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Multi-Ideology ISIS/Jihadist White Supremacist (MIWS) Dataset for Multi-Class Extremism Text Classification

Author

Listed:
  • Mayur Gaikwad

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

  • Swati Ahirrao

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

  • Shraddha Phansalkar

    (MIT Art, Design and Technology University, Pune MH 412201, India)

  • Ketan Kotecha

    (Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune MH 412115, India)

Abstract

Social media platforms are a popular choice for extremist organizations to disseminate their perceptions, beliefs, and ideologies. This information is generally based on selective reporting and is subjective in content. However, the radical presentation of this disinformation and its outreach on social media leads to an increased number of susceptible audiences. Hence, detection of extremist text on social media platforms is a significant area of research. The unavailability of extremism text datasets is a challenge in online extremism research. The lack of emphasis on classifying extremism text into propaganda, radicalization, and recruitment classes is a challenge. The lack of data validation methods also challenges the accuracy of extremism detection. This research addresses these challenges and presents a seed dataset with a multi-ideology and multi-class extremism text dataset. This research presents the construction of a multi-ideology ISIS/Jihadist White supremacist (MIWS) dataset with recent tweets collected from Twitter. The presented dataset can be employed effectively and importantly to classify extremist text into popular types like propaganda, radicalization, and recruitment. Additionally, the seed dataset is statistically validated with a coherence score of Latent Dirichlet Allocation (LDA) and word mover’s distance using a pretrained Google News vector. The dataset shows effectiveness in its construction with good coherence scores within a topic and appropriate distance measures between topics. This dataset is the first publicly accessible multi-ideology, multi-class extremism text dataset to reinforce research on extremism text detection on social media platforms.

Suggested Citation

  • Mayur Gaikwad & Swati Ahirrao & Shraddha Phansalkar & Ketan Kotecha, 2021. "Multi-Ideology ISIS/Jihadist White Supremacist (MIWS) Dataset for Multi-Class Extremism Text Classification," Data, MDPI, vol. 6(11), pages 1-15, November.
    • Handle: RePEc:gam:jdataj:v:6:y:2021:i:11:p:117-:d:679373
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    References listed on IDEAS

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    1. Elkhan Richard Sadik-Zada, 2021. "An Ode to ODA against all Odds? A Novel Game-Theoretical and Empirical Reappraisal of the Terrorism-Aid Nexus," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 49(2), pages 221-240, June.
    2. Hailin Liu & Ling Xu & Mengning Yang & Meng Yan & Xiaohong Zhang, 2015. "Predicting Component Failures Using Latent Dirichlet Allocation," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-15, July.
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