Visualizing large-scale human collaboration in Wikipedia
| Visualizing large-scale human collaboration in Wikipedia | |
| Author(s) | Biuk-Aghai R.P., Pang C.-I., Si Y.-W. |
| Editor(s) | Unknown [+] |
| Published in | Future Generation Computer Systems |
| Date | 2014 |
| Volume | 31 |
| Issue | 1 |
| Page(s) | 120–133 |
| Keyword(s) | Category, Co-authoring, Information visualization, Visualization of collaborative processes & applications, Wikipedia (Extra: Category, Co-authoring, Collaborative process, Information visualization, Wikipedia, Information systems, Probability distributions, Visualization, Data visualization) |
| Peer-reviewed? | Unknown [+] |
| Language(s) | English |
| License(s) | Unknown [+] |
| Identifiers | |
| ISSN | 0167739X |
| DOI | 10.1016/j.future.2013.04.001 |
| OCLC Number | Unknown [+] |
| CiteULike | Unknown [+] |
| arXiv | Unknown [+] |
| PubMed | Unknown [+] |
| Related material | |
| Concept(s) | Unknown [+] |
| Tool(s) | Unknown [+] |
| Dataset(s) | Unknown [+] |
| Slides | Not available [+] |
| Presentation | Not available [+] |
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Visualizing large-scale human collaboration in Wikipedia is a 2014 journal article written in English by Biuk-Aghai R.P., Pang C.-I., Si Y.-W. and published in Future Generation Computer Systems.
[edit] Abstract
Volunteer-driven large-scale human-to-human collaboration has become common in the Web 2.0 era. Wikipedia is one of the foremost examples of such large-scale collaboration, involving millions of authors writing millions of articles on a wide range of subjects. The collaboration on some popular articles numbers hundreds or even thousands of co-authors. We have analyzed the co-authoring across entire Wikipedias in different languages and have found it to follow a geometric distribution in all the language editions we studied. In order to better understand the distribution of co-author counts across different topics, we have aggregated content by category and visualized it in a form resembling a geographic map. The visualizations produced show that there are significant differences of co-author counts across different topics in all the Wikipedia language editions we visualized. In this article we describe our analysis and visualization method and present the results of applying our method to the English, German, Chinese, Swedish and Danish Wikipedias. We have evaluated our visualization against textual data and found it to be superior in usability, accuracy, speed and user preference. © 2013 Elsevier B.V. All rights reserved.
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