Evidence map›Paper›PMID 36649054›Full record

ArticleJMIR formative research2023

Estimation of Bedtimes of Reddit Users: Integrated Analysis of Time Stamps and Surveys.

William U Meyerson, Sarah K Fineberg, Ye Kyung Song, Adam Faber, Garrett Ash, Fernanda C Andrade, Philip Corlett, Mark B Gerstein, Rick H Hoyle

Abstract read
In one paragraph

Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

William U MeyersonDepartment of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC, United States.ORCID https://orcid.org/0000-0003-1757-1451
Sarah K FinebergDepartment of Psychiatry, Yale University, New Haven, CT, United States.ORCID https://orcid.org/0000-0001-6024-6721
Ye Kyung SongDepartment of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC, United States.ORCID https://orcid.org/0000-0002-6604-6031
Adam FaberDurham Veterans Affairs Healthcare System, Durham, NC, United States.ORCID https://orcid.org/0000-0002-5761-7242
Garrett AshCenter for Medical Informatics, Yale University, New Haven, CT, United States.ORCID https://orcid.org/0000-0002-8655-7525
Fernanda C AndradeDepartment of Psychology and Neuroscience, Duke University, Durham, NC, United States.ORCID https://orcid.org/0000-0002-4862-3544
Philip CorlettDepartment of Psychiatry, Yale University, New Haven, CT, United States.ORCID https://orcid.org/0000-0002-5368-1992
Mark B GersteinDepartment of Molecular Biochemistry & Biophysics, Yale University, New Haven, CT, United States.ORCID https://orcid.org/0000-0002-9746-3719
Rick H HoyleDepartment of Psychology and Neuroscience, Duke University, Durham, NC, United States.ORCID https://orcid.org/0000-0003-0900-2814

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
NCATS NIH HHS UL1 TR001863
6 · The paper itself

Abstract

backgroundIndividuals with later bedtimes have an increased risk of difficulties with mood and substances. To investigate the causes and consequences of late bedtimes and other sleep patterns, researchers are exploring social media as a data source. Pioneering studies inferred sleep patterns directly from social media data. While innovative, these efforts are variously unscalable, context dependent, confined to specific sleep parameters, or rest on untested assumptions, and none of the reviewed studies apply to the popular Reddit platform or release software to the research community.

objectiveThis study builds on this prior work. We estimate the bedtimes of Reddit users from the times tamps of their posts, test inference validity against survey data, and release our model as an R package (The R Foundation).

methodsWe included 159 sufficiently active Reddit users with known time zones and known, nonanomalous bedtimes, together with the time stamps of their 2.1 million posts. The model's form was chosen by visualizing the aggregate distribution of the timing of users' posts relative to their reported bedtimes. The chosen model represents a user's frequency of Reddit posting by time of day, with a flat portion before bedtime and a quadratic depletion that begins near the user's bedtime, with parameters fitted to the data. This model estimates the bedtimes of individual Reddit users from the time stamps of their posts. Model performance is assessed through k-fold cross-validation. We then apply the model to estimate the bedtimes of 51,372 sufficiently active, nonbot Reddit users with known time zones from the time stamps of their 140 million posts.

resultsThe Pearson correlation between expected and observed Reddit posting frequencies in our model was 0.997 on aggregate data. On average, posting starts declining 45 minutes before bedtime, reaches a nadir 4.75 hours after bedtime that is 87% lower than the daytime rate, and returns to baseline 10.25 hours after bedtime. The Pearson correlation between inferred and reported bedtimes for individual users was 0.61 (P<.001). In 90 of 159 cases (56.6%), our estimate was within 1 hour of the reported bedtime; 128 cases (80.5%) were within 2 hours. There was equivalent accuracy in hold-out sets versus training sets of k-fold cross-validation, arguing against overfitting. The model was more accurate than a random forest approach.

conclusionsWe uncovered a simple, reproducible relationship between Reddit users' reported bedtimes and the time of day when high daytime posting rates transition to low nighttime posting rates. We captured this relationship in a model that estimates users' bedtimes from the time stamps of their posts. Limitations include applicability only to users who post frequently, the requirement for time zone data, and limits on generalizability. Nonetheless, it is a step forward for inferring the sleep parameters of social media users passively at scale. Our model and precomputed estimated bedtimes of 50,000 Reddit users are freely available.

Indexed as

bedtimemodelobservational modelparametric modelsRedditresearch toolsleepsleep patternssocial mediausage data

Identifiers

PMID36649054
PMCPMC9890352

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.