Technology understood within the theoretical framework of infrastructural power developed by Michael Mann has received limited, albeit increasing, attention. Therefore, there are still many fringes when it comes to understanding technology as an element of state capacity. This paper conceptually expands the concept of technological infrastructural power, based on the well-known term brought by Michael Mann. Through data from Swedish municipalities, it is shown that telecommunications varied dramatically internally, with some epicenters bringing together most of such technological power. Second, three hypotheses are contrasted on what reasons explain the variation of technological power within the same state: social conflict, geography, and urbanization processes. Contrary to the existing literature that emphasizes the role of conflict in building strong states, in the case of "technological power", conflicts and geography did not play a significant role. In contrast, the classic urban / rural distinction happens to be the main determinant of early state-owned technological concentration. 

Scholars from all over the glove are concerned about the increasing partisan antagonisms in form of antipathy, distrust and negative affect towards political out-parties, which has been labeled as Affective Polarization (AP). Despite the importance of the phenomenon, we know little about its causes, especially in multi-party systems. In this article, using a 12-wave panel dataset on Spaniards political attitudes, I contend that an important cause of AP is individuals’ growing perceived party polarization regarding the two main cleavages of the country: the left-right and territorial conflicts. Through the analysis of public opinion trends, I analyze the abrupt irruption of new radical parties on left and right of the established parties. A recrudescence of both conflicts occurred, leading individuals to perceive a deeper gap between the positions of the parties at the extremes and to change their perceptions of the established parties’ positions. Most importantly, using xtreg models with fixed effects, I find that these changes on individuals’ perceptions of party left-right distance and the territorial cleavage are able to notably boost AP, in consonance with research that have underlined the importance of ideological and policy matters on AP beyond party identities. 

Text mining and sentiment analysis based on Twitter data can serve as a powerful sentinel to measure public opinion on a broad variety of topics. It can also be used to discover patterns in the external communication of specific actors. Leveraging these techniques and building on a large-scale original dataset of almost ten million Twitter posts from the past five years, this paper explores public perceptions of the EU’s relationship with five key partners. It also zooms in on the usage of the EU’s official Twitter accounts regarding these countries. 

Theoretically, we would expect that free democratic elections would prevent criminal or corrupt politicians from being elected to office. However, in practice, such candidates are frequently elected and re-elected into power. This raises the question, why do voters forgive criminal officials at the ballot? One explanation for this surprising behaviour is that voters base their decision using some underlying social preferences such as their ethnicity rather than policy platforms. It could be that voters believe that selecting ethnically aligned candidates would improve their access to political power or provide some form of patronage, even if it means overlooking allegations of wrongdoing. In this article, I develop a theoretical model to show how ethnic voting can provide an explanation for the electoral success of criminal candidates and empirically test its predictions using data from the Indian Lok Sabha elections held in 2014.