VASSL: A Visual Analytics Toolkit for Social Spambot Labeling

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.

Interactive Graph Construction for Graph-Based Semi-Supervised Learning

“…Users can label multiple similar images at the same time. VASSL [20] was proposed to detect and label social spambot groups on social media. Five coordinated views are utilized to display similarities between accounts from different perspectives to facilitate identifying clusters with anomalous behavior.…”

The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

“…As stated by these previous works, support for the visual parameter search is still an open research challenge. Topic 2 – investigation of the behavior. This topic class contains 2 out of 9 papers on topic analysis applications [CAA*19, KKZE20]. A common theme here is network visualization used for explaining Bayesian networks [CWS*17, VKA*18] and decision trees [vv11].…”

“…VASSL [KKZE20] is a system that works with the preprocessing/input phase and enhances the performance and scalability of the manual labeling process by providing multiple coordinated views and utilizing DR, sentiment analysis, and topic modeling. The system allows users to select and further investigate batches of accounts, which supports the discovery of spambot cases that may not be detected when checked independently.…”

“…Multiple views, linked matrix‐graph views, and data views in the form of lists are used to choose parameters and see the effects of them. Other papers apply different visual representations, some rare cases are waterfall charts, violin charts, Voronoi diagrams , and bipartite graphs [Aup07, HSD19, KKZE20, LA11, LGG*18, WGSY19, WGYS18, ZTR16].…”

A survey of visual analytics techniques for machine learning

“…To this end, Moehrmann et al [32] used an SOM-based visualization to place similar images together, allowing users to label multiple similar images of the same class in one go. This strategy is also used by Khayat et al [28] to identify social spambot groups with similar anomalous behavior, Kurzhals et al [29] to label mobile eye-tracking data, and Halter et al [24] to annotate and analyze primary color strategies used in films. Apart from placing similar items together, other strategies, like filtering, have also been applied to find items of interest for labeling.…”