Pandemics, Music, and Collective Sentiment: Evidence From the Outbreak of COVID-19

M. Liu, E. Zangerle, X. Hu, Alessandro Melchiorre, Markus Schedl

Research output: Chapter in Book/Report/Conference proceedingConference proceedingspeer-review

Abstract

The COVID-19 pandemic causes a massive global health crisis and produces substantial economic and social distress, which in turn may cause stress and anxiety among people. Real-world events play a key role in shaping collective sentiment in a society. As people listen to music daily everywhere in the world, the sentiment of music being listened to can reflect the mood of the listeners and serve as a measure of collective sentiment. However, the exact relationship between real-world events and the sentiment of music being listened to is not clear. Driven by this research gap, we use the unexpected outbreak of COVID19 as a natural experiment to explore how users’ sentiment of music being listened to evolves before and during the outbreak of the pandemic. We employ causal inference approaches on an extended version of the LFM-1b dataset of listening events shared on Last.fm, to examine the impact of the pandemic on the sentiment of music listened to by users in different countries. We find that, after the first COVID-19 case in a country was confirmed, the sentiment of artists users listened to becomes more negative. This negative effect is pronounced for males while females’ music emotion is less influenced by the outbreak of the COVID-19 pandemic. We further find a negative association between the number of new weekly COVID19 cases and users’ music sentiment. Our results provide empirical evidence that public sentiment can be monitored based on collective music listening behaviors, which can contribute to research in related disciplines.
Original languageEnglish
Title of host publicationProceedings of the 21th International Society for Music Information Retrieval Conference (ISMIR 2020)
Number of pages9
Publication statusPublished - 2020

Fields of science

  • 202002 Audiovisual media
  • 102 Computer Sciences
  • 102001 Artificial intelligence
  • 102003 Image processing
  • 102015 Information systems

JKU Focus areas

  • Digital Transformation

Cite this