"(Weitergeleitet von Journalistin)"

Zagovora, Olga; Flöck, Fabian; Wagner, Claudia

doi:10.1145/3091478.3091488

Cited by 13 publications

(3 citation statements)

References 23 publications

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“…The content analysis of data pertaining to the gender gap on Wikipedia offers a comprehensive view of the research landscape in this area. Academic literature consistently highlights the bidirectional relationship between biased contribution and the gender gap in encyclopedic content (Zagovora;Flöck;Wagner, 2017;Cabrera et al, 2018). This reciprocal relationship underscores that bias in participation and content mutually reinforce each other.…”

Section: Discussionmentioning

confidence: 94%

Wikipedia gender gap: a scoping review

Ferran-Ferrer,

Boté-Vericad,

Minguillón

2023

EPI

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In this scoping review, we portray an examination of the gender gap on Wikipedia by analyzing scholarly literature from 2007 to 2022. Employing quantitative methods, the study identifies key author characteristics such as gender, disciplines, countries, and institutions. The research reveals a slight majority of female authors, followed by male authors, with limited representation from non-binary authors. Qualitatively, content analysis uncovers two central themes: addressing the contribution gap and incorporating content related to underrepresented genders. Additionally, the study assesses results on the content gap, editing and participation bias, readership imbalances, and strategies to mitigate the gender gap. Furthermore, it explores the repercussions of this gap and categorizes the contributing factors as “the women’s problem,” “the mirror effect,” and “the systemic problem.” Overall, this comprehensive review enhances our comprehension of the Wikipedia gender gap and provides valuable insights into the research landscape in this domain.

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Section: Discussionmentioning

confidence: 94%

Wikipedia gender gap: a scoping review

Ferran-Ferrer,

Boté-Vericad,

Minguillón

2023

EPI

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“…The study reveals that white individuals and those with citizenship in Europe and North America are over represented in comparison to the rest of the world. In similar lines, there exists research works [32], attributed to Wikipedia, a sister project of Wikidata in which authors tried to reveal how imbalanced the gender presentation is on the occupations pages of Wikipedia. Authors in [12] studied gender differences in various Wikipedia language (European) editions with respect to the coverage of the Members of the European Parliament (MEP).…”

Section: Related Workmentioning

confidence: 99%

Diversity matters: Robustness of bias measurements in Wikidata

Das¹,

Panda²,

Guda³

et al. 2023

Preprint

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With the widespread use of knowledge graphs (KG) in various automated AI systems and applications, it is very important to ensure that information retrieval algorithms leveraging them are free from societal biases. Previous works have depicted biases that persist in KGs, as well as employed several metrics for measuring the biases. However, such studies lack in the systematic exploration of the sensitivity of the bias measurements, through varying sources of data, or the embedding algorithms used. To address this research gap, in this work, we present a holistic analysis of bias measurement on the knowledge graph. First, we attempt to reveal data biases that surface in Wikidata for thirteen different demographics selected from seven continents. Next, we attempt to unfold the variance in the detection of biases by two different knowledge graph embedding algorithms -TransE and ComplEx. We conduct our extensive experiments on a large number of occupations sampled from the thirteen demographics with respect to the sensitive attribute, i.e., gender. Our results show that the inherent data bias that persists in KG can be altered by specific algorithm bias as incorporated by KG embedding learning algorithms. Further, we show that the choice of the state-of-the-art KG embedding algorithm has a strong impact on the ranking of biased occupations irrespective of gender. In particular, we find that the embedding algorithm ComplEx is more robust to the choice of demographics compared to TransE. Subsequently, we observe that the similarity of the biased occupations across demographics is minimal which reflects the socio-cultural differences around the globe. This is often overlooked by most of the coarse-grained approaches working at the aggregate level. We believe that this full-scale audit of the bias measurement pipeline will raise awareness among the community while deriving insights related to design choices of data and algorithms both and refrain itself from the popular dogma of "one-size-fits-all".

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“…Furthermore, visual asymmetry could be displayed in non-biographical content. For example, research on German Wikipedia found that images used to describe occupations have an evident gender asymmetry: almost half of the images from the profession articles show men, and only around 12% depict women (Zagovora et al, 2017).…”

Section: Content Buildingmentioning

confidence: 99%

Visibility layers: a framework for systematising the gender gap in Wikipedia content

Beytía¹,

Wagner²

2022

Internet Policy Review

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The gender gap in Wikipedia content is a complex phenomenon that comprises several asymmetries, discursive dimensions, and social concerns. However, there is no theoretical framework to organise this complexity consistently. Based on writings by Foucault, Deleuze and Tkacz, we interpret Wikipedia as a 'field of visibility' and provide a framework to systemise its content gaps. Then we use that model to organise the complexity of the content gender gap on Wikipedia, performing a systematic overview of the asymmetries tested in empirical research. We suggest that this analysis is relevant for the effective planning of governance processes that seek to avoid female or non-male subordination in digital platforms' discourses. Issue 1This paper is part of The gender of the platform economy, a special issue of Internet Policy Review guest-edited by Mayo Fuster Morell, Ricard Espelt and David Megias.

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"(Weitergeleitet von Journalistin)"

Cited by 13 publications

References 23 publications

Wikipedia gender gap: a scoping review

Wikipedia gender gap: a scoping review

Diversity matters: Robustness of bias measurements in Wikidata

Visibility layers: a framework for systematising the gender gap in Wikipedia content

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