Chunlei Tang1, Li Zhou, Joseph Plasek, Ronen Rozenblum, David Bates. 1. Chunlei Tang, PhD, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, 1620 Tremont Street BS-3, Boston, MA 02120, USA, Phone: (857) 600-0628, Email: ctang5@partners.org.
Abstract
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.
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.
Entities:
Keywords:
Consumer Participation; Data Mining; Hospital; Oncology Service; Patient Engagement; Patient Satisfaction; Social Media
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