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Brief Research Report ARTICLE

Front. Big Data | doi: 10.3389/fdata.2020.00032

Dashboard of sentiment in Austrian social media during COVID-19 Provisionally accepted The final, formatted version of the article will be published soon. Notify me

 Max Pellert1, 2*,  Jana Lasser1, 2, Hannah Metzler1, 2, 3 and  David Garcia1, 2
  • 1Complexity Science Hub Vienna (CSH), Austria
  • 2Section for Science of Complex Systems, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Austria
  • 3Institute for Globally Distributed Open Research and Education (IGDORE), Sweden

To track online emotional expressions on social media platforms close to real-time during the COVID-19 pandemic, we build a self-updating monitor of emotion dynamics using digital traces from three different data sources in Austria. This enables decision makers and the interested public to assess dynamics of sentiment online during the pandemic. We use web scraping and API access to retrieve data from the news platform, Twitter and a chat platform for students. We document the technical details of our workflow in order to provide materials for other researchers interested in building a similar tool for different contexts. Automated text analysis allows us to highlight changes of language use during COVID-19 in comparison to a neutral baseline. We use special word clouds to visualize that overall difference. Longitudinally, our time series show spikes in anxiety that can be linked to several events and media reporting. Additionally, we find a marked decrease in anger. The changes last for remarkably long periods of time (up to 12 weeks). We discuss these and more patterns and connect them to the emergence of collective emotions. The interactive dashboard showcasing our data is available online at Our work is part of an web archive of resources on COVID-19 collected by the Austrian National Library.

Keywords: COVID-19, Collective emotions, Real-time monitoring, Social Media, Digital traces, Webscraping, Dashboard, Affective sciences

Received: 18 Jun 2020; Accepted: 11 Aug 2020.

Copyright: © 2020 Pellert, Lasser, Metzler and Garcia. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Max Pellert, Complexity Science Hub Vienna (CSH), Vienna, Austria,