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reputation is included as keyword or extra keyword in 0 datasets, 1 tools and 17 publications.


There is no datasets for this keyword.


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
From open-source software to Wikipedia: 'Backgrounding' trust by collective monitoring and reputation tracking De Laat P.B. Ethics and Information Technology English 2014 Open-content communities that focus on co-creation without requirements for entry have to face the issue of institutional trust in contributors. This research investigates the various ways in which these communities manage this issue. It is shown that communities of open-source software-continue to-rely mainly on hierarchy (reserving write-access for higher echelons), which substitutes (the need for) trust. Encyclopedic communities, though, largely avoid this solution. In the particular case of Wikipedia, which is confronted with persistent vandalism, another arrangement has been pioneered instead. Trust (i.e. full write-access) is 'backgrounded' by means of a permanent mobilization of Wikipedians to monitor incoming edits. Computational approaches have been developed for the purpose, yielding both sophisticated monitoring tools that are used by human patrollers, and bots that operate autonomously. Measures of reputation are also under investigation within Wikipedia; their incorporation in monitoring efforts, as an indicator of the trustworthiness of editors, is envisaged. These collective monitoring efforts are interpreted as focusing on avoiding possible damage being inflicted on Wikipedian spaces, thereby being allowed to keep the discretionary powers of editing intact for all users. Further, the essential differences between backgrounding and substituting trust are elaborated. Finally it is argued that the Wikipedian monitoring of new edits, especially by its heavy reliance on computational tools, raises a number of moral questions that need to be answered urgently. 0 0
No praise without effort: Experimental evidence on how rewards affect Wikipedia's contributor community Restivo M.
Van de Rijt A.
Information Communication and Society English 2014 The successful provision of public goods through mass volunteering over the Internet poses a puzzle to classic social science theories of human cooperation. A solution suggested by recent studies proposes that informal rewards (e.g. a thumbs-up, a badge, an editing award, etc.) can motivate participants by raising their status in the community, which acts as a select incentive to continue contributing. Indeed, a recent study of Wikipedia found that receiving a reward had a large positive effect on the subsequent contribution levels of highly-active contributors. While these findings are suggestive, they only pertained to already highly-active contributors. Can informal rewards also serve as a mechanism to increase participation among less-active contributors by initiating a virtuous cycle of work and reward? We conduct a field experiment on the online encyclopedia Wikipedia in which we bestowed rewards to randomly selected editors of varying productivity levels. Analysis of post-treatment activity shows that despite greater room for less-active contributors to increase their productive efforts, rewards yielded increases in work only among already highly-productive editors. On the other hand, rewards were associated with lower retention of less-active contributors. These findings suggest that the incentive structure in peer production is broadly meritocratic, as highly-active contributors accumulate the most rewards. However, this may also contribute to the divide between the stable core of highly-prodigious producers and a peripheral population of less-active contributors with shorter volunteer tenures. 0 0
Effects of implicit positive ratings for quality assessment of Wikipedia articles Yu Suzuki Journal of Information Processing English 2013 In this paper, we propose a method to identify high-quality Wikipedia articles by using implicit positive ratings. One of the major approaches for assessing Wikipedia articles is a text survival ratio based approach. In this approach, when a text survives beyond multiple edits, the text is assessed as high quality. However, the problem is that many low quality articles are misjudged as high quality, because every editor does not always read the whole article. If there is a low quality text at the bottom of a long article, and the text has not seen by the other editors, then the text survives beyond many edits, and the text is assessed as high quality. To solve this problem, we use a section and a paragraph as a unit instead of a whole page. In our method, if an editor edits an article, the system considers that the editor gives positive ratings to the section or the paragraph that the editor edits. From experimental evaluation, we confirmed that the proposed method could improve the accuracy of quality values for articles. 0 0
Towards Content-driven Reputation for Collaborative Code Repositories Andrew G. West
Insup Lee
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
Assessing quality values of Wikipedia articles using implicit positive and negative ratings Yu Suzuki Lecture Notes in Computer Science English 2012 In this paper, we propose a method to identify high-quality Wikipedia articles by mutually evaluating editors and text using implicit positive and negative ratings. One of major approaches for assessing Wikipedia articles is a text survival ratio based approach. However, the problem of this approach is that many low quality articles are misjudged as high quality, because of two issues. This is because, every editor does not always read the whole articles. Therefore, if there is a low quality text at the bottom of a long article, and the text have not seen by the other editors, then the text survives beyond many edits, and the survival ratio of the text is high. To solve this problem, we use a section or a paragraph as a unit of remaining instead of a whole page. This means that if an editor edits an article, the system treats that the editor gives positive ratings to the section or the paragraph that the editor edits. This is because, we believe that if editors edit articles, the editors may not read the whole page, but the editors should read the whole sections or paragraphs, and delete low-quality texts. From experimental evaluation, we confirmed that the proposed method could improve the accuracy of quality values for articles. 0 0
First results from an investigation into the validity of developer reputation derived from wiki articles and source code Prause C.R.
