We study the role of perceived threats from other cultures induced by terrorist attacks and by a criminal event on public discourse and voters' support for radical 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 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 radical right party, AfD, while the opposite happens for 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 from other cultures on the success of nationalist parties.

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We study the role of perceived threats from other cultures induced by terrorist attacks and by a criminal event on public discourse and voters' support for radical 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 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 radical right party, AfD, while the opposite happens for 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 from other cultures on the success of nationalist parties.