Terrorist Attacks, Cultural Incidents, and the Vote for Radical Parties: Analyzing Text from Twitter

Abstract

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.

Interoperability and Targeted Attacks on Terrorist Organizations Using Intelligent Tools from Network Science

“…Each attack strategy is evaluated based on "Interoperability", estimated by the size of the giant component of the network. Unlike most studies in this area, which rely on data from social media interactions [77][78][79][80][81][82][83][84][85][86][87][88][89][90][91], our work is grounded in real-world social ties among terrorists, namely physical face-to-face interactions [48,49,99,102,104,106].…”

“…This is because this kind of data are almost always classified or inappropriate for academic research [50][51][52][53][54][55]. Unlike most studies in this area, which rely on data from social media interactions [77][78][79][80][81][82][83][84][85][86][87][88][89][90][91], our work is grounded on real-world social ties among terrorists, namely physical face-to-face interactions. This kind of data is more reliable because social media interactions can be used for deliberately sharing false realities, aiming to mislead law enforcement agencies [92][93][94][95][96].…”

“…While numerous studies have investigated targeted node removal strategies, the scope and methods often differ. For instance, research has been conducted on the effectiveness of various node removal strategies, but a majority of them rely heavily on synthetic or social media data [77][78][79][80][81][82][83][84][85][86][87][88][89][90][91]. In contrast, the current study leverages real-world data from face-to-face interactions within terrorist networks, adding a layer of practical relevance not universally present in the literature.…”

Hostility on Twitter in the Aftermath of Terror Attacks

“…Czymara et al [42] showed, based on over 100,000 YouTube comments on immigration-related issues before and after a set of attacks, that ethnic insulting was more present after attacks. Analyzing 11 events related to terrorism and crime, Giavazzi et al [30] showed the language used on German Twitter became more similar to the language used by the rightwing Alternative für Deutschland (AfD) party after such events. Finally, Jović et al [43], analyzing views of Wikipedia pages before and after attacks, demonstrated that attacks boosted attention to pages on content related to the attackers (such as terrorism or Islam), as well as to pages on security and self-perception (such as the national or religious identities of one's society).…”

Hostility on Twitter in the Aftermath of Terror Attacks

“…Likewise, online language use can have consequences with respect to offline behavior. For example, the level of online hate speech has been shown to correlate with the vote share of farright parties [30]. Even more dramatically, hate speech against refugees on Facebook has also predicted anti-refugee violence in Germany [31].…”