Comment Topic Evolution on a Cancer Institution’s Facebook Page

Chunlei Tang; Li Zhou; Joseph Plasek; Ronen Rozenblum; David Bates

doi:10.4338/ACI-2017-04-RA-0055

Applied Clinical Informatics, Table of Contents

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

Authors

Chunlei Tang

¹Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA

²Harvard Medical School, Boston, MA, USA

³Clinical and Quality Analysis, Partners HealthCare System, Boston, MA, USA
Li Zhou

¹Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA

²Harvard Medical School, Boston, MA, USA

⁴Clinical Informatics, Partners eCare, Partners HealthCare System, Boston, MA, USA
Joseph Plasek

¹Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA

⁵Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, USA
Ronen Rozenblum

¹Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA

²Harvard Medical School, Boston, MA, USA
David Bates

¹Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA

²Harvard Medical School, Boston, MA, USA

³Clinical and Quality Analysis, Partners HealthCare System, Boston, MA, USA

Abstract

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

Keywords

Patient Engagement - Patient Satisfaction - Data Mining - Social Media - Consumer Participation - Oncology Service - Hospital

Full Text

References

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