Mohammad Omar Khursheed
Author directory2026
Two Centuries of Sexism in British Parliament: A Computational Analysis of Women’s Representation in the Hansard Corpus
Mohammad Omar Khursheed | Mandira Sawkar | Ashiqur R. KhudaBukhsh
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Mohammad Omar Khursheed | Mandira Sawkar | Ashiqur R. KhudaBukhsh
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
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.
2020
Analyzing Gender Bias within Narrative Tropes
Dhruvil Gala | Mohammad Omar Khursheed | Hannah Lerner | Brendan O’Connor | Mohit Iyyer
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
Dhruvil Gala | Mohammad Omar Khursheed | Hannah Lerner | Brendan O’Connor | Mohit Iyyer
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
Popular media reflects and reinforces societal biases through the use of tropes, which are narrative elements, such as archetypal characters and plot arcs, that occur frequently across media. In this paper, we specifically investigate gender bias within a large collection of tropes. To enable our study, we crawl tvtropes.org, an online user-created repository that contains 30K tropes associated with 1.9M examples of their occurrences across film, television, and literature. We automatically score the “genderedness” of each trope in our TVTROPES dataset, which enables an analysis of (1) highly-gendered topics within tropes, (2) the relationship between gender bias and popular reception, and (3) how the gender of a work’s creator correlates with the types of tropes that they use.