Pandemic Dreams: Network Analysis of Dream Content During the COVID-19 Lockdown

Anu-Katriina Pesonen, Jari Lipsanen, Risto Halonen, Marko Elovainio, Nils Sandman, Juha-Matti Makelä, Minea Antila, Deni Bechard, Hanna M. Ollila, Liisa Kuula

Research output: Contribution to journalArticleScientificpeer-review


We used crowdsourcing (CS) to examine how COVID-19 lockdown affects the content of dreams and nightmares. The CS took place on the sixth week of the lockdown. Over the course of 1 week, 4,275 respondents (mean age 43, SD = 14 years) assessed their sleep, and 811 reported their dream content. Overall, respondents slept substantially more (54.2%) but reported an average increase of awakenings (28.6%) and nightmares (26%) from the pre-pandemic situation. We transcribed the content of the dreams into word lists and performed unsupervised computational network and cluster analysis of word associations, which suggested 33 dream clusters including 20 bad dream clusters, of which 55% were pandemic-specific (e.g., Disease Management, Disregard of Distancing, Elderly in Trouble). The dream-association networks were more accentuated for those who reported an increase in perceived stress. This CS survey on dream-association networks and pandemic stress introduces novel, collectively shared COVID-19 bad dream contents
Original languageEnglish
Article number573961
JournalFrontiers in Psychology
Number of pages10
Publication statusPublished - 1 Oct 2020
MoE publication typeA1 Journal article-refereed

Fields of Science

  • dream
  • sleep
  • crowdsourcing
  • cluster
  • network analysis
  • COVID-19
  • nightmare
  • stress
  • 515 Psychology

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