# VASSL: A Visual Analytics Toolkit for Social Spambot Labeling

## TL;DR

VASSL is a visual analytics toolkit designed to improve the detection and labeling of social spambots on platforms like Twitter through interactive, multi-view analysis and advanced data techniques.

## Contribution

The paper introduces VASSL, a novel visual analytics system that enhances manual spambot detection with interactive visualization and data analysis methods.

## Key findings

- Improved spambot detection performance with VASSL.
- Users found VASSL useful and easy to use.
- VASSL enables detection of complex spambots.

## Abstract

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.