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|Related keyword(s)||rollback, vandalism|
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revert is included as keyword or extra keyword in 0 datasets, 0 tools and 4 publications.
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|Title||Author(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Reverts Revisited: Accurate Revert Detection in Wikipedia||Fabian Flöck
|Hypertext and Social Media 2012||English||June 2012||Wikipedia is commonly used as a proving ground for research in collaborative systems. This is likely due to its popularity and scale, but also to the fact that large amounts of data about its formation and evolution are freely available to inform and validate theories and models of online collaboration. As part of the development of such approaches, revert detection is often performed as an important pre-processing step in tasks as diverse as the extraction of implicit networks of editors, the analysis of edit or editor features and the removal of noise when analyzing the emergence of the con-tent of an article. The current state of the art in revert detection is based on a rather naïve approach, which identifies revision duplicates based on MD5 hash values. This is an efficient, but not very precise technique that forms the basis for the majority of research based on revert relations in Wikipedia. In this paper we prove that this method has a number of important drawbacks - it only detects a limited number of reverts, while simultaneously misclassifying too many edits as reverts, and not distinguishing between complete and partial reverts. This is very likely to hamper the accurate interpretation of the findings of revert-related research. We introduce an improved algorithm for the detection of reverts based on word tokens added or deleted to adresses these drawbacks. We report on the results of a user study and other tests demonstrating the considerable gains in accuracy and coverage by our method, and argue for a positive trade-off, in certain research scenarios, between these improvements and our algorithm’s increased runtime.||13||0|
|Don't bite the newbies: how reverts affect the quantity and quality of Wikipedia work||Aaron Halfaker
|WikiSym||English||2011||Reverts are important to maintaining the quality of Wikipedia. They fix mistakes, repair vandalism, and help enforce policy. However, reverts can also be damaging, especially to the aspiring editor whose work they destroy. In this research we analyze 400,000 Wikipedia revisions to understand the effect that reverts had on editors. We seek to understand the extent to which they demotivate users, reducing the workforce of contributors, versus the extent to which they help users improve as encyclopedia editors. Overall we find that reverts are powerfully demotivating, but that their net influence is that more quality work is done in Wikipedia as a result of reverts than is lost by chasing editors away. However, we identify key conditions – most specifically new editors being reverted by much more experienced editors – under which reverts are particularly damaging. We propose that reducing the damage from reverts might be one effective path for Wikipedia to solve the newcomer retention problem.||0||2|
|NICE: Social translucence through UI intervention||Aaron Halfaker
|WikiSym 2011 Conference Proceedings - 7th Annual International Symposium on Wikis and Open Collaboration||English||2011||Social production systems such as Wikipedia rely on attracting and motivating volunteer contributions to be successful. One strong demotivating factor can be when an editor's work is discarded, or "reverted", by others. In this paper we demonstrate evidence of this effect and design a novel interface aimed at improving communication between the reverting and reverted editors. We deployed the interface in a controlled experiment on the live Wikipedia site, and report on changes in the behavior of 487 contributors who were reverted by editors using our interface. Our results suggest that simple interface modifications (such as informing Wikipedians that the editor they are reverting is a newcomer) can have substantial positive effects in protecting against contribution loss in newcomers and improving the quality of work done by more experienced contributors.||0||0|
|Us vs. Them: Understanding social dynamics in wikipedia with revert graph visualizations||Bongwon Suh
|VAST IEEE Symposium on Visual Analytics Science and Technology 2007, Proceedings||English||2007||Wikipedia is a wiki-based encyclopedia that has become one of the most popular collaborative on-line knowledge systems. As in any large collaborative system, as Wikipedia has grown, conflicts and coordination costs have increased dramatically. Visual analytic tools provide a mechanism for addressing these issues by enabling users to more quickly and effectively make sense of the status of a collaborative environment. In this paper we describe a model for identifying patterns of conflicts in Wikipedia articles. The model relies on users' editing history and the relationships between user edits, especially revisions that void previous edits, known as "reverts". Based on this model, we constructed Revert Graph, a tool that visualizes the overall conflict patterns between groups of users. It enables visual analysis of opinion groups and rapid interactive exploration of those relationships via detail drill-downs. We present user patterns and case studies that show the effectiveness of these techniques, and discuss how they could generalize to other systems.||0||4|