Abstract:
The popular uprisings in a number of countries in the Middle East and North
Africa in the Spring of 2011 were enabled in large part by local populations access
to social networking services such as Twitter and Facebook. This thesis
attempts to use language independent features of Twitter traffic mentioning
different countries to distinguish between countries that are politically unstable
and others that are stable. Towards this end, we collected several data
sets of countries that were experiencing political unrest during the period
now known as the Arab Spring, as well as a set of countries that were not.
Several different methods are used to model the flow of information between
Twitter users in data sets as graphs, called information cascades. Na¨ıve
Bayesian, Support Vector Machines (SVM) and Bayesian logistic regression
classifiers are applied to all data sets. By using the dynamic properties of
information cascades, Na¨ıve Bayesian and SVM classifiers both achieve true
positives rates of 100%, with false positives rates of 3% and 0%, respectively.
