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

Rehman, Zia Ul; Abbas, Sagheer; Khan, Muhammad Adnan; Mustafa, Ghulam; Fayyaz, Hira; Hanif, Muhammad Kashif; Saeed, Mohammad

doi:10.32604/cmc.2020.012770

Cited by 23 publications

(27 citation statements)

References 13 publications

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“…( 2018 ); Fernandez and Alani ( 2018 ); Rehman et al. ( 2021 ); Kursuncu et al. ( 2019 ); Fernandez et al.…”

Section: Nlp Dataset and Toolsmentioning

confidence: 99%

“…( 2012 ); Rehman et al. ( 2021 ); Sabbah and Selamat ( 2015 ); Sharif et al. ( 2019 , 2020 ); Yang et al.…”

Section: Nlp Techniques For Extremism Researchmentioning

confidence: 99%

“…( 2019 ) NB Masood ( 2021 ); Rehman et al. ( 2021 ); Sharif et al. ( 2019 , 2020 ); Abd-Elaal et al.…”

Section: Applications Of Nlp In Extremism Researchmentioning

confidence: 99%

“…2013 ) 5.000.000 tweets—N/A users English Twitter Agarwal and Sureka ( 2015 ) Religious Texts Used By ISIS (Fifth-Tribe 2017 ) 2685 religious texts English Religious texts Rehman et al. ( 2021 ); Abd-Elaal et al. ( 2020 ) Tweets targeting ISIS (ActiveGalaXy 2016 ) 122.000 tweets—95.725 users English Twitter Rehman et al.…”

Section: Nlp Dataset and Toolsmentioning

confidence: 99%

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A survey on extremism analysis using natural language processing: definitions, literature review, trends and challenges

Torregrosa

Bello-Orgaz

Martínez‐Cámara

et al. 2022

J Ambient Intell Human Comput

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Extremism has grown as a global problem for society in recent years, especially after the apparition of movements such as jihadism. This and other extremist groups have taken advantage of different approaches, such as the use of Social Media, to spread their ideology, promote their acts and recruit followers. The extremist discourse, therefore, is reflected on the language used by these groups. Natural language processing (NLP) provides a way of detecting this type of content, and several authors make use of it to describe and discriminate the discourse held by these groups, with the final objective of detecting and preventing its spread. Following this approach, this survey aims to review the contributions of NLP to the field of extremism research, providing the reader with a comprehensive picture of the state of the art of this research area. The content includes a first conceptualization of the term extremism, the elements that compose an extremist discourse and the differences with other terms. After that, a review description and comparison of the frequently used NLP techniques is presented, including how they were applied, the insights they provided, the most frequently used NLP software tools, descriptive and classification applications, and the availability of datasets and data sources for research. Finally, research questions are approached and answered with highlights from the review, while future trends, challenges and directions derived from these highlights are suggested towards stimulating further research in this exciting research area.

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“…( 2018 ); Fernandez and Alani ( 2018 ); Rehman et al. ( 2021 ); Kursuncu et al. ( 2019 ); Fernandez et al.…”

Section: Nlp Dataset and Toolsmentioning

confidence: 99%

“…( 2012 ); Rehman et al. ( 2021 ); Sabbah and Selamat ( 2015 ); Sharif et al. ( 2019 , 2020 ); Yang et al.…”

Section: Nlp Techniques For Extremism Researchmentioning

confidence: 99%

“…( 2019 ) NB Masood ( 2021 ); Rehman et al. ( 2021 ); Sharif et al. ( 2019 , 2020 ); Abd-Elaal et al.…”

Section: Applications Of Nlp In Extremism Researchmentioning

confidence: 99%

Section: Nlp Dataset and Toolsmentioning

confidence: 99%

See 2 more Smart Citations

A survey on extremism analysis using natural language processing: definitions, literature review, trends and challenges

Torregrosa

Bello-Orgaz

Martínez‐Cámara

et al. 2022

J Ambient Intell Human Comput

View full text Add to dashboard Cite

show abstract

“…Online hate speech is characterized as the use of an offensive language, aimed at a specific group of people who share some common trait [1], while social networks have been recognized as a very favorable medium often used for planning and executing hate attack related activities [2]. Beyond the psychological harm, such toxic online content may be influencing and radicalizing individuals and could lead to actual hate crimes [3]. Therefore, it is important to detect such cases of cyber-aggression and cyber-bullying in good time [4].…”

Section: Introductionmentioning

confidence: 99%

Arabic Offensive and Hate Speech Detection Using a Cross-Corpora Multi-Task Learning Model

et al. 2021

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As social media platforms offer a medium for opinion expression, social phenomena such as hatred, offensive language, racism, and all forms of verbal violence have increased spectacularly. These behaviors do not affect specific countries, groups, or communities only, extending beyond these areas into people’s everyday lives. This study investigates offensive and hate speech on Arab social media to build an accurate offensive and hate speech detection system. More precisely, we develop a classification system for determining offensive and hate speech using a multi-task learning (MTL) model built on top of a pre-trained Arabic language model. We train the MTL model on the same task using cross-corpora representing a variation in the offensive and hate context to learn global and dataset-specific contextual representations. The developed MTL model showed a significant performance and outperformed existing models in the literature on three out of four datasets for Arabic offensive and hate speech detection tasks.

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Similarity Fuzzy Semantic Network for Social Media Analysis

Peña

Manuel

2022

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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

Cited by 23 publications

References 13 publications

A survey on extremism analysis using natural language processing: definitions, literature review, trends and challenges

A survey on extremism analysis using natural language processing: definitions, literature review, trends and challenges

Arabic Offensive and Hate Speech Detection Using a Cross-Corpora Multi-Task Learning Model

Similarity Fuzzy Semantic Network for Social Media Analysis

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