Gendered Expression Online: Exploring Gendered Communication on Facebook and in a Collaborative Editing Task

College students are increasingly using digital media, such as social network sites (SNSs) and collaborative editing tools (Wikipedia), as identity exploration tools, aligning or distancing themselves from their offline selves through the online affordances of anonymity and agentic choice. The opportunities for gender fluidity available online (Armentor-Cota, 2011) provide college students with opportunities to experiment with and manipulate varied identities in a safe space where consequences of confronting identity norms may be less severe (Turkle, 1996; Shaw, 1997). Similarly, restrictive offline gender differences may diminish in online spaces, favoring a more flexible and androgynous enactment of gender (Martin, Cook, & Andrews, 2016) in certain online spaces. Even so, research has identified a significant gender gap in collaborative digital spaces such as Wikipedia (Glott, Schmidt, & Ghosh, 2010; Hill & Shaw, 2013; Lam et al., 2011; Pande, 2011). The current research examined identity choices and gendered communicative patterns online using a popular SNS, Facebook, and a simulated collaborative editing environment. Study one explored gender variations in communicative patterns on Facebook, while study two explored gender expressions in a public, collaborative editing task. Although the studies found specific gendered communicative patterns on both Facebook and Wikipedia, the majority of the online behaviors were not gender-specific and online behaviors reflected more similarities than differences between men and women, supporting a more flexible understanding of gendered expressions (Martin, Cook, & Andrews, 2016) online. Based on these studies, some offline gender differences replicated through certain online spaces, such as women favoring relationship maintenance (Facebook), women orienting towards more harmonious behaviors/environments (Facebook and Wikipedia), and gender-specific power dynamics from offline spaces (Facebook). Women also favored more positive collaborative environments and those that included at least one other female editor, while men more actively edited in a neutral environment lacking positive affirmations. Other gender differences appear to dissipate in certain online environments, illustrated by both women and men actively editing and collaborating to the same extent on a fact-based section of an essay. Furthermore, men have more often favored this type of information sharing than women in other online environments. Overall, these results find that certain offline inequalities and power dynamics may replicate in online spaces. Online gender differences appear to be nuanced in nature with regards to specific online behaviors and expressions of gender may reflect the gender composition of peers engaging in the online space

First Women, Second Sex: Gender Bias in Wikipedia

Contributing to history has never been as easy as it is today. Anyone with access to the Web is able to play a part on Wikipedia, an open and free encyclopedia. Wikipedia, available in many languages, is one of the most visited websites in the world and arguably one of the primary sources of knowledge on the Web. However, not everyone is contributing to Wikipedia from a diversity point of view; several groups are severely underrepresented. One of those groups is women, who make up approximately 16% of the current contributor community, meaning that most of the content is written by men. In addition, although there are specific guidelines of verifiability, notability, and neutral point of view that must be adhered by Wikipedia content, these guidelines are supervised and enforced by men. In this paper, we propose that gender bias is not about participation and representation only, but also about characterization of women. We approach the analysis of gender bias by defining a methodology for comparing the characterizations of men and women in biographies in three aspects: meta-data, language, and network structure. Our results show that, indeed, there are differences in characterization and structure. Some of these differences are reflected from the off-line world documented by Wikipedia, but other differences can be attributed to gender bias in Wikipedia content. We contextualize these differences in feminist theory and discuss their implications for Wikipedia policy.Comment: 10 pages, ACM style. Author's version of a paper to be presented at ACM Hypertext 201

Evolution of Privacy Loss in Wikipedia

The cumulative effect of collective online participation has an important and adverse impact on individual privacy. As an online system evolves over time, new digital traces of individual behavior may uncover previously hidden statistical links between an individual's past actions and her private traits. To quantify this effect, we analyze the evolution of individual privacy loss by studying the edit history of Wikipedia over 13 years, including more than 117,523 different users performing 188,805,088 edits. We trace each Wikipedia's contributor using apparently harmless features, such as the number of edits performed on predefined broad categories in a given time period (e.g. Mathematics, Culture or Nature). We show that even at this unspecific level of behavior description, it is possible to use off-the-shelf machine learning algorithms to uncover usually undisclosed personal traits, such as gender, religion or education. We provide empirical evidence that the prediction accuracy for almost all private traits consistently improves over time. Surprisingly, the prediction performance for users who stopped editing after a given time still improves. The activities performed by new users seem to have contributed more to this effect than additional activities from existing (but still active) users. Insights from this work should help users, system designers, and policy makers understand and make long-term design choices in online content creation systems

"(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

Female scholars need to achieve more for equal public recognition

Different kinds of "gender gap" have been reported in different walks of the scientific life, almost always favouring male scientists over females. In this work, for the first time, we present a large-scale empirical analysis to ask whether female scientists with the same level of scientific accomplishment are as likely as males to be recognised. We particularly focus on Wikipedia, the open online encyclopedia that its open nature allows us to have a proxy of community recognition. We calculate the probability of appearing on Wikipedia as a scientist for both male and female scholars in three different fields. We find that women in Physics, Economics and Philosophy are considerable less likely than men to be recognised on Wikipedia across all levels of achievement.Comment: Under revie

Privacy in crowdsourcing:a systematic review

The advent of crowdsourcing has brought with it multiple privacy challenges. For example, essential monitoring activities, while necessary and unavoidable, also potentially compromise contributor privacy. We conducted an extensive literature review of the research related to the privacy aspects of crowdsourcing. Our investigation revealed interesting gender differences and also differences in terms of individual perceptions. We conclude by suggesting a number of future research directions.</p

Interactions of cultures and top people of Wikipedia from ranking of 24 language editions

Wikipedia is a huge global repository of human knowledge, that can be leveraged to investigate interwinements between cultures. With this aim, we apply methods of Markov chains and Google matrix, for the analysis of the hyperlink networks of 24 Wikipedia language editions, and rank all their articles by PageRank, 2DRank and CheiRank algorithms. Using automatic extraction of people names, we obtain the top 100 historical figures, for each edition and for each algorithm. We investigate their spatial, temporal, and gender distributions in dependence of their cultural origins. Our study demonstrates not only the existence of skewness with local figures, mainly recognized only in their own cultures, but also the existence of global historical figures appearing in a large number of editions. By determining the birth time and place of these persons, we perform an analysis of the evolution of such figures through 35 centuries of human history for each language, thus recovering interactions and entanglement of cultures over time. We also obtain the distributions of historical figures over world countries, highlighting geographical aspects of cross-cultural links. Considering historical figures who appear in multiple editions as interactions between cultures, we construct a network of cultures and identify the most influential cultures according to this network.Comment: 32 pages. 10 figures. Submitted for publication. Supporting information is available on http://www.quantware.ups-tlse.fr/QWLIB/topwikipeople