Terrorist Attacks, Cultural Incidents and the Vote for Radical Parties: Analyzing Text from Twitter
We study the role of perceived threats from cultural diversity induced by terrorist attacks and a salient criminal event on public discourse and voters' support for far-right parties. We first develop a rule which allocates Twitter users in Germany to electoral districts and then use a machine learning method to compute measures of textual similarity between the tweets they produce and tweets by accounts of the main German parties. Using the dates of the aforementioned exogenous events we estimate constituency-level shifts in similarity to party language. We find that following these events Twitter text becomes on average more similar to that of the main far-right party, AfD, while the opposite happens for some of the other parties. Regressing estimated shifts in similarity on changes in vote shares between federal elections we find a significant association. Our results point to the role of perceived threats on the success of nationalist parties.
We thank Guido Tabellini, Marco Tabellini, Jesse Shapiro and participants in the Spring 2019 NBER Political Economy meeting for very helpful comments. We gratefully acknowledge a grant from the Italian Ministry for Universities MIUR PRIN Prot. 2015FMRE5X The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.