| Megha Bassi|
(Alternative names for this author)
|Co-authors||Manoj Harpalani, Michael Hart, Rob Johnson, Thanadit Phumprao|
|Authorship||Publications (1), datasets (0), tools (0)|
|Citations||Total (0), average (0), median (0), max (0), min (0)|
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Megha Bassi is an author.
PublicationsOnly those publications related to wikis are shown here.
|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Wiki Vandalysis - Wikipedia Vandalism Analysis||CLEF||English||2010||Wikipedia describes itself as the "free encyclopedia that anyone can edit". Along with the helpful volunteers who contribute by improving the articles, a great number of malicious users abuse the open nature of Wikipedia by vandalizing articles. Deterring and reverting vandalism has become one of the
major challenges of Wikipedia as its size grows. Wikipedia editors fight vandalism both manually and with automated bots that use regular expressions and other simple rules to recognize malicious edits. Researchers have also proposed Machine Learning algorithms for vandalism detection, but these algorithms are still in their infancy and have much room for improvement. This paper presents an approach to fighting vandalism by extracting various features from the edits for machine learning classification. Our classifier uses information about the editor, the sentiment of the edit, the "quality" of the edit (i.e. spelling errors), and targeted regular expressions to capture patterns common in blatantvandalism, such as insertion of obscene words or multiple exclamations. We have successfully been able to achieve an area under the ROC curve (AUC) of 0.91 on a training set of 15000 human annotated edits and 0.887 on a random sample of 17472 edits from 317443.