A Diachronic Analysis of Human and Model Predictions on Audience Gender in How-to Guides

Nicola Fanton; Sidharth Ranjan; Titus Von Der Malsburg; Michael Roth

doi:10.18653/v1/2025.gebnlp-1.22

A Diachronic Analysis of Human and Model Predictions on Audience Gender in How-to Guides

Nicola Fanton, Sidharth Ranjan, Titus Von Der Malsburg, Michael Roth

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Abstract

We examine audience-specific how-to guides on wikiHow, in English, diachronically by comparing predictions from fine-tuned language models and human judgments. Using both early and revised versions, we quantitatively and qualitatively study how gender-specific features are identified over time. While language model performance remains relatively stable in terms of macro F₁-scores, we observe an increased reliance on stereotypical tokens. Notably, both models and human raters tend to overpredict women as an audience, raising questions about bias in the evaluation of educational systems and resources.

Anthology ID:: 2025.gebnlp-1.22
Volume:: Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
Month:: August
Year:: 2025
Address:: Vienna, Austria
Editors:: Agnieszka Faleńska, Christine Basta, Marta Costa-jussà, Karolina Stańczak, Debora Nozza
Venues:: GeBNLP | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 242–255
Language:
URL:: https://aclanthology.org/2025.gebnlp-1.22/
DOI:: 10.18653/v1/2025.gebnlp-1.22
Bibkey:
Cite (ACL):: Nicola Fanton, Sidharth Ranjan, Titus Von Der Malsburg, and Michael Roth. 2025. A Diachronic Analysis of Human and Model Predictions on Audience Gender in How-to Guides. In Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP), pages 242–255, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):: A Diachronic Analysis of Human and Model Predictions on Audience Gender in How-to Guides (Fanton et al., GeBNLP 2025)
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PDF:: https://aclanthology.org/2025.gebnlp-1.22.pdf

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@inproceedings{fanton-etal-2025-diachronic,
    title = "A Diachronic Analysis of Human and Model Predictions on Audience Gender in How-to Guides",
    author = "Fanton, Nicola  and
      Ranjan, Sidharth  and
      Malsburg, Titus Von Der  and
      Roth, Michael",
    editor = "Fale{\'n}ska, Agnieszka  and
      Basta, Christine  and
      Costa-juss{\`a}, Marta  and
      Sta{\'n}czak, Karolina  and
      Nozza, Debora",
    booktitle = "Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.gebnlp-1.22/",
    doi = "10.18653/v1/2025.gebnlp-1.22",
    pages = "242--255",
    ISBN = "979-8-89176-277-0",
    abstract = "We examine audience-specific how-to guides on wikiHow, in English, diachronically by comparing predictions from fine-tuned language models and human judgments. Using both early and revised versions, we quantitatively and qualitatively study how gender-specific features are identified over time. While language model performance remains relatively stable in terms of macro F$_1$-scores, we observe an increased reliance on stereotypical tokens. Notably, both models and human raters tend to overpredict women as an audience, raising questions about bias in the evaluation of educational systems and resources."
}

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%0 Conference Proceedings
%T A Diachronic Analysis of Human and Model Predictions on Audience Gender in How-to Guides
%A Fanton, Nicola
%A Ranjan, Sidharth
%A Malsburg, Titus Von Der
%A Roth, Michael
%Y Faleńska, Agnieszka
%Y Basta, Christine
%Y Costa-jussà, Marta
%Y Stańczak, Karolina
%Y Nozza, Debora
%S Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
%D 2025
%8 August
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-277-0
%F fanton-etal-2025-diachronic
%X We examine audience-specific how-to guides on wikiHow, in English, diachronically by comparing predictions from fine-tuned language models and human judgments. Using both early and revised versions, we quantitatively and qualitatively study how gender-specific features are identified over time. While language model performance remains relatively stable in terms of macro F₁-scores, we observe an increased reliance on stereotypical tokens. Notably, both models and human raters tend to overpredict women as an audience, raising questions about bias in the evaluation of educational systems and resources.
%R 10.18653/v1/2025.gebnlp-1.22
%U https://aclanthology.org/2025.gebnlp-1.22/
%U https://doi.org/10.18653/v1/2025.gebnlp-1.22
%P 242-255

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Markdown (Informal)

[A Diachronic Analysis of Human and Model Predictions on Audience Gender in How-to Guides](https://aclanthology.org/2025.gebnlp-1.22/) (Fanton et al., GeBNLP 2025)

A Diachronic Analysis of Human and Model Predictions on Audience Gender in How-to Guides (Fanton et al., GeBNLP 2025)

ACL

Nicola Fanton, Sidharth Ranjan, Titus Von Der Malsburg, and Michael Roth. 2025. A Diachronic Analysis of Human and Model Predictions on Audience Gender in How-to Guides. In Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP), pages 242–255, Vienna, Austria. Association for Computational Linguistics.