15,969 research outputs found
Gender inequality in new media: Evidence from Wikipedia
Media is critical for gender equality. I analyze Wikipedia, one of the prominent examples of new media. Using data from a survey and a randomized survey experiment, I study why women are less likely to contribute to Wikipedia, the implications of the gender gap on Wikipedia’s content, and what can be done about it. I find that: (1) gender differences in the frequency of Wikipedia use and in beliefs about one’s competence explain a large share of the gender gap in Wikipedia writing; (2) the gender gap among contributors leads to unequal coverage of topics; (3) providing information about gender inequality has a large effect on contributions
"(Weitergeleitet von Journalistin)": The Gendered Presentation of Professions on Wikipedia
Previous research has shown the existence of gender biases in the depiction
of professions and occupations in search engine results. Such an unbalanced
presentation might just as likely occur on Wikipedia, one of the most popular
knowledge resources on the Web, since the encyclopedia has already been found
to exhibit such tendencies in past studies. Under this premise, our work
assesses gender bias with respect to the content of German Wikipedia articles
about professions and occupations along three dimensions: used male vs. female
titles (and redirects), included images of persons, and names of professionals
mentioned in the articles. We further use German labor market data to assess
the potential misrepresentation of a gender for each specific profession. Our
findings in fact provide evidence for systematic over-representation of men on
all three dimensions. For instance, for professional fields dominated by
females, the respective articles on average still feature almost two times more
images of men; and in the mean, 83% of the mentioned names of professionals
were male and only 17% female.Comment: In the 9th International ACM Web Science Conference 2017 (WebSci'17),
June 25-28, 2017, Troy, NY, USA. Based on the results of the thesis:
arXiv:1702.0082
Building automated vandalism detection tools for Wikidata
Wikidata, like Wikipedia, is a knowledge base that anyone can edit. This open
collaboration model is powerful in that it reduces barriers to participation
and allows a large number of people to contribute. However, it exposes the
knowledge base to the risk of vandalism and low-quality contributions. In this
work, we build on past work detecting vandalism in Wikipedia to detect
vandalism in Wikidata. This work is novel in that identifying damaging changes
in a structured knowledge-base requires substantially different feature
engineering work than in a text-based wiki like Wikipedia. We also discuss the
utility of these classifiers for reducing the overall workload of vandalism
patrollers in Wikidata. We describe a machine classification strategy that is
able to catch 89% of vandalism while reducing patrollers' workload by 98%, by
drawing lightly from contextual features of an edit and heavily from the
characteristics of the user making the edit
The Evolution of Wikipedia's Norm Network
Social norms have traditionally been difficult to quantify. In any particular
society, their sheer number and complex interdependencies often limit a
system-level analysis. One exception is that of the network of norms that
sustain the online Wikipedia community. We study the fifteen-year evolution of
this network using the interconnected set of pages that establish, describe,
and interpret the community's norms. Despite Wikipedia's reputation for
\textit{ad hoc} governance, we find that its normative evolution is highly
conservative. The earliest users create norms that both dominate the network
and persist over time. These core norms govern both content and interpersonal
interactions using abstract principles such as neutrality, verifiability, and
assume good faith. As the network grows, norm neighborhoods decouple
topologically from each other, while increasing in semantic coherence. Taken
together, these results suggest that the evolution of Wikipedia's norm network
is akin to bureaucratic systems that predate the information age.Comment: 22 pages, 9 figures. Matches published version. Data available at
http://bit.ly/wiki_nor
Group Minds and the Case of Wikipedia
Group-level cognitive states are widely observed in human social systems, but
their discussion is often ruled out a priori in quantitative approaches. In
this paper, we show how reference to the irreducible mental states and
psychological dynamics of a group is necessary to make sense of large scale
social phenomena. We introduce the problem of mental boundaries by reference to
a classic problem in the evolution of cooperation. We then provide an explicit
quantitative example drawn from ongoing work on cooperation and conflict among
Wikipedia editors, showing how some, but not all, effects of individual
experience persist in the aggregate. We show the limitations of methodological
individualism, and the substantial benefits that come from being able to refer
to collective intentions, and attributions of cognitive states of the form
"what the group believes" and "what the group values".Comment: 21 pages, 6 figures; matches published versio
Beyond opening up the black box: Investigating the role of algorithmic systems in Wikipedian organizational culture
Scholars and practitioners across domains are increasingly concerned with
algorithmic transparency and opacity, interrogating the values and assumptions
embedded in automated, black-boxed systems, particularly in user-generated
content platforms. I report from an ethnography of infrastructure in Wikipedia
to discuss an often understudied aspect of this topic: the local, contextual,
learned expertise involved in participating in a highly automated
social-technical environment. Today, the organizational culture of Wikipedia is
deeply intertwined with various data-driven algorithmic systems, which
Wikipedians rely on to help manage and govern the "anyone can edit"
encyclopedia at a massive scale. These bots, scripts, tools, plugins, and
dashboards make Wikipedia more efficient for those who know how to work with
them, but like all organizational culture, newcomers must learn them if they
want to fully participate. I illustrate how cultural and organizational
expertise is enacted around algorithmic agents by discussing two
autoethnographic vignettes, which relate my personal experience as a veteran in
Wikipedia. I present thick descriptions of how governance and gatekeeping
practices are articulated through and in alignment with these automated
infrastructures. Over the past 15 years, Wikipedian veterans and administrators
have made specific decisions to support administrative and editorial workflows
with automation in particular ways and not others. I use these cases of
Wikipedia's bot-supported bureaucracy to discuss several issues in the fields
of critical algorithms studies, critical data studies, and fairness,
accountability, and transparency in machine learning -- most principally
arguing that scholarship and practice must go beyond trying to "open up the
black box" of such systems and also examine sociocultural processes like
newcomer socialization.Comment: 14 pages, typo fixed in v
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