Evidence map›Paper›PMID 32731600›Full record

ArticleInternational journal of environmental research and public health2020

Topic Modeling and User Network Analysis on Twitter during World Lupus Awareness Day.

Salvatore Pirri, Valentina Lorenzoni, Gianni Andreozzi, Marta Mosca, Giuseppe Turchetti

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Exploring the Factors Associated with Mental Health Attitude in China: A Structural Topic Modeling Approach.International journal of environmental research and public health · 2022
    Article
  3. 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.

Salvatore PirriInstitute of Management, Scuola Superiore Sant'Anna, 56127 Pisa, Italy.ORCID 0000-0002-8126-9663
Valentina LorenzoniInstitute of Management, Scuola Superiore Sant'Anna, 56127 Pisa, Italy.
Gianni AndreozziInstitute of Management, Scuola Superiore Sant'Anna, 56127 Pisa, Italy.
Marta MoscaRheumatology Unit, Department of Clinical and Experimental Medicine, Università di Pisa, 56126 Pisa, Italy.
Giuseppe TurchettiInstitute of Management, Scuola Superiore Sant'Anna, 56127 Pisa, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Twitter is increasingly used by individuals and organizations to broadcast their feelings and practices, providing access to samples of spontaneously expressed opinions on all sorts of themes. Social media offers an additional source of data to unlock information supporting new insights disclosures, particularly for public health purposes. Systemic lupus erythematosus (SLE) is a complex, systemic autoimmune disease that remains a major challenge in therapeutic diagnostic and treatment management. When supporting patients with such a complex disease, sharing information through social media can play an important role in creating better healthcare services. This study explores the nature of topics posted by users and organizations on Twitter during world Lupus day to extract latent topics that occur in tweet texts and to identify what information is most commonly discussed among users. We identified online influencers and opinion leaders who discussed different topics. During this analysis, we found two different types of influencers that employed different narratives about the communities they belong to. Therefore, this study identifies hidden information for healthcare decision-makers and provides a detailed model of the implications for healthcare organizations to detect, understand, and define hidden content behind large collections of text.

Indexed as

Social MediaHumansLupus Erythematosus, SystemicPublic Healthnetwork analysissocial mediasystemic lupus erythematosus (SLE)text analysistopic modelingTwitter

Identifiers

PMID32731600
PMCPMC7432829

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.