Social networks data analysis with semantics: application to the radicalization problem

Social networks are currently the main medium through which terrorist organisations reach out to vulnerable people with the objective of radicalizing and recruiting them to commit violent acts of terrorism. Fortunately, radicalization on social networks has warning signals and indicators that can be detected at the early stages of the radicalization process. In this article, we explore the use of the semantic web and domain ontologies to automatically mine the radicalisation indicators from messages and posts on social networks. Specifically, we propose an ontology for the radicalisation domain as well as an approach to automatically compute the radicalisation indicators. In our approach, social messages are annotated with concepts and instances defined formally in a domain ontology. Annotations are then exploited within an inference phase to identify the messages exhibiting a radicalization indicator. The indicators are then computed using a set of SPARQL queries. We have implemented and evaluated the proposed solution based on a pubic Tweet dataset. Obtained results show the effectiveness of our approach. The article presents also the implemented prototype.

[1]  Matthew D. Turner A Simple Ontology for the Analysis of Terrorist Attacks , 2011 .

[2]  Lisa Kaati,et al.  Detecting Jihadist Messages on Twitter , 2015, 2015 European Intelligence and Security Informatics Conference.

[3]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[4]  Djamal Benslimane,et al.  Measuring the Radicalisation Risk in Social Networks , 2017, IEEE Access.

[5]  Lisa Kaati,et al.  Detecting Multipliers of Jihadism on Twitter , 2015, 2015 IEEE International Conference on Data Mining Workshop (ICDMW).

[6]  Matthew Rowe,et al.  Mining Pro-ISIS Radicalisation Signals from Social Media Users , 2016, ICWSM.

[7]  Nada Lavrac,et al.  Relational and Semantic Data Mining - - Invited Talk - , 2015, LPNMR.

[8]  Khalil Drira,et al.  A collaborative methodology for tacit knowledge management: Application to scientific research , 2016, Future Gener. Comput. Syst..

[9]  Walid Magdy,et al.  #FailedRevolutions: Using Twitter to study the antecedents of ISIS support , 2015, First Monday.

[10]  Andreas Abecker,et al.  Ontologies and the Semantic Web , 2011, Handbook of Semantic Web Technologies.

[11]  Ashish Sureka,et al.  Using KNN and SVM Based One-Class Classifier for Detecting Online Radicalization on Twitter , 2015, ICDCIT.

[12]  Dennis McLeod,et al.  Context-based information analysis for the Web environment , 2012, Knowledge and Information Systems.

[13]  Martin J. O'Connor,et al.  SQWRL: A Query Language for OWL , 2009, OWLED.