Appl Clin Inform 2017; 08(03): 854-865
DOI: 10.4338/ACI-2017-04-RA-0055
Research Article
Schattauer GmbH

Comment Topic Evolution on a Cancer Institution’s Facebook Page

Chunlei Tang
1  Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA
2  Harvard Medical School, Boston, MA, USA
3  Clinical and Quality Analysis, Partners HealthCare System, Boston, MA, USA
,
Li Zhou
1  Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA
2  Harvard Medical School, Boston, MA, USA
4  Clinical Informatics, Partners eCare, Partners HealthCare System, Boston, MA, USA
,
Joseph Plasek
1  Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA
5  Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, USA
,
Ronen Rozenblum
1  Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA
2  Harvard Medical School, Boston, MA, USA
,
David Bates
1  Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA
2  Harvard Medical School, Boston, MA, USA
3  Clinical and Quality Analysis, Partners HealthCare System, Boston, MA, USA
› Author Affiliations
Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-forprofit sectors.
Further Information

Publication History

received: 06 April 2017

accepted in revised form: 25 June 2017

Publication Date:
20 December 2017 (online)

Summary

Objectives: Our goal was to identify and track the evolution of the topics discussed in free-text comments on a cancer institution’s social media page.

Methods: We 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.

Results: A 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).

Conclusions: A 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.

Citation: Tang C, Zhou L, Plasek J, Rozenblum R, Bates D. Comment Topic Evolution on a Cancer Institution’s Facebook Page. Appl Clin Inform 2017; 8: 854–865 https://doi.org/10.4338/ACI-2017-04-RA-0055

Clinical Relevance Statement

The user-generated data came from online comments on a healthcare organizations social media platform, and this type of data is associated with clinical outcomes [2–12]. Emotional and psychological distress is common among loved ones of cancer patients, who sometimes report more severe mental health issues than the patients themselves. Patients’ family and friends are active users of the DFCI social media page and these users tended to express a desire for support (emotional, instrumental, and social) and hope, rather than in-depth information-based content about treatments.


Human Subjects Protection

This study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was reviewed by approved by the Partners and DFCI Institutional Review Boards (IRB).


Contributions

All authors provided substantial contribution to the conception and design of this work, its data analysis and interpretation, and helped draft and revise the manuscript. All the authors are accountable for the integrity of this work.