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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".