0662 Digital sleep-health surveillance: Population nightmare frequency is observable in social media
Bibliographic record
Abstract
Abstract Introduction Nightmares are a critical component of multidimensional sleep health. Frequent nightmare recallers suffer reduced sleep length, depressed mood, and are more likely to be diagnosed with a psychiatric disorder in the future. Reliable population estimates of nightmare frequency are difficult to obtain and often restricted to rigid timepoints (e.g., once per year). Thus, there is a need for low-cost methods to track population levels of nightmare frequency and their dynamic changes over time. In the current study, we tracked dream content and nightmare frequency using a popular new approach – digital health surveillance – that analyzes language of public and freely-accessible social media posts to track population characteristics. Methods To evaluate whether social media could detect known changes in population sleep health, we extracted posts from r/Dreaming, a popular subreddit dedicated to dream sharing. Prior survey studies have shown a reliable increase in nightmare frequency during the first wave of the COVID-19 pandemic. Thus, we quantified the amount of nightmares posted on Reddit surrounding the World Health Organization’s (WHO) declaration of a COVID-19 as a global pandemic. Nightmares were identified using a word-search algorithm that identified nightmare-related words in a post title. Nightmare frequency was compared before and after the WHO declaration using a chi-squared analysis. Results The percentage of dreams posted on Reddit that were identified as nightmares was higher after the WHO declaration than before (p < .05). Additionally, the amount of anxiety in all dreams posted on Reddit was higher after the WHO declaration than before (p < .05). Weekly change in dream anxiety was positively correlated with the percentage of COVID-19 news headlines (p < .05). Conclusion We observed an increase in nightmares shared on Reddit immediately following the WHO’s declaration of COVID-19 as a global pandemic. This novel approach to tracking nightmares might offer the field of sleep medicine a low-cost and real-time system for monitoring population sleep health. Other recent work suggests this method might be viable for tracking other components of sleep health. Support (if any)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".