Analyzing the Creative Editing Behavior of Wikipedia Editors (original) (raw)
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Open content web sites depend on users to produce information of value. Wikipedia is the largest and most well-known such site. Previous work has shown that a small fraction of editors -Wikipedians -do most of the work and produce most of the value. Other work has offered conjectures about how Wikipedians differ from other editors and how Wikipedians change over time. We quantify and test these conjectures. Our key findings include: Wikipedians' edits last longer; Wikipedians invoke community norms more often to justify their edits; on many dimensions of activity, Wikipedians start intensely, tail off a little, then maintain a relatively high level of activity over the course of their career. Finally, we show that the amount of work done by Wikipedians and non-Wikipedians differs significantly from their very first day. Our results suggest a design opportunity: customizing the initial user experience to improve retention and channel new users' intense energy.
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Wikipedia, a paradigmatic example of online knowledge space is organized in a collaborative, bottom-up way with voluntary contributions, yet it maintains a level of reliability comparable to that of traditional encyclopedias. The lack of selected professional writers and editors makes the judgement about quality and trustworthiness of the articles a real challenge. Here we show that a self-consistent metrics for the network defined by the edit records captures well the character of editors' activity and the articles' level of complexity. Using our metrics, one can better identify the human-labeled high-quality articles, e.g., "featured" ones, and differentiate them from the popular and controversial articles. Furthermore, the dynamics of the editor-article system is also well captured by the metrics, revealing the evolutionary pathways of articles and diverse roles of editors. We demonstrate that the collective effort of the editors indeed drives to the direction o...
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Wikipedia serves as a good example of how editors collaborate to form and maintain an article. The relationship between editors, derived from their sequence of editing activity, results in a directed network structure called the revision network, that potentially holds valuable insights into editing activity. In this paper we create revision networks to assess differences between controversial and non-controversial articles, as labelled by Wikipedia. Originating from complex networks, we apply motif analysis, which determines the under or over-representation of induced sub-structures, in this case triads of editors. We analyse 21,631 Wikipedia articles in this way, and use principal component analysis to consider the relationship between their motif subgraph ratio profiles. Results show that a small number of induced triads play an important role in characterising relationships between editors, with controversial articles having a tendency to cluster. This provides useful insight into editing behaviour and interaction capturing counter-narratives, without recourse to semantic analysis. It also provides a potentially useful feature for future prediction of controversial Wikipedia articles.
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Collaborations such as Wikipedia are a key part of the value of the modern Internet. At the same time there is concern that these collaborations are threatened by high levels of member turnover. In this paper we borrow ideas from topic analysis to editor activity on Wikipedia over time into a latent space that offers an insight into the evolving patterns of editor behavior. This latent space representation reveals a number of different categories of editor (e.g. content experts, social networkers) and we show that it does provide a signal that predicts an editor's departure from the community. We also show that long term editors gradually diversify their participation by shifting edit preference from one or two namespaces to multiple namespaces and experience relatively soft evolution in their editor profiles, while short term editors generally distribute their contribution randomly among the namespaces and experience considerably fluctuated evolution in their editor profiles.
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Understanding the social roles played by contributors to online communities can facilitate the process of task routing. In this work, we develop new techniques to find roles in Wikipedia based on editors' low-level edit types and investigate how work contributed by people from different roles affect the article quality. To do this, we first built machine-learning models to automatically identify the edit categories associated with edits. We then applied a graphical model analogous to Latent Dirichlet Allocation to uncover the latent roles in editors' edit histories. Applying this technique revealed eight different roles editors play. Finally, we validated how our identified roles collaborate to improve the quality of articles. The results demonstrate that editors carrying on different roles contribute differently in terms of edit categories and articles in different quality stages need different types of editors. Implications for editor role identification and the validation...
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The power-law distribution of participation characterizes a wide variety of technology-mediated social participation (TMSP) systems, and Wikipedia is no exception. A minority of active contributors does most of the work. While the existence of a core of highly active contributors is well documented, how those individuals came to be so active is less well understood. In this study we extend prior research on TMSP and Wikipedia by examining in detail the characteristics of the revisions that new contributors make. In particular we focus on new users who maintain a minimum level of sustained activity during their first six months. We use content analysis of individual revisions as well as other quantitative techniques to examine three research questions regarding the effect of early diversification of activity, nature vs. nurture, and associations with later administrative and organizational activity. We present analyses that address each of these questions, and conclude with implications for our understanding of the progression of participation on Wikipedia and other TMSP systems.