DOI: 10.5061/dryad.r7sqv9scc
title: Epidemic Dreams: Dreaming about health during the COVID-19 pandemic

description: The continuity hypothesis of dreams - a widely studied model of dreaming -
 suggests that the content of dreams is largely continuous with the waking
 experiences of the dreamer. Given the unprecedented nature of the
 experiences during the pandemic of COVID-19, we studied the continuity
 hypothesis in the context of such a pandemic. To that end, we implemented
 a state-of-the-art deep-learning algorithm that can accurately extract
 mentions of virtually any medical condition from text and applied it to
 two sets of data collected during the COVID-19 pandemic: 2,888 dream
 reports (dreaming life experiences), and 57M tweets mentioning the
 pandemic (waking life experiences). We found that the health expressions
 that were shared by both sets were common COVID-19 symptoms (e.g.,
 coronavirus, anxiety, coughing, and stress), suggesting that dreams
 reflected people’s real-world experiences. On the other hand, we found
 that the health expressions that distinguished the two sets reflected
 differences in thought processes: health expressions in waking life
 reflected a linear and logical thought process and, as such, described
 realistic symptoms or related disorders (e.g., body aches, nasal pain,
 SARS, H1N1); by contrast, those in dreaming life reflected a thought
 process likely based on the activation of the visual and emotional areas
 of the brain and, as such, described either conditions not necessarily
 associated with the pandemic’s virus (e.g., maggots, deformities,
 snakebites), or conditions of surreal nature (e.g., teeth suddenly falling
 out, body crumbling into sand). Our results confirm that, in addition to
 the sources of health data being researched lately (e.g., psychological
 conditions inferred from social media posts, physiological readings from
 commercial wearables), dream reports, if interpreted correctly, represent
 an understudied yet valuable source of people’s health experiences in the
 real world.
description: In this study, we compared textual expressions of health
 experiences in waking discussions and in dreams. To do so, we resorted to
 a state-of-the-art deep-learning method
 (https://dl.acm.org/doi/10.1145/3368555.3384467) capable of extracting
 mentions of any medical condition from text, and applied it to two main
 data sources: 1) A collection of 57M tweets posted in
 relation to COVID-19
 (https://publichealth.jmir.org/2020/2/e19273/) 2) A set
 of written dream reports that describe the dreams of 2,888 people during
 the pandemic (https://psycnet.apa.org/record/2020-71980-003)
 This dataset contains two main sources of data:
 A) The list of medical entities extracted from Twitter and dream
 reports, together with their frequency B) A
 co-occurrence graph of the 1,732 unique medical conditions we found in
 dream reports. In this graph, nodes represent all the medical conditions
 extracted from the dream reports. Two nodes are connected by an edge if
 they both appeared in the same dream report. Edges are weighted by the
 number of dreams in which the two conditions co-occurred.
description: See README.txt for details on the different fields