  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.
