The Analysis on the Role of Social Network in the Field of Anti-Terrorism Take the "East Turkistan" Organization as an Example

Liu, Xiaoyuan; Sun, Tianyang; Bu, Fanliang; Qin, Hang

doi:10.1109/icmcce51767.2020.00493

Cited by 2 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to the efforts made by governments and social media platforms, cybersecurity researchers have also contributed to understanding and studying this phenomenon from various aspects, thereby aiding in the efforts of detecting and preventing it. They have utilized various approaches such as artificial intelligence (AI) and social networks analysis (SNA), which have previously shown effectiveness in ad- dressing issues in various fields, including detecting fake news [8], [9], examining public opinion changes towards COVID-19 vaccination in social networks [10]- [12], community detection [13], [14], business [15], [16], security, and intelligence [17], [18]. In this context, numerous studies have been conducted to minimize the impact of this process.…”

mentioning

confidence: 99%

A Comprehensive Survey of Detection and Prevention Approaches for Online Radicalization: Identifying Gaps and Future Directions

Berjawi,

Fenza,

Loia

2023

IEEE Access

View full text Add to dashboard Cite

In the digital era, online radicalization has emerged as a significant concern for governments, social media platforms, and researchers. Detecting and preventing online radicalization have become key priorities, leading to extensive research efforts. This study presents a comprehensive survey of existing works in this field, covering various techniques and methodologies. An extensive assessment of 68 publications from databases such as IEEE, SCOPUS, and Web of Science (WoS) was conducted to analyze recent literature on detecting and preventing online radicalization. This research provides an overview of the definition of online radicalization and its relationship with social media. It explores different types and sources of datasets used in studying online radicalization. Additionally, it categorizes approaches and techniques, including Machine Learning (ML), Deep Learning (DL), and Graph algorithms, for detecting and preventing online radicalization. The survey identifies limitations and challenges in the field, highlighting existing gaps and suggesting potential directions for further study. To the best of the authors' knowledge, this work is the first of its kind to undertake such a holistic investigation that consolidates these methodologies presenting them in an accessible manner. The findings contribute as a valuable resource for academics, decision-makers, and professionals working in the field of counter-radicalization and provide insights into existing countermeasures against this expanding threat.

show abstract