Vandalism Detection in Wikipedia using Trustworthy Ranking and Semantic Context Analysis

1. Vandalism Detection in Wikipedia using Trustworthy Ranking and Semantic Context Analysis DeepikaSethi Raga Sowmya Tummalapenta

2. Wikipedia Vandalism • Wikipedia • Benefits • Problems • Vandalism • Example

3. Approach • Approach 1 • Trustworthy Ranking based on a Trustworthy search engine • Approach 2 • Semantic Context Analysis using an Ontology.

4. Approach 1 • Collection of top ranked documents using a trustworthy search engine • Co-occurrence probability of an edit and its corresponding page. • Probability too low might imply out of context and vandalism. • Feature extraction followed by data-trained classification.

5. Approach 2 • DBpediaextracts data from Wikipedia. • Finding Word-relationships using ontology. • Semantic Distance • Feature extraction followed by data-trained classification.

6. Evaluation • Feature 1 : Co-occurrence probability • Feature 2 : Semantic Distance • Feature 3 : Combination of both • 10-fold cross-validation

7. Related Work • Wikipedia Vandalism Detection : Combining Natural Language, Metadata, and Reputation Features. • Elusive Vandalism Detection in Wikipedia : A Text Stability Based Approach • Wikipedia Vandalism Detection through Machine Learning

8. Thank You