The Effect of Photoperiod on the Mood of Reddit Users
Bibliographic record
Abstract
Research into the seasonality of mood has long been stymied by a lack of data, in part due to the prohibitive cost of traditional data collection and the tendency for data to be highly localized. Recent work using social media data has evinced the utility of psycholinguistic features in tracking mood and mental illness, but Twitter data, which are nonanonymous and short-form by design, have almost exclusively been the subject of analysis. In this article, we present a novel corpus within this field of study, comments from the social network Reddit, which does not suffer from these potential limitations. We find that although there are no notable changes in mood in the entire population over the course of a year, a small cohort is acutely sensitive to changes in the relative day length (i.e., the relative photoperiod). Our findings corroborate the phase shift hypothesis, which is the prevailing theory for the seasonality of mood. We also demonstrate the viability of the Reddit comments corpus for studies in mood and, more broadly, mental health.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".