TY - GEN
T1 - Pro-ISIS Tweets Analysis Using Machine Learning Techniques
AU - Thee, Julia
AU - Alsmadi, Izzat
AU - Al-Khateeb, Samer
N1 - Funding Information:
This work is funded in part by the Center for Undergraduate Research and Scholarship (CURAS) at Creighton University. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations. The researchers gratefully acknowledge the support.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/10
Y1 - 2020/12/10
N2 - 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.
AB - 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.
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U2 - 10.1109/BigData50022.2020.9378356
DO - 10.1109/BigData50022.2020.9378356
M3 - Conference contribution
AN - SCOPUS:85103843464
T3 - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
SP - 4351
EP - 4358
BT - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
A2 - Wu, Xintao
A2 - Jermaine, Chris
A2 - Xiong, Li
A2 - Hu, Xiaohua Tony
A2 - Kotevska, Olivera
A2 - Lu, Siyuan
A2 - Xu, Weijia
A2 - Aluru, Srinivas
A2 - Zhai, Chengxiang
A2 - Al-Masri, Eyhab
A2 - Chen, Zhiyuan
A2 - Saltz, Jeff
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Big Data, Big Data 2020
Y2 - 10 December 2020 through 13 December 2020
ER -