VASSL: A Visual Analytics Toolkit for Social Spambot Labeling
This work addresses the challenge of detecting malicious spambots for social media platforms and users, but it is incremental as it builds on existing visual analytics and detection methods.
The authors tackled the problem of detecting evolving social spambots on platforms like Twitter by proposing VASSL, a visual analytics toolkit that assists in labeling spambots, resulting in enhanced performance and scalability in manual labeling through interactive features and insights from techniques like dimensionality reduction.
Social media platforms such as Twitter are filled with social spambots. Detecting these malicious accounts is essential, yet challenging, as they continually evolve and evade traditional detection techniques. In this work, we propose 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 techniques, which offer new insights that enable 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 conducted 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.