  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.
