & Related papers  (2021-12-22T16:39:14Z)
• #ContextMatters: Advantages and Limitations of Using Machine Learning to Support Women in Politics [0.15749416770494704]
  ParityBOT was deployed across elections in Canada, the United States and New Zealand. It was used to analyse and classify more than 12 million tweets directed at women candidates and counter toxic tweets with supportive ones. We examine the rate of false negatives, where ParityBOT failed to pick up on insults directed at specific high profile women.
  arXiv  Detail & Related papers  (2021-09-30T22:55:49Z)
• Protecting gender and identity with disentangled speech representations [49.00162808063399]
  We show that protecting gender information in speech is more effective than modelling speaker-identity information. We present a novel way to encode gender information and disentangle two sensitive biometric identifiers.
  arXiv  Detail & Related papers  (2021-04-22T13:31:41Z)
• Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models [104.41668491794974]
  We quantify the usage of adjectives and verbs generated by language models surrounding the names of politicians as a function of their gender. We find that while some words such as dead, and designated are associated with both male and female politicians, a few specific words such as beautiful and divorced are predominantly associated with female politicians.
  arXiv  Detail & Related papers  (2021-04-15T15:03:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.