Network analysis of collaboration structure in Wikipedia

Brandes, Ulrik; Kenis, Patrick; Lerner, Jürgen; Raaij, Denise van

doi:10.1145/1526709.1526808

Cited by 145 publications

(113 citation statements)

References 26 publications

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“…Because networks can be very heavily connected, they are sometimes called complex networks. Complex networks been used to model citation networks (Garfield 1955;Price 1965;Hummon and Doreian 1989;Vazquez 2001;Albert and Barabási 2002, p. 53), blogospheres (Adar and Zhang 2004), design networks (Eckert and Stacey 2000;Grace 2011, p. 19), Wikipedia networks (Schönhofen 2006;Zlatic et al 2006;Brandes et al 2009;Massa 2011;Aragon et al 2012) and births and deaths (Schich et al 2014), to name a small percentage of the works available.…”

Section: The Implicit Inspiration Networkmentioning

confidence: 99%

Conceptualising Inspiration Networks in Game Design

Tomitsch

Bednarz

2015

ToDIGRA

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This paper examines the concept of inspiration and proposes a design process for inspiration networks using survey data from Global Game Jam. Global Game Jam is a 48-hour game development competition that invites anyone to participate worldwide. The study illuminates the presence of the famous 'six degrees of separation' effect in these inspiration networks, leveraged to model ideations as related concepts, identifying four major lodestars. It follows research through design, embodying the body of knowledge in the inspiration network as a design artifact. Inspiration networks offer a novel way to discover game genres that have not yet been formally recognised, but have displayed a large influence. The findings offer three design implications: an understanding gained by "thinking with networks", the ability to identify emerging game genres, and the support for intuitive, visual browsing of ideas. Formalisation of inspiration network poses an open field for research.

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Section: The Implicit Inspiration Networkmentioning

confidence: 99%

Conceptualising Inspiration Networks in Game Design

Tomitsch

Bednarz

2015

ToDIGRA

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“…Brandes et al [7] have also analysed the collaboration structure in Wikipedia. Their work has focused on the edit interactions on individual articles.…”

Section: Related Researchmentioning

confidence: 99%

Integration of multiple network views in Wikipedia

Cunningham

2014

Knowl Inf Syst

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One of the challenges in network data analysis is the determination of the most informative perspective on the network to use in analysis. This is particularly an issue when the network is dynamic and is defined by events that occur over time. We present an example of such a scenario in the analysis of edit networks in Wikipedia -the networks of editors interacting on Wikipedia pages. We propose the prediction of article quality as a task that allows us to quantify the informativeness of alternative network views. We present three fundamentally different views on the data that attempt to capture structural and temporal aspects of the edit networks. We demonstrate that each view captures information that is unique to that view and propose a strategy for integrating the different sources of information.

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“…Among the large body of work on Wikipedia, our work mostly relates to trust and reputation management, where a trust score is assigned to an article [6,16,34], to selected parts of an article [2], or even to contributors [3,7,17]. These works often use information from the edit history (or the so-called revision history) of an article, including edit operations and the way the article evolves in response to an edit, for their scoring.…”

Section: Related Workmentioning

confidence: 99%

Leveraging editor collaboration patterns in wikipedia

Rad¹,

Makazhanov

Rafiei

et al. 2012

Proceedings of the 23rd ACM Conference on Hypertext and Social Media

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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.

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Network analysis of collaboration structure in Wikipedia

Cited by 145 publications

References 26 publications

Conceptualising Inspiration Networks in Game Design

Conceptualising Inspiration Networks in Game Design

Integration of multiple network views in Wikipedia

Leveraging editor collaboration patterns in wikipedia

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