Working Paper: CEPR ID: DP14455
Authors: Francesco Giavazzi; Giacomo Lemoli; Gaia Rubera; Felix Iglhaut
Abstract: 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.
Keywords: national elections; political parties; text analysis; social media; terrorism
JEL Codes: C45; D72; H56
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Terrorist attacks (H56) | increases in textual similarity to AfD language (C59) |
| Salient crime event (K42) | increases in textual similarity to AfD language (C59) |
| increases in textual similarity to AfD language (C59) | positive changes in vote shares for AfD (D79) |
| Salient crime event (K42) | decreases in textual similarity to other parties' language (Z13) |
| Terrorist attacks (H56) | decreases in textual similarity to other parties' language (Z13) |
| perceived threats from cultural diversity (F52) | influence on support for nationalist parties (F52) |