Evidence map›Paper›PMID 39106232›Full record

ArticlePloS one2024

Too much focus on your health might be bad for your health: Reddit user's communication style predicts their Long COVID likelihood.

Ludovica Segneri, Nandor Babina, Teresa Hammerschmidt, Andrea Fronzetti Colladon, Peter A Gloor

Abstract read
In one paragraph

Article in PloS one, 2024. 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

5 authors.

Ludovica SegneriDepartment of Engineering, University of Perugia, Perugia, Italy.ORCID 0000-0001-6952-0045
Nandor BabinaApplied Information and Data Science, University of Applied Sciences Lucerne, Lucerne, Switzerland.
Teresa HammerschmidtDepartment of Business IT, University of Bamberg, Bamberg, Germany.
Andrea Fronzetti ColladonDepartment of Engineering, University of Perugia, Perugia, Italy.
Peter A GloorMIT Center for Collective Intelligence, Cambridge, MA, United States of America.ORCID 0000-0002-7271-3224

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long Covid is a chronic disease that affects more than 65 million people worldwide, characterized by a wide range of persistent symptoms following a Covid-19 infection. Previous studies have investigated potential risk factors contributing to elevated vulnerability to Long Covid. However, research on the social traits associated with affected patients is scarce. This study introduces an innovative methodological approach that allows us to extract valuable insights directly from patients' voices. By analyzing written texts shared on social media platforms, we aim to collect information on the psychological aspects of people who report experiencing Long Covid. In particular, we collect texts of patients they wrote BEFORE they were afflicted with Long Covid. We examined the differences in communication style, sentiment, language complexity, and psychological factors of natural language use among the profiles of 6.107 Reddit users, distinguishing between those who claim they have never contracted Covid -19, those who claim to have had it, and those who claim to have experienced Long Covid symptoms. Our findings reveal that people in the Long Covid group frequently discussed health-related topics before the pandemic, indicating a greater focus on health-related concerns. Furthermore, they exhibited a more limited network of connections, lower linguistic complexity, and a greater propensity to employ emotionally charged expressions than the other groups. Using social media data, we can provide a unique opportunity to explore potential risk factors associated with Long Covid, starting from the patient's perspective.

Indexed as

CommunicationCOVID-19Social MediaAdultFemaleHumansMalePandemicsPost-Acute COVID-19 SyndromeRisk FactorsSARS-CoV-2

Identifiers

PMID39106232
PMCPMC11302924

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.