| Vandalism detection|
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|Related keyword(s)||vandalism, spam, vandal fighting|
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Vandalism detection is included as keyword or extra keyword in 0 datasets, 0 tools and 11 publications.
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|Title||Author(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|A content-context-centric approach for detecting vandalism in Wikipedia||Lakshmish Ramaswamy
|Proceedings of the 9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, COLLABORATECOM 2013||English||2013||Collaborative online social media (CSM) applications such as Wikipedia have not only revolutionized the World Wide Web, but they also have had a hugely positive effect on modern free societies. Unfortunately, Wikipedia has also become target to a wide-variety of vandalism attacks. Most existing vandalism detection techniques rely upon simple textual features such as existence of abusive language or spammy words. These techniques are ineffective against sophisticated vandal edits, which often do not contain the tell-tale markers associated with vandalism. In this paper, we argue for a context-aware approach for vandalism detection. This paper proposes a content-context-aware vandalism detection framework. The main idea is to quantify how well the words contained in the edit fit into the topic and the existing content of the Wikipedia article. We present two novel metrics, called WWW co-occurrence probability and top-ranked co-occurrence probability for this purpose. We also develop efficient mechanisms for evaluating these two metrics, and machine learning-based schemes that utilize these metrics. The paper presents a range of experiments to demonstrate the effectiveness of the proposed approach.||0||0|
|WikiDetect: Automatic vandalism detection for Wikipedia using linguistic features||Cioiu D.
|Lecture Notes in Computer Science||English||2013||Vandalism of the content has always been one of the greatest problems for Wikipedia, yet only few completely automatic solutions for solving it have been developed so far. Volunteers still spend large amounts of time correcting vandalized page edits, instead of using this time to improve the quality of the content of articles. The purpose of this paper is to introduce a new vandalism detection system, that only uses natural language processing and machine learning techniques. The system has been evaluated on a corpus of real vandalized data in order to test its performance and justify the design choices. The same expert annotated wikitext, extracted from the encyclopedia's database, is used to evaluate different vandalism detection algorithms. The paper presents a critical analysis of the obtained results, comparing them to existing solutions, and suggests different statistical classification methods that bring several improvements to the task at hand.||0||0|
|Detecting Wikipedia vandalism with a contributing efficiency-based approach||Tang X.
|Lecture Notes in Computer Science||English||2012||The collaborative nature of wiki has distinguished Wikipedia as an online encyclopedia but also makes the open contents vulnerable against vandalism. The current vandalism detection methods relying on basic statistic language features work well for explicitly offensive edits that perform massive changes. However, these techniques are evadable for the elusive vandal edits which make only a few unproductive or dishonest modifications. In this paper we proposed a contributing efficiency-based approach to detect the vandalism in Wikipedia and implement it with machine-learning based classifiers that incorporate the contributing efficiency along with other languages features. The results of extensional experiment show that the contributing efficiency can improve the recall of machine learning-based vandalism detection algorithms significantly.||0||0|
|Multilingual Vandalism Detection using Language-Independent & Ex Post Facto Evidence||Andrew G. West
|PAN-CLEF||English||September 2011||There is much literature on Wikipedia vandalism detection. However, this writing addresses two facets given little treatment to date. First, prior efforts emphasize zero-delay detection, classifying edits the moment they are made. If classification can be delayed (e.g., compiling offline distributions), it is possible to leverage ex post facto evidence. This work describes/evaluates several features of this type, which we find to be overwhelmingly strong vandalism indicators.
Second, English Wikipedia has been the primary test-bed for research. Yet, Wikipedia has 200+ language editions and use of localized features impairs portability. This work implements an extensive set of language-independent indicators and evaluates them using three corpora (German, English, Spanish). The work then extends to include language-specific signals. Quantifying their performance benefit, we find that such features can moderately increase classifier accuracy, but significant effort and language fluency are required to capture this utility.Aside from these novel aspects, this effort also broadly addresses the task, implementing 65 total features. Evaluation produces 0.840 PR-AUC on thezero-delay task and 0.906 PR-AUC with ex post facto evidence (averaging languages). Performance matches the state-of-the-art (English), sets novel baselines (German, Spanish), and is validated by a first-place finish over the 2011 PAN-CLEF test set.
|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|
|Vandalism detection in Wikipedia: A high-performing, feature-rich model and its reduction through Lasso||Sara Javanmardi
David W. McDonald
|WikiSym 2011 Conference Proceedings - 7th Annual International Symposium on Wikis and Open Collaboration||English||2011||User generated content (UGC) constitutes a significant fraction of the Web. However, some wiiki-based sites, such as Wikipedia, are so popular that they have become a favorite target of spammers and other vandals. In such popular sites, human vigilance is not enough to combat vandalism, and tools that detect possible vandalism and poor-quality contributions become a necessity. The application of machine learning techniques holds promise for developing efficient online algorithms for better tools to assist users in vandalism detection. We describe an efficient and accurate classifier that performs vandalism detection in UGC sites. We show the results of our classifier in the PAN Wikipedia dataset. We explore the effectiveness of a combination of 66 individual features that produce an AUC of 0.9553 on a test dataset - the best result to our knowledge. Using Lasso optimization we then reduce our feature - rich model to a much smaller and more efficient model of 28 features that performs almost as well - the drop in AUC being only 0.005. We describe how this approach can be generalized to other user generated content systems and describe several applications of this classifier to help users identify potential vandalism.||0||0|
|Vandalism detection in Wikipedia: a high-performing, feature-rich model and its reduction through Lasso||Sara Javanmardi
David W. McDonald
Cristina V. Lopes
|Crowdsourcing a Wikipedia Vandalism Corpus||Martin Potthast||SIGIR||English||2010||We report on the construction of the PAN Wikipedia vandalism corpus, PAN-WVC-10, using Amazon’s Mechanical Turk. The corpus compiles 32 452 edits on 28 468 Wikipedia articles, among which 2 391 vandalism edits have been identified. 753 human annotators cast a total of 193 022 votes on the edits, so that each edit was reviewed by at least 3 annotators, whereas the achieved level of agreement was analyzed in order to label an edit as “regular” or “vandalism.” The corpus is available free of charge.||6||1|
|Elusive vandalism detection in Wikipedia: A text stability-based approach||Wu Q.
|International Conference on Information and Knowledge Management, Proceedings||English||2010||The open collaborative nature of wikis encourages participation of all users, but at the same time exposes their content to vandalism. The current vandalism-detection techniques, while effective against relatively obvious vandalism edits, prove to be inadequate in detecting increasingly prevalent sophisticated (or elusive) vandal edits. We identify a number of vandal edits that can take hours, even days, to correct and propose a text stability-based approach for detecting them. Our approach is focused on the likelihood of a certain part of an article being modified by a regular edit. In addition to text-stability, our machine learning-based technique also takes into account edit patterns. We evaluate the performance of our approach on a corpus comprising of 15000 manually labeled edits from the Wikipedia Vandalism PAN corpus. The experimental results show that text-stability is able to improve the performance of the selected machine-learning algorithms significantly.||0||0|
|Elusive vandalism detection in wikipedia: a text stability-based approach||Qinyi Wu
|Detector y corrector automático de ediciones maliciosas en Wikipedia||Emilio J. Rodríguez-Posada||Spanish||2009||El proyecto desarrolla AVBOT (acrónimo de Anti-Vandalism BOT), un programa que detecta y corrige automáticamente ediciones maliciosas en Wikipedia en español. Está programado en Python y utiliza las librerías pywikipediabot y python-irclib.||0||0|