Suchergebnisse

Computational methods offer a new perspective on the evolving agendas of right-wing movements and parties online. This article showcases computational approaches to text analysis (specifically so-called topic models) to diachronically investigate nativist right-wing issues in social media by comparing comments posted on the Facebook page of the Pegida movement to those of the Alternative for Germany. After describing topic modelling as an increasingly popular method and drawing on the literature on right-wing nativism online, we investigate a set of shared issues relevant to the mobilization of commentators, including opposition to Islam, migration, the government and the media. We furthermore show contrastively how issue prevalence differs between the two groups, and how issue shares change over time, in some instances converging on a shared nativist core. We close with a series of suggestions on the utility of computation content analysis for the study of rapidly evolving political agendas.

The Czech Republic (or Czechia) is facing the second wave of COVID-19 epidemic, with the rate of growth in the number of confirmed cases (among) the highest in Europe. Learning from the spring first wave, when many countries implemented interventions that effectively stopped national economics (i.e., a form of lockdown), political representations are now unwilling to do that again, at least until really necessary. Therefore, it is necessary to look back and assess efficiency of each of the first wave restrictions, so that interventions can now be more finely tuned. We develop an age-structured model of COVID-19 epidemic, distinguish several types of contact, and divide the population into 206 counties. We calibrate the model by sociological and population movement data and use it to analyze the first wave of COVID-19 epidemic in Czechia, through assessing effects of applied restrictions as well as exploring functionality of alternative intervention schemes that were discussed later. To harness various sources of uncertainty in our input data, we apply the Approximate Bayesian Computation framework. We found that (1) personal protective measures as face masks and increased hygiene are more effective than reducing contacts, (2) delaying the lockdown by four days led to twice more confirmed cases, (3) implementing personal protection and effective testing as early as possible is a priority, and (4) tracing and quarantine or just local lockdowns can effectively compensate for any global lockdown if the numbers of confirmed cases not exceedingly high.