S. Subramanian, P. Pushparaj, Zerong Liu, Aidong Lu
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Explainable Visualization of Collaborative Vandal Behaviors in Wikipedia
Online social networks are prone to be targeted by various frauds and attacks, which are difficult to detect due to their complexity and variations. The challenge is to make sense of all information with suitable exploration tools for different groups of users. This project focuses on an explainable visualization approach to study collaborative behaviors of vandal users on Wikipedia. Our approach creates visualization with commonly used techniques from cartography and statistical graphics that are familiar to the general public for effectiveness and explainability. We have built a large-scale visualization system which supports an illustrative interface with multiple data query, filtering, analysis, and interactive exploration functions. Examples and case studies are provided to demonstrate that our approach can be used effectively for a set of Wikipedia behavior analysis tasks.