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Political representation is a central pillar of modern democracies. Well-functioning representation is essential for trust between voters and political parties. However, the trust that political parties act in the interest of their voters is repeatedly challenged - not least by populist forces.

Human coding of large text corpora is an important, but very labor intensive and time-consuming pillar of political science research. Consequently, the rise and constant improvement of automated classification models presents itself as a perfect possibility to alleviate this burden, by enabling researchers to execute large coding tasks within a fraction of the time and work typically required. However, the reliability, consistency and quality of automated coding remains a concern. Can the computer really take over from humans and how comparable will the results be?

The project "Visions of the Post-Viral Society: Changes in Political Perceptions in the Course of the Covid19-Crisis" investigates whether new forms and sources of political legitimacy emerged during and as a result of the Covid19 pandemic, in particular as a consequence of the strong political intervention in public life and the economy. Machine learning algorithms will be used to analyze the evolution of political discourse on social media before, during and after the crisis in order to identify new and changing political demands and interests of citizens active on these platforms.

Since the 1990s, the protest scene in Germany has been shaped by issues such as migration, the environment and welfare. These issues have often led to violent mobilisations and counter-mobilisations on the streets. The aim of the WZB ProtestMonitoring is to monitor the German protest landscape in a systematic way. On an ongoing basis, we analyse the temporal and spatial contexts of protest events, the discursive opportunity structures of protest, as well as the radicalisation processes of individual protest actors.