Violent Vocabulary Extraction Methodology: Application to the Radicalism Detection on Social Media

Rekik, Amal; Jamoussi, Salma; Hamadou, Abdelmajid Ben

doi:10.1007/978-3-030-28374-2_9

Cited by 16 publications

(17 citation statements)

References 12 publications

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“…In conclusion, the approach in [38] shows a new methodology for violent vocabulary extraction from social networks. The approach collected a list of radical and non-radical users who shared violent extremism and radicalisation on social media.…”

Section: Approachmentioning

confidence: 96%

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Detection of Radicalisation and Extremism Online: A Survey

Khan

Pais

Cordeiro

et al. 2022

Preprint

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Introduction: Due to the lack of regulation, the large volume of user-generated online content reflects more closely the offline world than official news sources. Therefore, social media platforms have become an attractive space for anyone seeking independent information. One of the main goals of this work is to clarify concepts such as Extremism and Collective Radicalisation, Social Media, Sentiments/Emotions/Opinions Analysis, as well as the combinations of all of them. Methods: The automatic identification of extremism and collective radicalisation requires sophisticated Natural Language Processing (NLP) methods and resources, especially those dealing with opinions, emotions or sentiment analysis. Text mining and knowledge extraction are also crucial, in particular, directed toward social media and micro-blogging. Results: The present document comprehends a study on theoretical material, focusing on the main concepts of the subject, including the main problems and challenges, from the different areas that compose online radicalisation research. Understanding and detecting extremism and collective radicalism online has a connection to sentiment analysis and opinion mining. There are many barriers to understanding extremism and collective radicalisation; one is to differentiate between who is really engaged in the process and who is just eventually talking about it. Conclusions: The other focus of this work is to find the best ways to identify extremism and collective radicalisation on the internet, using sentiment analysis and focusing on probabilistic methods to create an unsupervised and language-independent approach.

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Section: Approachmentioning

confidence: 96%

“…It is about a supervised, language-dependent approach to extract and analyse violent vocabulary shared on social media and to be able to detect the emergence of radicalism [38]. First, the approach relies on a series of collected profiles from social networks interpreted by a domain expert as both extremist and non-extremist users.…”

Section: Approachmentioning

confidence: 99%

Detection of Radicalisation and Extremism Online: A Survey

Khan

Pais

Cordeiro

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…Moreover, for negative correlations, there is a frequent use of words related to uneasiness and fear as well as words having different meanings. After the investigation step of the different recent papers, we note that most of authors have worked on: (i) the consequences of psychological diseases such as suicide, violence or terrorism [15,16,17] than detecting the disease itself, (ii) some kind of personality disorders, in fact, we did not discover any paper which treats paranoid disease on social media. There is an excessive use of the classical machine learning approach [9,13,16,17] among the drawbacks of this approach is the ignorance of the semantic aspect and the difficulty founded in the selection of features.…”

Section: Related Workmentioning

confidence: 97%

“…In another context, there are works that have treated subjects related to psychological problems for that there are those who have treated the disease itself, such as narcissistic problems detection, mental disorders detection [13,14] and there are those who have worked on the consequences of these diseases like detection of terrorism, harassment and suicide [15,16,17]. For the work [13] the objective of this paper is to analyse data published on social media in order to: (i) detect the temperament of a person from their writing styles, metadata and personality traits using a machine learning approach, (ii) hunt for relations between the distinctive features by using the diverse measurements of relation detection.…”

Section: Related Workmentioning

confidence: 99%

“…Detecting of the symptoms and the consequences of mental disorder are also a very evolutionary research axis. In this context, [15] propose a method that takes advantage of several techniques such as Natural language processing and data mining to extract and analyse violent vocabulary shared on social media. The main idea of this work is to identify the two types of lexicon used by extremist and not-extremist persons.…”

Section: Related Workmentioning

confidence: 99%

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A deep learning approach for detecting the behavior of people having personality disorders towards Covid-19 from Twitter

Ellouze¹,

Mechti²,

Krichen³

et al. 2021

Preprint

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This paper proposes an architecture taking advantage of artificial intelligence and text mining techniques in order to: (i) detect paranoid people by classifying their set of tweets into two classes (Paranoid/not-Paranoid), (ii) ensure the surveillance of these people by classifying their tweets about Covid-19 into two classes (person with normal behavior, person with inappropriate behavior). These objectives are achieved using an approach that takes advantage of different information related to the textual part, user and tweets for features selection task and deep neural network for the classification task. We obtained as an F-score rate 70% for the detection of paranoid people and 73% for the detection of the behavior of these people towards Covid-19. The obtained results are motivating and encouraging researchers to improve them given the interest and the importance of this research axis.

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A Deep Learning Architecture Based on Advanced Textual Language Models for Detecting Disease Through Its Symptoms Associated with a Reinforcement Learning Algorithm

Ellouze

Belguith

2023

Communications in Computer and Information Science

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Violent Vocabulary Extraction Methodology: Application to the Radicalism Detection on Social Media

Cited by 16 publications

References 12 publications

Detection of Radicalisation and Extremism Online: A Survey

Detection of Radicalisation and Extremism Online: A Survey

A deep learning approach for detecting the behavior of people having personality disorders towards Covid-19 from Twitter

A Deep Learning Architecture Based on Advanced Textual Language Models for Detecting Disease Through Its Symptoms Associated with a Reinforcement Learning Algorithm

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