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  1. Home
  3. Multi-ideology, Multiclass Online Extremism Dataset, And Its Evaluation Using Machine Learning.
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  3. Multi-ideology, Multiclass Online Extremism Dataset, And Its Evaluation Using Machine Learning.

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Multi-Ideology, Multiclass Online Extremism Dataset, and Its Evaluation Using Machine Learning.

Mayur Gaikwad1, Swati Ahirrao1, Shraddha Phansalkar2

  • 1Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, MH 412115, India.

Computational Intelligence and Neuroscience
|March 13, 2023

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a new, balanced dataset for detecting extremism online, covering multiple ideologies and classifying text into propaganda, radicalization, and recruitment. Machine learning models, particularly support vector machines, show promising results for this multi-class extremism detection task.

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Area of Science:

  • Computer Science
  • Social Sciences
  • Computational Linguistics

Background:

  • Social media platforms are exploited for extremist propaganda, radicalization, and recruitment, necessitating effective detection methods.
  • Existing extremism detection research is limited by single-ideology datasets, binary classification, and manual data validation.
  • Challenges include class imbalance and a lack of automated data validation in current extremism detection studies.

Purpose of the Study:

  • To develop a balanced, multi-ideology extremism text dataset for improved detection.
  • To classify extremism into propaganda, radicalization, and recruitment categories using robust validation.
  • To address limitations of existing single-ideology and binary classification datasets.

Main Methods:

  • Created a versatile extremism text dataset generalizing multiple ideologies (ISIS, White Supremacist).
  • Utilized TF-IDF (unigram, bigrams, trigrams) and pretrained word2vec features for analysis.
  • Evaluated machine learning classifiers including Naïve Bayes, Support Vector Machine, Random Forest, and XGBoost.

    • Main Results:

    • The Support Vector Machine with TF-IDF unigram features achieved the best performance with a 0.67 F1 score.
    • The proposed multi-ideology, multi-class dataset demonstrated comparable performance to existing single-ideology datasets.
    • Feature extraction included TF-IDF and word2vec for semantic analysis.

    Conclusions:

    • The developed multi-ideology dataset enhances extremism text classification accuracy.
    • Machine learning models can effectively identify propaganda, radicalization, and recruitment on social media.
    • This research contributes a valuable resource for combating online extremism through improved detection.