ArticleApplied clinical informatics2017
Comment Topic Evolution on a Cancer Institution's Facebook Page.
Article in Applied clinical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- A Structured Social Media Health Support Program after Bariatric Surgery.Applied clinical informatics · 2024Observational
- Scalable Feature Engineering from Electronic Free Text Notes to Supplement Confounding Adjustment of Claims-Based Pharmacoepidemiologic Studies.Clinical pharmacology and therapeutics · 2023Article
- Examining Analytic Practices in Latent Dirichlet Allocation Within Psychological Science: Scoping Review.Journal of medical Internet research · 2022Article
- Facebook Users' Interactions, Organic Reach, and Engagement in a Smoking Cessation Intervention: Content Analysis.Journal of medical Internet research · 2021Article
- A Collaborative Framework Based for Semantic Patients-Behavior Analysis and Highlight Topics Discovery of Alcoholic Beverages in Online Healthcare Forums.Journal of medical systems · 2020Article
- Characterizing Artificial Intelligence Applications in Cancer Research: A Latent Dirichlet Allocation Analysis.JMIR medical informatics · 2019Article
- Medical Information Extraction Model for User-generated Content.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2019Article
- Cancer Informatics in 2017: A New Beginning and a Bright Future.Yearbook of medical informatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesOur goal was to identify and track the evolution of the topics discussed in free-text comments on a cancer institution's social media page.
methodsWe utilized the Latent Dirichlet Allocation model to extract ten topics from free-text comments on a cancer research institution's Facebook™ page between January 1, 2009, and June 30, 2014. We calculated Pearson correlation coefficients between the comment categories to demonstrate topic intensity evolution.
resultsA total of 4,335 comments were included in this study, from which ten topics were identified: greetings (17.3%), comments about the cancer institution (16.7%), blessings (10.9%), time (10.7%), treatment (9.3%), expressions of optimism (7.9%), tumor (7.5%), father figure (6.3%), and other family members & friends (8.2%), leaving 5.1% of comments unclassified. The comment distributions reveal an overall increasing trend during the study period. We discovered a strong positive correlation between greetings and other family members & friends (r=0.88; p<0.001), a positive correlation between blessings and the cancer institution (r=0.65; p<0.05), and a negative correlation between blessings and greetings (r=-0.70; p<0.05).
conclusionsA cancer institution's social media platform can provide emotional support to patients and family members. Topic analysis may help institutions better identify and support the needs (emotional, instrumental, and social) of their community and influence their social media strategy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.