Dream Content Discovery from Reddit with an Unsupervised Mixed-Method Approach

TL;DR

This paper introduces a novel unsupervised mixed-method NLP approach to analyze large-scale dream reports from Reddit, identifying extensive themes and patterns, and tracking changes over time and major events.

Contribution

It presents the largest collection of dream topics to date and demonstrates a data-driven method that surpasses traditional scales in analyzing dream content.

Findings

Identified 217 topics grouped into 22 themes from 44,213 reports

Validated topics against Hall and van de Castle scale

Detected changes in dream themes during major events like COVID-19 and war

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.