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reputation is included as keyword or extra keyword in 0 datasets, 1 tools and 8 publications.
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|Tool||Operating System(s)||Language(s)||Programming language(s)||License||Description||Image|
|WikiTrust||English||New BSD License
|WikiTrust is an open-source, on-line reputation system for Wikipedia authors and content.|
|Title||Author(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Towards Content-driven Reputation for Collaborative Code Repositories||Andrew G. West
|WikiSym||English||August 2012||As evidenced by SourceForge and GitHub, code repositories now integrate Web 2.0 functionality that enables global participation with minimal barriers-to-entry. To prevent detrimental contributions enabled by crowdsourcing, reputation is one proposed solution. Fortunately this is an issue that has been addressed in analogous version control systems such as the *wiki* for natural language content. The WikiTrust algorithm ("content-driven reputation"), while developed and evaluated in wiki environments operates under a possibly shared collaborative assumption: actions that "survive" subsequent edits are reflective of good authorship. In this paper we examine WikiTrust's ability to measure author quality in collaborative code development. We first define a mapping from repositories to wiki environments and use it to evaluate a production SVN repository with 92,000 updates. Analysis is particularly attentive to reputation loss events and attempts to establish ground truth using commit comments and bug tracking. A proof-of-concept evaluation suggests the technique is promising (about two-thirds of reputation loss is justified) with false positives identifying areas for future refinement. Equally as important, these false positives exemplify differences in content evolution and the cooperative process between wikis and code repositories.||0||0|
|Autonomous Link Spam Detection in Purely Collaborative Environments||Andrew G. West
|WikiSym||English||October 2011||Collaborative models (e.g., wikis) are an increasingly prevalent Web technology. However, the open-access that defines such systems can also be utilized for nefarious purposes. In particular, this paper examines the use of collaborative functionality to add inappropriate hyperlinks to destinations outside the host environment (i.e., link spam). The collaborative encyclopedia, Wikipedia, is the basis for our analysis.
Recent research has exposed vulnerabilities in Wikipedia's link spam mitigation, finding that human editors are latent and dwindling in quantity. To this end, we propose and develop an autonomous classifier for link additions. Such a system presents unique challenges. For example, low barriers-to-entry invite a diversity of spam types, not just those with economic motivations. Moreover, issues can arise with how a link is presented (regardless of the destination).In this work, a spam corpus is extracted from over 235,000 link additions to English Wikipedia. From this, 40+ features are codified and analyzed. These indicators are computed using "wiki" metadata, landing site analysis, and external data sources. The resulting classifier attains 64% recall at 0.5% false-positives (ROC-AUC=0.97). Such performance could enable egregious link additions to be blocked automatically with low false-positive rates, while prioritizing the remainder for human inspection. Finally, a live Wikipedia implementation of the technique has been developed.
|Wikipedia Vandalism Detection: Combining Natural Language, Metadata, and Reputation Features||B. Thomas Adler
Luca de Alfaro
Santiago M. Mola Velasco
Andrew G. West
|Lecture notes in computer science||English||February 2011||Wikipedia is an online encyclopedia which anyone can edit. While most edits are constructive, about 7% are acts of vandalism. Such behavior is characterized by modifications made in bad faith; introducing spam and other inappropriate content. In this work, we present the results of an effort to integrate three of the leading approaches to Wikipedia vandalism detection: a spatio-temporal analysis of metadata (STiki), a reputation-based system (WikiTrust), and natural language processing features. The performance of the resulting joint system improves the state-of-the-art from all previous methods and establishes a new baseline for Wikipedia vandalism detection. We examine in detail the contribution of the three approaches, both for the task of discovering fresh vandalism, and for the task of locating vandalism in the complete set of Wikipedia revisions.||0||1|
|Wikipedia vandalism detection||Santiago M. Mola Velasco||World Wide Web||English||2011||0||0|
|Wikis in scholarly publishing||Daniel Mietchen
Konrad U. Förstner
M. Fabiana Kubke
|Information Services and Use||English||2011||Scientific research is a process concerned with the creation, collective accumulation, contextualization, updating and maintenance of knowledge. Wikis provide an environment that allows to collectively accumulate, contextualize, update and maintain knowledge in a coherent and transparent fashion. Here, we examine the potential of wikis as platforms for scholarly publishing. In the hope to stimulate further discussion, the article itself was drafted on Species ID – http://species-id.net; a wiki that hosts a prototype for wiki-based scholarly publishing – where it can be updated, expanded or otherwise improved.||0||1|
|Modeling user reputation in wikis||Sara Javanmardi
|Stat. Anal. Data Min.||English||2010||0||2|
|QuWi: quality control in Wikipedia||Alberto Cusinato
Vincenzo Della Mea
Francesco Di Salvatore
|Wiki Trust Metrics based on Phrasal Analysis||Mark Kramer