Evidence map›Paper›PMID 35615305›Full record

ArticleProceedings of the ACM on human-computer interaction2021

A Social Media Study on Demographic Differences in Perceived Job Satisfaction.

Koustuv Saha, Asra Yousuf, Louis Hickman, Pranshu Gupta, Louis Tay, Munmun DE Choudhury

Abstract read
In one paragraph

Article in Proceedings of the ACM on human-computer interaction, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
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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

6 authors.

Koustuv SahaGeorgia Institute of Technology, USA.
Asra YousufGeorgia Institute of Technology, USA.
Louis HickmanPurdue University, USA.
Pranshu GuptaGeorgia Institute of Technology, USA.
Louis TayPurdue University, USA.
Munmun DE ChoudhuryGeorgia Institute of Technology, USA.

Funding

Leveraging Social Media Data and Machine Learning to Optimize Treatment Paradigms for Youth with SchizophreniaR01MH117172 · NIMH · GEORGIA INSTITUTE OF TECHNOLOGY · PI DE CHOUDHURY, MUNMUN · 2019 to 2022
$2.6M
NIMH NIH HHS R01 MH117172
6 · The paper itself

Abstract

Effective ways to measure employee job satisfaction are fraught with problems of scale, misrepresentation, and timeliness. Current methodologies are limited in capturing subjective differences in expectations, needs, and values at work, and they do not lay emphasis on demographic differences, which may impact people's perceptions of job satisfaction. This study proposes an approach to assess job satisfaction by leveraging large-scale social media data. Starting with an initial Twitter dataset of 1.5M posts, we examine two facets of job satisfaction,

Indexed as

demographic differencesgendergeographyjob satisfactionmacroeconomic constructsracesexsocial mediaTwitterworkplace

Identifiers

PMID35615305
PMCPMC9128314

What OpenQuestion holds

Textmetadata
LicenceTDM
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.