Open Access

ARTICLE

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

Zia Ul Rehman^1,2, Sagheer Abbas¹, Muhammad Adnan Khan^3,*, Ghulam Mustafa², Hira Fayyaz⁴, Muhammad Hanif^1,2, Muhammad Anwar Saeed⁵

1 School of Computer Science, National College of Business Administration & Economics, Lahore, 54000, Pakistan
2 Department of Computer Sciences, Bahria University, Lahore, 54000, Pakistan
3 Department of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan
4 School of Systems and Technology, University of Management and Technology, Lahore, 54000, Pakistan
5 Department of CS & IT, Virtual University of Pakistan, Lahore, 54000, Pakistan

* Corresponding Author: Muhammad Adnan Khan. Email:

Computers, Materials & Continua 2021, 66(2), 1075-1090. https://doi.org/10.32604/cmc.2020.012770

Received 12 July 2020; Accepted 10 August 2020; Issue published 26 November 2020

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 dataset to be employed in radicalization detection; (ii) In depth analysis of new and previous datasets so that the variation in extremist group narrative could be identified; (iii) An approach to train classifier employing religious features along with radical features to detect radicalization; (iv) Observing the use of violent and bad words in radical, neutral and random groups by employing violent, terrorism and bad words dictionaries. Our research results clearly indicate that incorporating religious text in model training improves the accuracy, precision, recall, and F1-score of the classifiers. Secondly a variation in extremist narrative has been observed implying that usage of new dataset can have substantial effect on classifier performance. In addition to this, violence and bad words are creating a differentiating factor between radical and random users but for neutral (anti-ISIS) group it needs further investigation.

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