Hello everyone,
The next Research Showcase, *Gaps and Biases in Wikipedia*, will be
live-streamed Wednesday, May 18, at 9:30 AM PST/16:30 UTC. View your local
time here <https://zonestamp.toolforge.org/1652891400>.
YouTube stream:
https://www.youtube.com/watch?v=Q8FlunZ0mH4
You are welcome to ask questions via YouTube chat or on IRC at
#wikimedia-research.
This month's presentations:
Ms. Categorized: Gender, notability, and inequality on Wikipedia
By Francesca Tripodi (University of North Carolina at Chapel Hill)
For the last five decades, sociologists have argued that gender is one of
the most pervasive and insidious forms of inequality. Research demonstrates
how these inequalities persist on Wikipedia - arguably the largest
encyclopedic reference in existence. Roughly eighty percent of Wikipedia's
editors are men and pages about women and women's interests are
underrepresented. English language Wikipedia contains more than 1.5 million
biographies about notable writers, inventors, and academics, but less than
nineteen percent of these biographies are about women. To try and improve
these statistics, activists host “edit-a-thons” to increase the visibility
of notable women. While this strategy helps create several biographies
previously inexistent, it fails to address a more inconspicuous form of
gender exclusion. Drawing on ethnographic observations, interviews, and
quantitative analysis of web-scraped metadata this talk demonstrates that
women’s biographies are more frequently considered non-notable and
nominated for deletion compared to men’s biographies. This disproportionate
rate is another dimension of gender inequality on Wikipedia previously
unexplored by social scientists and provides broader insights into how
women’s achievements are (under)valued in society.
Controlled Analyses of Social Biases in Wikipedia Bios
By Yulia Tsvetkov (University of Washington)
Social biases on Wikipedia could greatly influence public opinion.
Wikipedia is also a popular source of training data for NLP models, and
subtle biases in Wikipedia narratives are liable to be amplified in
downstream NLP models. In this talk I'll present two approaches to
unveiling social biases in how people are described on Wikipedia, across
demographic attributes and across languages. First, I'll present a
methodology that isolates dimensions of interest (e.g., gender), from other
attributes (e.g., occupation). This methodology allows us to quantify
systemic differences in coverage of different genders and races, while
controlling for confounding factors. Next, I'll show an NLP case study that
uses this methodology in combination with people-centric sentiment analysis
to identify disparities in Wikipedia bios of members of the LGBTQIA+
community across three languages: English, Russian, and Spanish. Our
results surface cultural differences in narratives and signs of social
biases. Practically, these methods can be used to automatically identify
Wikipedia articles for further manual analysis—articles that might contain
content gaps or an imbalanced representation of particular social groups.
You can also watch our past research showcases here:
https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase
Emily, on behalf of the Research team
--
Emily Lescak (she / her)
Senior Research Community Officer
The Wikimedia Foundation