Pro-ISIS Tweets Analysis Using Machine Learning Techniques

Julia Thee, Izzat Alsmadi, Samer Al-Khateeb

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The spread of violent extremism and propaganda is a critical threat both nationally and globally. With the ever-increasing popularity and use of social media, spreading this extremism has never been easier for terrorist organizations and their followers. One terrorist organization, in particular, ISIS (the Islamic State of Iraq and Syria), uses Twitter for the vast majority of their social media interaction. It is crucial to have cyber analytics tools developed to combat these extremists' online presence and influence on social media platforms, such as Twitter. In this research, we apply machine learning algorithms to understand popular ISIS supporters' behavior and techniques and their possible influence on other users. We collected and analyzed a dataset containing over seventeen thousand tweets posted by pro-ISIS Twitterers. We utilized three machine learning algorithms with several models/settings in an attempt to classify and predict whether the top 4 pro-ISIS Twitter users (the most followed and tweeted users) authored a specific tweet. The algorithms applied in this work include sequential neural networks, random forests, and XGBoost. The models were ensembled, timed, and one model was simplified to attempt to improve performance and runtime.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
EditorsXintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4351-4358
Number of pages8
ISBN (Electronic)9781728162515
DOIs
StatePublished - Dec 10 2020
Event8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States
Duration: Dec 10 2020Dec 13 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference8th IEEE International Conference on Big Data, Big Data 2020
Country/TerritoryUnited States
CityVirtual, Atlanta
Period12/10/2012/13/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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