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.
explored the process by which articles improve most efficiently and found that articles with a small group of highly active editors and a large group of less active editors were more likely to increase in quality than articles whose editors contributed more evenly @cite_10 . They argued that this is due to the lower coordination cost when few people are primarily engaged in the construction of an article. challenged the conclusions of by showing a strong correlation between diversity of experience (global inequality) between editors who are active and positive changes in article quality @cite_17 . The visibility of articles in Wikipedia seems to be critical to their development. , showed that hiding newly created articles from Wikipedia readers in a drafting space substantially reduced the overall productivity of editors in Wikipeida @cite_23 .
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Wikipedia's success is often attributed to the large numbers of contributors who improve the accuracy, completeness and clarity of articles while reducing bias. However, because of the coordination needed to write an article collaboratively, adding contributors is costly. We examined how the number of editors in Wikipedia and the coordination methods they use affect article quality. We distinguish between explicit coordination, in which editors plan the article through communication, and implicit coordination, in which a subset of editors structure the work by doing the majority of it. Adding more editors to an article improved article quality only when they used appropriate coordination techniques and was harmful when they did not. Implicit coordination through concentrating the work was more helpful when many editors contributed, but explicit coordination through communication was not. Both types of coordination improved quality more when an article was in a formative stage. These results demonstrate the critical importance of coordination in effectively harnessing the "wisdom of the crowd" in online production environments. The success of Wikipedia and the relative high quality of its articles seem to contradict conventional wisdom. Recent studies have begun shedding light on the processes contributing to Wikipedia's success, highlighting the role of coordination and contribution inequality. In this study, we expand on these works in two ways. First, we make a distinction between global (Wikipedia-wide) and local (article-specific) inequality and investigate both constructs. Second, we explore both direct and indirect effects of these inequalities, exposing the intricate relationships between global inequality, local inequality, coordination, and article quality. We tested our hypotheses on a sample of a Wikipedia articles using structural equation modeling and found that global inequality exerts significant positive impact on article quality, while the effect of local inequality is indirect and is mediated by coordination Prior scholarship on Wikipedia's collaboration processes has examined the properties of either editors or articles, but not the interactions between both. We analyze the coauthorship network of Wikipedia articles about breaking news demanding intense coordination and compare the properties of these articles and the editors who contribute to them to articles about contemporary and historical events. Using p* ERGM methods to test a multi-level, multi-theoretical model, we identify how editors' attributes and editing patterns interact with articles' attributes and authorship history. Editors' attributes like prior experience have a stronger influence on collaboration patterns, but article attributes also play significant roles. Finally, we discuss the implications our findings and methods have for understanding the socio-material duality of collective intelligence systems beyond Wikipedia. The quality of Wikipedia articles is debatable. On the one hand, existing research indicates that not only are people willing to contribute articles but the quality of these articles is close to that found in conventional encyclopedias. On the other hand, the public has never stopped criticizing the quality of Wikipedia articles, and critics never have trouble finding low-quality Wikipedia articles. Why do Wikipedia articles vary widely in qualityq We investigate the relationship between collaboration and Wikipedia article quality. We show that the quality of Wikipedia articles is not only dependent on the different types of contributors but also on how they collaborate. Based on an empirical study, we classify contributors based on their roles in editing individual Wikipedia articles. We identify various patterns of collaboration based on the provenance or, more specifically, who does what to Wikipedia articles. Our research helps identify collaboration patterns that are preferable or detrimental for article quality, thus providing insights for designing tools and mechanisms to improve the quality of Wikipedia articles.