Women through the glass ceiling: gender asymmetries in Wikipedia

By Claudia Wagner, Eduardo Graells-Garrido, David García. 2016

165 citations recorded by the source corpus.

Contributing to the writing of history has never been as easy as it is today thanks to Wikipedia, a community-created encyclopedia that aims to document the world’s knowledge from a neutral point of view. Though everyone can participate it is well known that the editor community has a narrow diversity, with a majority of white male editors. While this participatory gender gap has been studied extensively in the literature, this work sets out to assess potential gender inequalities in Wikipedia articles along different dimensions: notability, topical focus, linguistic bias, structural properties, and meta-data presentation.We find that (i) women in Wikipedia are more notable than men, which we interpret as the outcome of a subtle glass ceiling effect; (ii) family-, gender-, and relationship-related topics are more present in biographies about women; (iii) linguistic bias manifests in Wikipedia since abstract terms tend to be used to describe positive aspects in the biographies of men and negative aspects in the biographies of women; and (iv) there are structural differences in terms of meta-data and hyperlinks, which have consequences for information-seeking activities. While some differences are expected, due to historical and social contexts, other differences are attributable to Wikipedia editors. The implications of such differences are discussed having Wikipedia contribution policies in mind. We hope that the present work will contribute to increased awareness about, first, gender issues in the content of Wikipedia, and second, the different levels on which gender biases can manifest on the Web.

Subfields: Communication, Information Systems, Molecular Biology, Human-Computer Interaction, Social Sciences & Humanities

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Citations in the corpus

Scalable Cross Lingual Pivots to Model Pronoun Gender for Translation (2020, methodology): “This is not surprising given the disparity in representation by gender found in Wikipedia (Wagner et al., 2016).”
The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpus (2021, background): “Second, even for topics it does cover, there might be biases that cannot be resolved without looking at a broader context (Wagner et al., 2016; Graells-Garrido et al., 2015).”
Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models (2021, methodology): “However, the BookCorpus data has since been shown to suffer from similar gender imbalance problems (Tan and Celis, 2019) as has English Wikipedia where, for example, only 15.5% of the biographies are of women (Wagner et al., 2016).”
Addressing Age-Related Bias in Sentiment Analysis (2018, background): “, women and familyrelated topics) [Wagner et al., 2016].”
Word Embeddings and Gender Stereotypes in Swedish and English (2019, background): “As the popularity and usage of algorithms trained no natural language grows, there is a great risk that biases present in the training data, like how men and women are treated differently in Wikipedia articles [45, 46], will remain in the result and help propagate stereotypes.”
Who Makes Trends? Understanding Demographic Biases in Crowdsourced Recommendations (2017, background): “In another research direction, many efforts attempt to quantify inequalities in social media systems, including Wikipedia (Wagner et al. 2016), Pinter-est (Gilbert et al. 2013), and Twitter (Nilizadeh et al. 2016).”
Bias in word embeddings (2020, background): “This could be because Wikipedia extensively includes biographies of historical figures, in which women are typically associated with marriage and familial relations, while men are associated with concepts such as war and governance [95, 96].”
Automatically Inferring Gender Associations from Language (2019, background): “…people talk to women and men (Voigt et al., 2018), as well as how people talk about women and men, typically in specific domains such as sports journalism (Fu et al., 2016), fiction writing (Fast et al., 2016), movie scripts (Sap et al., 2017), and Wikipedia biographies (Wagner et al., 2015, 2016).”
Undesirable biases in NLP: Averting a crisis of measurement (2022, background): “For instance (see Figure 1), we can distinguish dataset bias from model bias and consider their respective operationalizations: word frequency information (e.g., Bordia & Bowman, 2019; Wagner et al., 2016; Zhao et al., 2019), bias classifiers (e.”
Knowledge Graphs (2020, unknown): “, labels and descriptions) for certain languages [276], social biases that under-represent people of particular genders or races [544], and so forth.”
A Taxonomy of Knowledge Gaps for Wikimedia Projects (First Draft) (2020, background): “The gender gap – the fact that the majority of Wikimedia content about people focuses on male subjects – is one of the most-studied examples of content gaps and well-documented in the research literature [3, 14, 31, 69, 75, 81, 92, 102, 136, 142, 167, 168, 210, 211] and interactive tools such as the”
The Birth of Bias: A case study on the evolution of gender bias in an English language model (2022, background): “Gender asymmetries are regularly observed in word frequencies and co-occurrences in datasets (e.g. Zhao et al., 2019; Tan and Celis, 2019; Wagner et al., 2016) and in language use in general (e.g. the “male-as-norm bias”, Danesi, 2014).”