Dream Content Discovery from Reddit with an Unsupervised Mixed-Method Approach
Anubhab Das, Sanja Šćepanović, Luca Maria Aiello, Remington Mallett, Deirdre Barrett, Daniele Quercia
arXiv (Cornell University) July 9, 2023 preprint DOI: 10.48550/arxiv.2307.04167 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study using natural language processing on existing text data |
|---|---|
| Sample size | 44,213 |
| Population | Dream reports from Reddit's r/Dreams subreddit |
| Topics | Dreaming |
| Keywords | Dream Scale ratio Big data Content measure theory Data science Artificial intelligence Cartography Data mining |
| Citations | 1 |
| Key findings | The method identified 217 dream topics grouped into 22 larger themes from 44,213 dream reports, validated against the Hall and van de Castle scale, and can detect patterns in different dream types and changes over time. |
Abstract
Dreaming is a fundamental but not fully understood part of human experience that can shed light on our thought patterns. Traditional dream analysis practices, while popular and aided by over 130 unique scales and rating systems, have limitations. Mostly based on retrospective surveys or lab studies, they struggle to be applied on a large scale or to show the importance and connections between different dream themes. To overcome these issues, we developed a new, data-driven mixed-method approach for identifying topics in free-form dream reports through natural language processing. We tested this method on 44,213 dream reports from Reddit's r/Dreams subreddit, where we found 217 topics, grouped into 22 larger themes: the most extensive collection of dream topics to date. We validated our topics by comparing it to the widely-used Hall and van de Castle scale. Going beyond traditional scales, our method can find unique patterns in different dream types (like nightmares or recurring dreams), understand topic importance and connections, and observe changes in collective dream experiences over time and around major events, like the COVID-19 pandemic and the recent Russo-Ukrainian war. We envision that the applications of our method will provide valuable insights into the intricate nature of dreaming.
Comparable studies
Other observational and cohort studies on dreaming, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Lucid Dreaming: a State of Consciousness with Features of Both Waking and Non-Lucid Dreaming Student volunteers trained to become lucid and signal lucidity with horizontal eye... | 2009 | Observational study | n = 6 |
| Nightmare Prevalence, Nightmare Distress, and Self-Reported Psychological Disturbance Young adults with a mean age of 20 years | 2002 | Prospective observational study | n = 116 |
| Long-term effects of extreme situational stress on sleep and dreaming Survivors of traumatic events who complained of sleep disturbances, compared with... | 1987 | Case-control study | n = 11 |
| The relationship of nightmare frequency and nightmare distress to well‐being Adults who reported dreams | 2004 | Observational cohort | n = 147 |
| Dreaming in NREM Sleep: A High-Density EEG Study of Slow Waves and Spindles Healthy human subjects | 2018 | Observational cohort | n = 14 |