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.