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VASSL: A Visual Analytics Toolkit for Social Spambot Labeling.

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    This study introduces VASSL, a visual analytics system designed to improve the detection and labeling of social spambots. VASSL enhances manual efforts by integrating multiple views and analytical techniques for identifying malicious accounts.

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    Area of Science:

    • Computer Science
    • Data Science
    • Social Network Analysis

    Background:

    • Social media platforms are increasingly targeted by sophisticated social spambots.
    • Current detection methods struggle against evolving spambot tactics, necessitating advanced tools.
    • Manual labeling of spambots is labor-intensive and often inefficient.

    Purpose of the Study:

    • To present VASSL, a novel visual analytics system for detecting and labeling social spambots.
    • To enhance the performance and scalability of manual spambot identification processes.
    • To provide interactive tools for analyzing groups of accounts to uncover sophisticated spambots.

    Main Methods:

    • Developed VASSL, a visual analytics system integrating multiple connected views.
    • Employed dimensionality reduction, sentiment analysis, and topic modeling within the system.
    • Facilitated interactive selection and analysis of account groups for spambot identification.
    • Conducted a user study to evaluate VASSL's performance, usefulness, and usability.

    Main Results:

    • VASSL enhances the efficiency and scalability of manual spambot labeling.
    • The system's integrated analytical techniques provide deeper insights into spambot behavior.
    • Interactive group analysis enables the detection of spambots missed by individual examination.
    • User study demonstrated VASSL's effectiveness and user-friendliness.

    Conclusions:

    • VASSL offers a powerful and interactive solution for combating social spambots.
    • The system's visual analytics approach aids in identifying complex and evolving spambot activities.
    • VASSL represents a significant advancement in automated and semi-automated spambot detection.