VASSL: A Visual Analytics Toolkit for Social Spambot Labeling.

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ID: 51376
2019
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Abstract
Social media platforms are filled with social spambots. Detecting these malicious accounts is essential, yet challenging, as they continually evolve to evade detection techniques. In this article, we present VASSL, a visual analytics system that assists in the process of detecting and labeling spambots. Our tool enhances the performance and scalability of manual labeling by providing multiple connected views and utilizing dimensionality reduction, sentiment analysis and topic modeling, enabling insights for the identification of spambots. The system allows users to select and analyze groups of accounts in an interactive manner, which enables the detection of spambots that may not be identified when examined individually. We present a user study to objectively evaluate the performance of VASSL users, as well as capturing subjective opinions about the usefulness and the ease of use of the tool.
Reference Key
khayat2019vasslieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Khayat, Mosab;Karimzadeh, Morteza;Zhao, Jieqiong;Ebert, David S;
Journal ieee transactions on visualization and computer graphics
Year 2019
DOI 10.1109/TVCG.2019.2934266
URL
Keywords
sentiment analysis natural language processing discriminative learning distributed representation neural probabilistic language model semi-supervised representation learning

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