PREDICTING ELECTION RESULTS USING ONLINE SENTIMENTS IN RUSSIA AND THE US

Abstract

Social media is one of the most prominent spaces for communication in the 21st century and as such is deeply intertwined with our political life being both its reflection and influencer. Due to this phenomenon, there is a rising interest among scholars in using social media to predict election results with mixed results. This thesis aims to test the connection between political processes, regime types, and predictive power of social media data by using two countries as case studies: the United States and Russia. Several tentative results are produced. Firstly, the predictive power of online opinions is revealed to be higher for the US as compared to Russia – presumably due to the former’s democratic and the latter’s non-democratic political system. Secondly, filtering on certain sociodemographic groups can affect the accuracy of predictions. For instance, while only selecting large city urban populations can increase the errors in predictions for both countries, removing tweets from election candidates can have an asymmetric effect in two countries: improving the predictions for Russia, while decreasing their accuracy for the US. While the results have little claim to generalizability across regime types, they can provide a starting groundwork for further research on the way different political phenomena and conditions shape the way election predictions can be improved.