Eisenhauer M.
2012 5th International Workshop on Co-operative and Human Aspects of Software Engineering, CHASE 2012 - Proceedings English 2012 The internal quality of software is often neglected by developers for various reasons like time pressure or a general dislike for certain activities. Yet internal quality is important to speed up development and to keep software maintainable. We present a way to use reputation systems to improve the internal quality of software by putting artifacts like wiki articles and source code under their control. Specifically, we show that reputation scores derived from such artifacts reflect actual reputation in the developer community using data from a work group wiki and an open source project. 0 0
Autonomous Link Spam Detection in Purely Collaborative Environments Andrew G. West
Avantika Agrawal
Phillip Baker
Brittney Exline
Insup Lee
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.
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Wikipedia Vandalism Detection: Combining Natural Language, Metadata, and Reputation Features B. Thomas Adler
Luca de Alfaro
Santiago M. Mola Velasco
Paolo Rosso
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
Automatic reputation assessment in Wikipedia Wohner T.
Kohler S.
Ralf Peters
International Conference on Information Systems 2011, ICIS 2011 English 2011 The online encyclopedia Wikipedia is predominantly created by anonymous or pseudonymous authors whose knowledge and motivations are unknown. For that reason there is an uncertainty in terms of their contribution quality. An approach to this problem is provided by automatic reputation systems, which have been becoming a new research branch in the recent years. In previous research, different metrics for automatic reputation assessment have been suggested. Nevertheless, the metrics are evaluated insufficiently and considered isolated only. As a result, the significance of these metrics is quite unclear. In this paper, we compare and assess seven metrics, both originated from the literature and new suggestions. Additionally, we combine these metrics via a discriminant analysis to deduce a significant reputation function. The analysis reveals that our newly suggested metric editing efficiency is particularly effective. We validate our reputation function by means of an analysis of Wikipedia user groups. 0 0
Using domain ontologies for finding experts in corporate wikis Schafermeier R.
Paschke A.
