Skip to main content
main-content
Top

Hint

Swipe to navigate through the articles of this issue

01-12-2019 | Research | Issue 1/2019 Open Access

Human-centric Computing and Information Sciences 1/2019

Detection and classification of social media-based extremist affiliations using sentiment analysis techniques

Journal:
Human-centric Computing and Information Sciences > Issue 1/2019
Authors:
Shakeel Ahmad, Muhammad Zubair Asghar, Fahad M. Alotaibi, Irfanullah Awan
Important notes

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1186/​s13673-019-0185-6) contains supplementary material, which is available to authorized users.
A correction to this article is available online at https://​doi.​org/​10.​1186/​s13673-019-0189-2.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

Identification and classification of extremist-related tweets is a hot issue. Extremist gangs have been involved in using social media sites like Facebook and Twitter for propagating their ideology and recruitment of individuals. This work aims at proposing a terrorism-related content analysis framework with the focus on classifying tweets into extremist and non-extremist classes. Based on user-generated social media posts on Twitter, we develop a tweet classification system using deep learning-based sentiment analysis techniques to classify the tweets as extremist or non-extremist. The experimental results are encouraging and provide a gateway for future researchers.

Our product recommendations

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Maschinenbau + Werkstoffe​​​​​​​




Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 58.000 Bücher
  • über 300 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Testen Sie jetzt 30 Tage kostenlos.

Show more products
Supplementary Material
Additional file 1: Appendix A. A sample implementation of LSTM+CNN for extremist classification.
Literature
About this article

Other articles of this Issue 1/2019

Human-centric Computing and Information Sciences 1/2019 Go to the issue

Premium Partner

    Image Credits