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    This study helps practitioners choose and configure text clustering algorithms and visualizations for disinformation analysis. We demonstrate optimal methods using 2016 US Election tweets and evaluate visualization comprehensibility.

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    Area of Science:

    • Computational Social Science
    • Data Mining
    • Natural Language Processing

    Background:

    • Text clustering algorithms are established but lack practical guidance for practitioners.
    • Choosing appropriate algorithms and visualizations for specific datasets remains challenging.

    Purpose of the Study:

    • To provide practical guidance on selecting and configuring text clustering algorithms.
    • To evaluate the comprehensibility of different visualization techniques for disinformation analysis.
    • To demonstrate a methodology using real-world disinformation datasets.

    Main Methods:

    • Case study analysis of two disinformation datasets (2016 US Presidential Election tweets).
    • Systematic evaluation of text clustering algorithm configurations.
    • User experiment to assess the comprehensibility of three distinct visualizations.

    Main Results:

    • Identified optimal configurations for text clustering algorithms on disinformation datasets.
    • Determined which visualization types enhance user understanding of clustered text data.
    • Provided a reproducible methodology with source code available.

    Conclusions:

    • Effective text clustering requires careful algorithm selection and configuration tailored to the data.
    • Visualization choice significantly impacts the interpretability of clustering results in disinformation studies.
    • The study offers practical insights and tools for researchers analyzing online disinformation.