[Analytics] Re: [Wikimedia Research Showcase] March 15

Hi all, The next Research Showcase, focused on Gender and Equity on Wikipedia, will be live-streamed Wednesday, March 15, at 9:30 AM PST / 16:30 UTC. Find your local time here <https://zonestamp.toolforge.org/1678897840>. YouTube stream: https://www.youtube.com/watch?v=lw4MzJgDIzo You can join the conversation on IRC at #wikimedia-research. You can also watch our past research showcases here: https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase This month's presentations: Men Are elected, women are marriedː events gender bias on Wikipedia By *Jiao Sun, University of Southern California*Human activities can be seen as sequences of events, which are crucial to understanding societies. Disproportional event distribution for different demographic groups can manifest and amplify social stereotypes, and potentially jeopardize the ability of members in some groups to pursue certain goals. In this paper, we present the first event-centric study of gender biases in a Wikipedia corpus. To facilitate the study, we curate a corpus of career and personal life descriptions with demographic information consisting of 7,854 fragments from 10,412 celebrities. Then we detect events with a state-of-the-art event detection model, calibrate the results using strategically generated templates, and extract events that have asymmetric associations with genders. Our study discovers that the Wikipedia pages tend to intermingle personal life events with professional events for females but not for males, which calls for the awareness of the Wikipedia community to formalize guidelines and train the editors to mind the implicit biases that contributors carry. Our work also lays the foundation for future works on quantifying and discovering event biases at the corpus level. - Paperː Sun, J. & Peng, N. (2021). Men Are Elected, Women Are Married: Events Gender Bias on Wikipedia. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Conference on Natural Language Processing, 350-360. <https://aclanthology.org/2021.acl-short.45.pdf> Twitter reacts to absence of women on Wikipediaː a mixed-methods analysis of #VisibleWikiWomen campaignBy *Sneh Gupta, Guru Gobind Singh Indraprastha University*Digital gender divide (DGD) is visible in access, participation, representation, and biases against women embedded in Wikipedia, the largest digital reservoir of co-created content. This article examined the content of #VisibleWikiWomen, a global digital advocacy campaign aimed at encouraging inclusion of women voices in the global technology conversation and improving digital sustainability of feminist data on Wikipedia. In a mixed-methods study, Sentiment Analysis followed by a Feminist Critical Discourse Analysis of the campaign tweets reveals how digital gender divide manifested in the public response. An overwhelming majority of tweets expressed positive sentiment towards the objective of the campaign. An inductive reading of the coded tweets (n = 1067) generated five themes: Feminist Activism, Invisibility & Marginalization of Women, Technology for Women Empowerment, Gendered Knowledge Inequity, and Power Dynamics in the Digital Sphere. Twitter discourse presented many agitated digital users calling out the epistemic injustice on Wikipedia that goes beyond the invisibility of women. Their tweets reveal that they want an equal social platform inclusive of women of color and varied identities currently absent in the Wikipedia universe. Extracting ideas, values, and themes from new media campaigns holds unparalleled potential in the diffusion of interventions and messages on a larger scale. - Paperː Gupta, S., & Trehan, K. (2022). Twitter reacts to absence of women on Wikipedia: a mixed-methods analysis of #VisibleWikiWomen campaign. Media Asia, 49(2), 130-154. <https://www.researchgate.net/publication/356909618_Twitter_reacts_to_absence_of_women_on_Wikipedia_a_mixed-methods_analysis_of_VisibleWikiWomen_campaign> Warm regards, Emily -- Emily Lescak (she / her) Senior Research Community Officer The Wikimedia Foundation _______________________________________________ Analytics mailing list -- analytics(a)lists.wikimedia.org To unsubscribe send an email to analytics-leave(a)lists.wikimedia.org