Change Point Detection in Terrorism-Related Online Content Using Deep Learning Derived Indicators

Theodosiadou, Ourania; Pantelidou, Kyriaki; Bastas, Nikolaos; Chatzakou, Despoina; Tsikrika, Theodora; Vrochidis, Stefanos; Kompatsiaris, Ioannis

doi:10.3390/info12070274

Cited by 16 publications

(9 citation statements)

References 28 publications

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“…The last process identifies the topic using predicted change points to improve accuracy. [15] To present a system that would enable to pinpointing of significant change points in time series relevant to extremism that could help in determining the occurrence of activities that require increasing monitoring.…”

Section: Authors and Publication Yearsmentioning

confidence: 99%

Opinion Mining in Arabic Extremism Texts: A Systematic Literature Review

Al-Shukrawi,

Jamil,

Alzuabidi

et al. 2023

AKJS

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In this paper, a systematic literature review was provided that investigated the present evidence regarding extremist words in Arabic opinion mining methods. This study aimed to perform a Systematic Literature Review (SLR) in order to detect, evaluate, and synthesize the existing evidence regarding opinion mining techniques for extremist Arabic text. From the SLR, it is evident that opinion-mining techniques have several opportunities for detecting extremism in the Arabic text. Over the past few years, multimedia sentiment analysis has gained traction as visual content is becoming more incorporated into social media networking. Opinion mining is the process of identifying, extracting, and categorizing views about anything. It is a sort of Natural Language Processing (NLP) used to track public sentiment about a certain law, policy, or marketing, for example. It entails the creation of a method for collecting and analyzing comments and opinions concerning legislation, regulations, policies, and so on that are posted on social media. The process of information extraction is critical since it is both a beneficial tool and a difficult undertaking. In this article, we have examined the recent and advanced methodologies to extract sentiment from a web-wide item, opinion-mining methods must be automated. Also, we have analyzed the novel Artificial Intelligence and lexical-based algorithms for sentiment analysis. These methodologies find better applications in the customer feedback analysis of any organization.

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Section: Authors and Publication Yearsmentioning

confidence: 99%

Opinion Mining in Arabic Extremism Texts: A Systematic Literature Review

Al-Shukrawi,

Jamil,

Alzuabidi

et al. 2023

AKJS

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show abstract

“…It includes Arabic and English posts from the AlJihad Network, available only in English and Arabic [84]. Theodosiadou et al in [85] used it to analyze the English posts of jihadist movements. Petrovskiy et al in [86] used the KavkazChat dataset [87], which contains over 600k posts related to Islamic jihadists in the Caucasus, in multiple languages (Arabic, English, Russian, etc.).…”

Section: Blog and Forumsmentioning

confidence: 99%

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

Berjawi,

Fenza,

Loia

2023

IEEE Access

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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.

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“…While, Theodosiadou et al implemented CNN to identify terrorism and hate speech‐related text from existing datasets on dark web forums. The labeled text was taken from existing kaggle datasets on terrorism and hatespeech to assist the deep learning model to learn the patterns for text classification (Theodosiadou et al, 2021). It can be observed from the research articles that neural networks are suitable where a large amount of training data is available.…”

Section: Data Enrichmentmentioning

confidence: 99%

Open source intelligence extraction for terrorism‐related information: A review

Chaudhary

Bansal

2022

WIREs Data Min & Knowl

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In this contemporary era, where a large part of the world population is deluged by extensive use of the internet and social media, terrorists have found it a potential opportunity to execute their vicious plans. They have got a befitting medium to reach out to their targets to spread propaganda, disseminate training content, operate virtually, and further their goals. To restrain such activities, information over the internet in context of terrorism needs to be analyzed to channel it to appropriate measures in combating terrorism. Open Source Intelligence (OSINT) accounts for a felicitous solution to this problem, which is an emerging discipline of leveraging publicly accessible sources of information over the internet by effectively utilizing it to extract intelligence. The process of OSINT extraction is broadly observed to be in three phases (i) Data Acquisition, (ii) Data Enrichment, and (iii) Knowledge Inference. In the context of terrorism, researchers have given noticeable contributions in compliance with these three phases. However, a comprehensive review that delineates these research contributions into an integrated workflow of intelligence extraction has not been found. The paper presents the most current review in OSINT, reflecting how the various state‐of‐the‐art tools and techniques can be applied in extracting terrorism‐related textual information from publicly accessible sources. Various data mining and text analysis‐based techniques, that is, natural language processing, machine learning, and deep learning have been reviewed to extract and evaluate textual data. Additionally, towards the end of the paper, we discuss challenges and gaps observed in different phases of OSINT extraction. This article is categorized under: Application Areas > Government and Public Sector Commercial, Legal, and Ethical Issues > Social Considerations Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

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Change Point Detection in Terrorism-Related Online Content Using Deep Learning Derived Indicators

Cited by 16 publications

References 28 publications

Opinion Mining in Arabic Extremism Texts: A Systematic Literature Review

Opinion Mining in Arabic Extremism Texts: A Systematic Literature Review

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

Open source intelligence extraction for terrorism‐related information: A review

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