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 married? 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.
- Paper? 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 Wikipedia? 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.
- Paper? 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
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 married? 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.
- Paper? 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 Wikipedia? 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.
- Paper? 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