Title: Monitoring the Gender Gap with Wikidata Human Gender Indicators

Authors: Maximilian Klein (GroupLens Research), Harsh Gupta, Vivek Rai (Indian Institute of Technology, Kharagpur), Piotr Konieczny (Hanyang University) and Haiyi Zhu (GroupLens Research)

Abstract: The gender gap in Wikipedia’s content, specifically in the representation of women in biographies, is well-known but has been difficult to measure. Furthermore the impacts of efforts to address this gender gap have received little attention. To investigate we utilise Wikidata, the database that feeds Wikipedia, and introduce the “Wikidata Human Gender Indicators” (WHGI), a free and open source, longitudinal, biographical dataset monitoring gender disparities across time, space, culture, occupation and language. Through these lenses we show how the representation of women is changing along 11 dimensions. Validations of WHGI are presented against three exogenous datasets: the world’s historical population, “traditional” gender-disparity indices (GDI, GEI, GGGI and SIGI), and occupational gender according to the US Bureau of Labor Statistics. Furthermore, to demonstrate its general use in research, we revisit previously published findings on Wikipedia’s gender bias that can be strengthened by WHGI.

This contribution to OpenSym 2016 will be made available as part of the OpenSym 2016 proceedings on or after August 17, 2016.

Title: Mining team characteristics to predict Wikipedia article quality

Authors: Grace Gimon Betancourt, Armando Segnini, Carlos Trabuco, Amira Rezgui and Nicolas Jullien (Télécom Bretagne)

Abstract: In this study, we were interested in studying which characteristics of virtual teams are good predictors for the quality of their production. The experiment involved obtaining the Spanish Wikipedia database dump and applying different data mining techniques sui- table for large data sets to label the whole set of articles according to their quality (comparing them with the Featured/Good Articles, or FA/GA). Then we created the attributes that describe the characteristics of the team who produced the articles and using decision tree methods, we obtained the most relevant characteristics of the teams that produced FA/GA. The team’s maximum efficiency and the total length of contribution are the most important predictors. This article contributes to the literature on virtual team organization.

This contribution to OpenSym 2016 will be made available as part of the OpenSym 2016 proceedings on or after August 17, 2016.

Title: An Empirical Evaluation of Property Recommender Systems for Wikidata and Collaborative Knowledge Bases

Authors: Eva Zangerle, Wolfgang Gassler, Martin Pichl, Stefan Steinhauser, Günther Specht (University of Innsbruck)

Abstract: The Wikidata platform is a crowdsourced, structured knowledgebase aiming to provide integrated, free and languageagnostic facts which are amongst others used by Wikipedias. Users who actively enter, review and revise data on Wikidata are assisted by a property suggesting system which provides users with properties that might also be applicable to a given item. We argue that evaluating and subsequently improving this recommendation mechanism and hence, assisting users, can directly contribute to an even more integrated, consistent and extensive knowledge base serving a huge variety of applications. However, the quality and usefulness of such recommendations has not been evaluated yet. In this work, we provide the first evaluation of different approaches aiming to provide users with property recommendations in the process of curating information on Wikidata. We compare the approach currently facilitated on Wikidata with two state-of-the-art recommendation approaches stemming from the field of RDF recommender systems and collaborative information systems. Further, we also evaluate hybrid recommender systems combining these approaches. Our evaluations show that the current recommendation algorithm works well in regards to recall and precision, reaching a recall@7 of 79.71% and a precision@7 of 27.97%. We also find that generally, incorporating contextual as well as classifying information into the computation of property recommendations can further improve its performance significantly.

This contribution to OpenSym 2016 will be made available as part of the OpenSym 2016 proceedings on or after August 17, 2016.

Title: Evaluating and Improving Navigability of Wikipedia: A Comparative Study of Eight Language Editions

Authors: Daniel Lamprecht (KTI, Graz University of Technology), Dimitar Dimitrov (GESIS – Leibniz Institute for the Social Sciences), Denis Helic (KTI, Graz University of Technology) and Markus Strohmaier (GESIS – Leibniz Institute for the Social Sciences and University of Koblenz-Landau)

Abstract: Wikipedia supports its users to reach a wide variety of goals: looking up facts, researching a topic, making an edit or simply browsing to pass time. Some of these goals, such as the lookup of facts, can be effectively supported by search functions. However, for other use cases such as researching an unfamiliar topic, users need to rely on the links to connect articles. In this paper, we investigate the state of navigability in the article networks of eight language versions of Wikipedia. We find that, when taking all links of articles into account, all language versions enable mutual reachability for almost all articles. However, previous research has shown that visitors of Wikipedia focus most of their attention on the areas located close to the top. We therefore investigate different restricted navigational views that users could have when looking at articles. We find that restricting the view of articles strongly limits the navigability of the resulting networks and impedes navigation. Based on this analysis we then propose a link recommendation method to augment the link network to improve navigability in the network. Our approach selects links from a less restricted view of the article and proposes to move these links into more visible sections. The recommended links are therefore relevant for the article. Our results are relevant for researchers interested in the navigability of Wikipedia and open up new avenues for link recommendations in Wikipedia editing.

This contribution to OpenSym 2016 will be made available as part of the OpenSym 2016 proceedings on or after August 17, 2016.