ACM International Conference Proceeding Series English 2011 Finding experts is a relevant problem in large, distributed organizations, and automated solutions are needed. In this paper, we propose an approach for finding experts among Wiki authors, since Wikis have emerged as important collaboration and knowledge management tool in enterprizes. By analyzing revision histories and by semantically mapping Wiki contributions to concepts defined in corporate domain ontologies we identify experts. We apply semantic similarity metrics in order to detect references to ontology topics not explicitly mentioned in the text. Furthermore, we use information from the revision history in order to assess the level of expertise and examine the collaborative peer-reviewing processes happening in Wiki systems in order to calculate a reputation score for each author, based on the author's contribution lifetime. We evaluated our approach on the Eclipse project Wiki and conducted a survey with Eclipse project members to assess the quality of our expert finding approach. The results show that the approach yields accurate expertise information. 0 0
Wikipedia vandalism detection Santiago M. Mola Velasco World Wide Web English 2011 0 0
Wikis in scholarly publishing Daniel Mietchen
Gregor Hagedorn
Konrad U. Förstner
M. Fabiana Kubke
Claudia Koltzenburg
Mark Hahnel
Lyubomir Penev
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 –; 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
Cristina Lopes
Pierre Baldi
Statistical Analysis and Data Mining English 2010 Collaborative systems available on the Web allow millions of users to share information through a growing collection of tools and platforms such as wikis, blogs, and shared forums. By their very nature, these systems contain resources and information with different quality levels. The open nature of these systems, however, makes it difficult for users to determine the quality of the available information and the reputation of its providers. Here, we first parse and mine the entire English Wikipedia history pages in order to extract detailed user edit patterns and statistics. We then use these patterns and statistics to derive three computational models of a user's reputation. Finally, we validate these models using ground-truth Wikipedia data associated with vandals and administrators. When used as a classifier, the best model produces an area under the receiver operating characteristic {(ROC)} curve {(AUC)} of 0.98. Furthermore, we assess the reputation predictions generated by the models on other users, and show that all three models can be used efficiently for predicting user behavior in Wikipedia. 0 2
QuWi: Quality control in Wikipedia Alberto Cusinato
Vincenzo Della Mea
Francesco Di Salvatore
Stefano Mizzaro
WICOW'09 - Proceedings of the 3rd Workshop on Information Credibility on the Web, Co-located with WWW 2009 English 2009 We propose and evaluate QuWi (Quality in Wikipedia), a framework for quality control in Wikipedia. We build upon a previous proposal by Mizzaro [11], who proposed a method for substituting and/or complementing peer review in scholarly publishing. Since articles in Wikipedia are never finished, and their authors change continuously, we define a modified algorithm that takes into account the different domain, with particular attention to the fact that authors contribute identifiable pieces of information that can be further modified by other authors. The algorithm assigns quality scores to articles and contributors. The scores assigned to articles can be used, e.g., to let the reader understand how reliable are the articles he or she is looking at, or to help contributors in identifying low quality articles to be enhanced. The scores assigned to users measure the average quality of their contributions to Wikipedia and can be used, e.g., for conflict resolution policies based on the quality of involved users. Our proposed algorithm is experimentally evaluated by analyzing the obtained quality scores on articles for deletion and featured articles, also on six temporal Wikipedia snapshots. Preliminary results demonstrate that the proposed algorithm seems to appropriately identify high and low quality articles, and that high quality authors produce more long-lived contributions than low quality authors Copyright 200X ACM. 0 0
QuWi: quality control in Wikipedia Alberto Cusinato
Vincenzo Della Mea
Francesco Di Salvatore
Stefano Mizzaro
WICOW English 2009 0 0
Robust content-driven reputation Krishnendu Chatterjee
Luca de Alfaro
Ian Pye
Proceedings of the ACM Conference on Computer and Communications Security English 2008 In content-driven reputation systems for collaborative content, users gain or lose reputation according to how their contributions fare: authors of long-lived contributions gain reputation, while authors of reverted contributions lose reputation. Existing content-driven systems are prone to Sybil attacks, in which multiple identities, controlled by the same person, perform coordinated actions to increase their reputation. We show that content-driven reputation systems can be made resistant to such attacks by taking advantage of thefact that the reputation increments and decrements depend on content modifications, which are visible to all. We present an algorithm for content-driven reputation that prevents a set of identities from increasing their maximum reputation without doing any useful work. Here, work is considered useful if it causes content to evolve in a direction that is consistent with the actions of high-reputation users. We argue that the content modifications that require no effort, such as the insertion or deletion of arbitrary text, are invariably non-useful. We prove a truthfullness result for the resulting system, stating that users who wish to perform a contribution do not gain by employing complex contribution schemes, compared to simply performing the contribution at once. In particular, splitting the contribution in multiple portions, or employing the coordinated actions of multiple identities, do not yield additional reputation. Taken together, these results indicate that content-driven systems can be made robust with respect to Sybil attacks. Copyright 2008 ACM. 0 0
Wiki Trust Metrics based on Phrasal Analysis Mark Kramer
Andy Gregorowicz
Bala Iyer
WikiSym English 2008 0 0