Evidence map›Paper›PMID 33028170›Full record

ArticleWomen's health (London, England)

Studying social media language changes associated with pregnancy status, trimester, and parity from medical records.

Sharath Chandra Guntuku, Jessica S Gaulton, Emily K Seltzer, David A Asch, Sindhu K Srinivas, Lyle H Ungar, Christina Mancheno, Elissa V Klinger, Raina M Merchant

Open access · goldAbstract read
In one paragraph

Article in Women's health (London, England). 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
0.4field-weighted citation impact, top 29% of its field
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, 3 citations in OpenAlex.

  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 at 2 institutions in 1 country.

Sharath Chandra GuntukuPenn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0002-2929-0035
Jessica S GaultonPenn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, USA.
Emily K SeltzerPenn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, USA.
David A AschPenn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, USA.
Sindhu K SrinivasDepartment of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Lyle H UngarPenn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, USA.
Christina ManchenoPenn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, USA.
Elissa V KlingerPenn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, USA.
Raina M MerchantPenn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, PA, USA.
University of Pennsylvania Health System · USUniversity of Pennsylvania · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We sought to evaluate whether there was variability in language used on social media across different time points of pregnancy (before, during, and after pregnancy, as well as by trimester and parity). Consenting patients shared access to their individual Facebook posts and electronic medical records. Random forest models trained on Facebook posts could differentiate first trimester of pregnancy from 3 months before pregnancy (F1 score = .63) and from a random 3-month time period (F1 score = .64). Posts during pregnancy were more likely to include themes about family (β = .22), food craving (β = .14), and date/times (β = .13), while posts 3 months prior to pregnancy included themes about social life (β = .30), sleep (β = .31), and curse words (β = .27), and 3 months post-pregnancy included themes of gratitude (β = .17), health appointments (β = .21), and religiosity (β = .18). Users who were pregnant for the first time were more likely to post about lack of sleep (β = .15), activities of daily living (β = .09), and communication (β = .08) compared with those who were pregnant after having a child who posted about others' birthdays (β = .16) and life events (.12). A better understanding about social media timelines can provide insight into lifestyle choices that are specific to pregnancy.

Indexed as

LanguageMedical RecordsParityPregnancy TrimestersSocial MediaActivities of Daily LivingAdultFemaleHumansLife StylePregnancyYoung AdultFacebooklanguagemachine learningpregnancysocial mediatrimester

Identifiers

PMID33028170
PMCPMC7549071
OpenAlexW3092563260

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.