Detecting national political unrest on Twitter

Haroon Raja, Muhammad U. Ilyas, Saad Saleh, Alex X. Liu, Hayder Radha

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The popular uprisings in a number of countries in the Middle East and North Africa in the Spring of 2011 were broadcasted live and enabled by local populations' access to social networking services such as Twitter and Facebook. The goal of this paper is to study the flow characteristics of the information flow of these broadcasts on Twitter. We have used language independent features of Twitter traffic to identify differences in information flows on Twitter mentioning countries experiencing some form of unrest, compared to traffic mentioning countries with peaceful political situations. We used these features to identify countries with political unstable situation. For empirical analysis, we collected several data sets of countries that were experiencing political unrest, as well as a set of countries in a control group that were not subject to such socio-political condition. Several different methods are used to model the flow of information between Twitter users in data sets as graphs, called information cascades. By using the dynamic properties of information cascades, naïve Bayes and SVM classifiers both achieve true positives rates of 100%, with false positives rates of 3% and 0%, respectively.
Original languageEnglish
Title of host publication2016 IEEE International Conference on Communications (ICC)
Number of pages7
ISBN (Print)978-1-4799-6665-3
Publication statusPublished - 27 May 2016
Event2016 IEEE International Conference on Communications (ICC) - Kuala Lumpur, Malaysia
Duration: 22 May 201627 May 2016


Conference2016 IEEE International Conference on Communications (ICC)


  • Twitter
  • Springs
  • Data models
  • Media
  • Tagging
  • Nominations and elections


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