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

Deep learning Extremism Machine learning Natural language processing Radicalization

Journal

Journal of ambient intelligence and humanized computing
ISSN: 1868-5137
Titre abrégé: J Ambient Intell Humaniz Comput
Pays: Germany
ID NLM: 101538212

Informations de publication

Date de publication:
12 Jan 2022
Historique:
received: 10 08 2021
accepted: 12 12 2021
entrez: 18 1 2022
pubmed: 19 1 2022
medline: 19 1 2022
Statut: aheadofprint

Résumé

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.

Identifiants

pubmed: 35039755
doi: 10.1007/s12652-021-03658-z
pii: 3658
pmc: PMC8754364
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1-37

Informations de copyright

© The Author(s) 2022.

Références

Psychol Assess. 2010 Jun;22(2):246-258
pubmed: 20528052
Neural Comput Appl. 2020 Sep 16;:1-14
pubmed: 32958982
Psychol Rev. 2021 Mar;128(2):264-289
pubmed: 32915010
Neuropsychopharmacology. 2021 Jan;46(1):252-253
pubmed: 32895453
Inf Fusion. 2016 Mar;28:45-59
pubmed: 32288689
J Biomed Inform. 2018 Jan;77:34-49
pubmed: 29162496
Cancer Res. 2019 Nov 1;79(21):5463-5470
pubmed: 31395609

Auteurs

Javier Torregrosa (J)

Computer Systems Engineering Department, Universidad Politécnica de Madrid, Madrid, Spain.

Gema Bello-Orgaz (G)

Computer Systems Engineering Department, Universidad Politécnica de Madrid, Madrid, Spain.

Eugenio Martínez-Cámara (E)

Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain.

Javier Del Ser (JD)

TECNALIA, Basque Research and Technology Alliance (BRTA), Mendaro, Spain.

David Camacho (D)

Computer Systems Engineering Department, Universidad Politécnica de Madrid, Madrid, Spain.

Classifications MeSH