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  • Open Access


    Prediction of Extremist Behaviour and Suicide Bombing from Terrorism Contents Using Supervised Learning

    Nasir Mahmood*, Muhammad Usman Ghani Khan

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4411-4428, 2022, DOI:10.32604/cmc.2022.013956

    Abstract This study proposes an architecture for the prediction of extremist human behaviour from projected suicide bombings. By linking ‘dots’ of police data comprising scattered information of people, groups, logistics, locations, communication, and spatiotemporal characters on different social media groups, the proposed architecture will spawn beneficial information. This useful information will, in turn, help the police both in predicting potential terrorist events and in investigating previous events. Furthermore, this architecture will aid in the identification of criminals and their associates and handlers. Terrorism is psychological warfare, which, in the broadest sense, can be defined as the utilisation of deliberate violence for… More >

  • Open Access


    Applying Machine Learning Techniques for Religious Extremism Detection on Online User Contents

    Shynar Mussiraliyeva1, Batyrkhan Omarov1,*, Paul Yoo1,2, Milana Bolatbek1

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 915-934, 2022, DOI:10.32604/cmc.2022.019189

    Abstract In this research paper, we propose a corpus for the task of detecting religious extremism in social networks and open sources and compare various machine learning algorithms for the binary classification problem using a previously created corpus, thereby checking whether it is possible to detect extremist messages in the Kazakh language. To do this, the authors trained models using six classic machine-learning algorithms such as Support Vector Machine, Decision Tree, Random Forest, K Nearest Neighbors, Naive Bayes, and Logistic Regression. To increase the accuracy of detecting extremist texts, we used various characteristics such as Statistical Features, TF-IDF, POS, LIWC, and… More >

  • Open Access


    Understanding the Language of ISIS: An Empirical Approach to Detect Radical Content on Twitter Using Machine Learning

    Zia Ul Rehman1,2, Sagheer Abbas1, Muhammad Adnan Khan3,*, Ghulam Mustafa2, Hira Fayyaz4, Muhammad Hanif1,2, Muhammad Anwar Saeed5

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1075-1090, 2021, DOI:10.32604/cmc.2020.012770

    Abstract The internet, particularly online social networking platforms have revolutionized the way extremist groups are influencing and radicalizing individuals. Recent research reveals that the process initiates by exposing vast audiences to extremist content and then migrating potential victims to confined platforms for intensive radicalization. Consequently, social networks have evolved as a persuasive tool for extremism aiding as recruitment platform and psychological warfare. Thus, recognizing potential radical text or material is vital to restrict the circulation of the extremist chronicle. The aim of this research work is to identify radical text in social media. Our contributions are as follows: (i) A new… More >

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