Do editors or articles drive collaboration? Multilevel statistical network analysis of wikipedia coauthorship (original) (raw)

Multilevel Statistical Network Analysis of Wikipedia Coauthorship

2012

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, multitheoretical 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.

Co-authorship 2.0 : Patterns of collaboration in Wikipedia

The study of collaboration patterns in wikis can help shed light on the process of content creation by online communities. To turn a wiki's revision history into a collaboration network, we propose an algorithm that identifies as authors of a page the users who provided the most of its relevant content, measured in terms of quantity and of acceptance by the community. The scalability of this approach allows us to study the English Wikipedia community as a co-authorship network. We find evidence of the presence of a nucleus of very active contributors, who seem to spread over the whole wiki, and to interact preferentially with inexperienced users. The fundamental role played by this elite is witnessed by the growing centrality of sociometric stars in the network. Isolating the community active around a category, it is possible to study its specific dynamics and most influential authors.

structure and dynamics of Wikipedia's breaking news collaborations

Despite the fact that Wikipedia articles about current events are more popular and attract more contributions than typical articles, canonical studies of Wikipedia have only analyzed articles about pre-existing information. We expect the coauthoring of articles about breaking news incidents to exhibit high-tempo coordination dynamics which are not found in articles about historical events and information. Using 1.03 million revisions made by 158,384 users to 3,233 English Wikipedia articles about disasters, catastrophes, and conflicts since 1990, we construct "article trajectories" of editor interactions as they coauthor an article. Examining a subset of this corpus, our analysis demonstrates that articles about current events exhibit structures and dynamics distinct from those observed among articles about non-breaking events. These findings have implications for how collective intelligence systems can be leveraged to process and make sense of complex information.

Social capital increases efficiency of collaboration among Wikipedia editors

2011

Abstract In this study we measure the impact of pre-existing social capital on the efficiency of collaboration among Wikipedia editors. To construct a social network among Wikipedians we look to mutual interaction on the user talk pages of Wikipedia editors. As our data set, we analyze the communication networks associated with 3085 featured articles-the articles of highest quality in the English Wikipedia, comparing it to the networks of 80154 articles of lower quality.

Social patterns and dynamics of creativity in Wikipedia

International Journal of Organisational Design and Engineering, 2016

Our paper explores contribution and self-organising patterns of creative collaboration among Wikipedia editors as manifestations of social dynamics between the editors. We collect contribution data from a random sample of Wikipedia articles and use a novel approach of analysing the correlations between editors' contribution patterns over the lifetime of the articles. We find support for the existence of four socially conditioned personas among the editors and statistical difference in distribution of personas in articles of different qualities. Our findings add domain-specific details, features and attributes to the existing knowledge on editor roles and personas. Contributions to theories of the implicit self-organisation of collaborative innovation networks and creativity as well as implications for practise are discussed.

Network analysis of collaboration structure in Wikipedia

Proceedings of the 18th international conference on World wide web, 2009

In this paper we give models and algorithms to describe and analyze the collaboration among authors of Wikipedia from a network analytical perspective. The edit network encodes who interacts how with whom when editing an article; it significantly extends previous network models that code author communities in Wikipedia. Several characteristics summarizing some aspects of the organization process and allowing the analyst to identify certain types of authors can be obtained from the edit network. Moreover, we propose several indicators characterizing the global network structure and methods to visualize edit networks. It is shown that the structural network indicators are correlated with quality labels of the associated Wikipedia articles.

Ecology of the digital world of Wikipedia

Scientific Reports

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 of article improvem...

What Makes a Good Team of Wikipedia Editors? A Preliminary Statistical Analysis

Lecture Notes in Computer Science, 2014

The paper concerns studying the quality of teams of Wikipedia authors with statistical approach. We report preparation of a dataset containing numerous behavioural and structural attributes and its subsequent analysis and use to predict team quality. We have performed exploratory analysis using partial regression to remove the influence of attributes not related to the team itself. The analysis confirmed that the key issue significantly influencing article's quality are discussions between teem members. The second part of the paper successfully uses machine learning models to predict good articles based on features of the teams that created them.

Leveraging Editor Collaboration Patterns in Wikipedia

Predicting the positive or negative attitude of individuals towards each other in a social environment has long been of interest, with applications in many domains. We investigate this problem in the context of the collaborative editing of articles in Wikipedia, showing that there is enough information in the edit history of the articles that can be utilized for predicting the attitude of co-editors. We train a model using a distant supervision approach, by labeling interactions between editors as positive or negative depending on how these editors vote for each other in Wikipedia admin elections. We use the model to predict the attitude among other editors, who have neither run nor voted in an election. We validate our model by assessing its accuracy in the tasks of predicting the results of the actual elections, and identifying controversial articles. Our analysis reveals that the interactions in co-editing articles can accurately predict votes, although there are differences between positive and negative votes. For instance, the accuracy when predicting negative votes substantially increases by considering longer traces of the edit history. As for predicting controversial articles , we show that exploiting positive and negative interactions during the production of an article provides substantial improvements on previous attempts at detecting controversial articles in Wikipedia.