Changes in Policy Preferences in German Tweets during the COVID Pandemic

TL;DR

This study introduces a new dataset and machine learning approach to analyze fine-grained political preferences expressed on German Twitter during COVID-19, revealing increased political opinion expression and shifts in specific policy categories.

Contribution

The paper presents a novel annotated dataset and a text classification model for extracting detailed political preferences from social media data during the pandemic.

Findings

Increased expression of political opinions during COVID-19

Dominance of pro-welfare, pro-education, and pro-governmental efficiency categories

Public political discourse intensified in response to pandemic measures

Abstract

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 preferences.