Changes in Policy Preferences in German Tweets during the COVID Pandemic. (arXiv:2308.04444v1 [cs.CY])

Online social media have become an important forum for exchanging political
opinions. In response to COVID measures citizens expressed their policy
preferences directly on these platforms. Quantifying political preferences in
online social media remains challenging: The vast amount of content requires
scalable automated extraction of political preferences — however fine grained
political preference extraction is difficult with current machine learning (ML)
technology, due to the lack of data sets. Here we present a novel data set of
tweets with fine grained political preference annotations. A text
classification model trained on this data is used to extract policy preferences
in a German Twitter corpus ranging from 2019 to 2022. Our results indicate that
in response to the COVID pandemic, expression of political opinions increased.
Using a well established taxonomy of policy preferences we analyse fine grained
political views and highlight changes in distinct political categories. These
analyses suggest that the increase in policy preference expression is dominated
by the categories pro-welfare, pro-education and pro-governmental
administration efficiency. All training data and code used in this study are
made publicly available to encourage other researchers to further improve
automated policy preference extraction methods. We hope that our findings
contribute to a better understanding of political statements in online social
media and to a better assessment of how COVID measures impact political



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