Cornelius Erfort's repositories
partypress
Political parties emphasize different issues in their public communication efforts to address the topics of the day and to strengthen their policy profiles. Here, we develop and evaluate models to automatically classify parties' press releases into issue categories to dynamically measure issue attention.
germany-53-17-districts
Calculating comparable, historic county-level election results for West Germany 1953-2017 using geodata.
erststimme2017.de
(German) Election forecast for the single-member districts at the 2017 German federal elections.
web-data-and-text-r
Material for the class Automated Web Data Collection and Text as Data (2023)
cornelius-erfort.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
germany-53-21-districts
Updated for 2021. Calculating comparable, historic county-level election results for West Germany 1953-2021 using geodata.
measuring-protest
Protests are an important and well researched aspect of political behavior, making measurement validity crucial. Unlike conventional forms of behavior such as voting, protest can be difficult to observe. Most studies rely on news articles for event coding, introducing a possible selection bias. Validation is often done by comparing the characteristics of different newspaper measures or using independent sources. In this paper, I benchmark a manually and a partly automatically coded dataset from the PolDem project against a unique, large government dataset covering all extreme right demonstrations and rallies in Germany from 2005 to 2020. Coverage of events in newspapers mainly depends on the region and the number of participants. Conversely, machine learning can provide a good confidence estimate about the possible misdetection of an event. The results have important implications for the study of protests. Researchers should carefully assess the advantages and shortfalls of news media based datasets.