Evidence map›Paper›PMID 28793981›Full record

ArticleJMIR public health and surveillance2017

Attitudes of Crohn's Disease Patients: Infodemiology Case Study and Sentiment Analysis of Facebook and Twitter Posts.

Marco Roccetti, Gustavo Marfia, Paola Salomoni, Catia Prandi, Rocco Maurizio Zagari, Faustine Linda Gningaye Kengni, Franco Bazzoli, Marco Montagnani

Open access · goldAbstract read
In one paragraph

Article in JMIR public health and surveillance, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 3 pooled it
20.1field-weighted citation impact, top 1% 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

31 citing papers in PubMed, 3 syntheses or guidelines pooled it, 63 citations in OpenAlex.

  1. Pooled it
  2. A systematic literature review of machine learning in online personal health data.Journal of the American Medical Informatics Association : JAMIA · 2019
    Pooled it
  3. Pooled it
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Automatic Diagnosis of Mental Healthcare Information Actionability: Developing Binary Classifiers.International journal of environmental research and public health · 2021
    Article
  20. 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

8 authors at 2 institutions in 1 country.

Marco Roccetti *Department of Computer Science and Engineering, University of Bologna, Bologna, Italy.ORCID http://orcid.org/0000-0003-1264-8595
Gustavo Marfia *Department for Life Quality Studies, University of Bologna, Rimini, Italy.ORCID http://orcid.org/0000-0003-3058-8004
Paola Salomoni *Department of Computer Science and Engineering, University of Bologna, Bologna, Italy.ORCID http://orcid.org/0000-0002-1717-1901
Catia Prandi *Madeira Interactive Technologies Institute, Funchal, Portugal.ORCID http://orcid.org/0000-0002-5566-2269
Rocco Maurizio Zagari *Gastroenterology Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.ORCID http://orcid.org/0000-0001-9949-8619
Faustine Linda Gningaye Kengni *Gastroenterology Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.ORCID http://orcid.org/0000-0002-6796-9460
Franco Bazzoli *Gastroenterology Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.ORCID http://orcid.org/0000-0002-9329-6888
Marco Montagnani *Gastroenterology Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.ORCID http://orcid.org/0000-0003-0103-0327
University of Bologna · ITInstituto de Tecnologias Interativas

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundData concerning patients originates from a variety of sources on social media.

objectiveThe aim of this study was to show how methodologies borrowed from different areas including computer science, econometrics, statistics, data mining, and sociology may be used to analyze Facebook data to investigate the patients' perspectives on a given medical prescription.

methodsTo shed light on patients' behavior and concerns, we focused on Crohn's disease, a chronic inflammatory bowel disease, and the specific therapy with the biological drug Infliximab. To gain information from the basin of big data, we analyzed Facebook posts in the time frame from October 2011 to August 2015. We selected posts from patients affected by Crohn's disease who were experiencing or had previously been treated with the monoclonal antibody drug Infliximab. The selected posts underwent further characterization and sentiment analysis. Finally, an ethnographic review was carried out by experts from different scientific research fields (eg, computer science vs gastroenterology) and by a software system running a sentiment analysis tool. The patient feeling toward the Infliximab treatment was classified as positive, neutral, or negative, and the results from computer science, gastroenterologist, and software tool were compared using the square weighted Cohen's kappa coefficient method.

resultsThe first automatic selection process returned 56,000 Facebook posts, 261 of which exhibited a patient opinion concerning Infliximab. The ethnographic analysis of these 261 selected posts gave similar results, with an interrater agreement between the computer science and gastroenterology experts amounting to 87.3% (228/261), a substantial agreement according to the square weighted Cohen's kappa coefficient method (w2K=0.6470). A positive, neutral, and negative feeling was attributed to 36%, 27%, and 37% of posts by the computer science expert and 38%, 30%, and 32% by the gastroenterologist, respectively. Only a slight agreement was found between the experts' opinion and the software tool.

conclusionsWe show how data posted on Facebook by Crohn's disease patients are a useful dataset to understand the patient's perspective on the specific treatment with Infliximab. The genuine, nonmedically influenced patients' opinion obtained from Facebook pages can be easily reviewed by experts from different research backgrounds, with a substantial agreement on the classification of patients' sentiment. The described method allows a fast collection of big amounts of data, which can be easily analyzed to gain insight into the patients' perspective on a specific medical therapy.

Indexed as

consumer health informationhealth information systemspublic health informaticssocial networking

Identifiers

PMID28793981
PMCPMC5569247
OpenAlexW2745005270

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.