Journal article
Understanding Topics and Sentiment in an Online Cancer Survivor Community
Journal of the National Cancer Institute. Monographs, Vol.2013(47), pp.195-198
12/2013
DOI: 10.1093/jncimonographs/lgt025
PMID: 24395991
Abstract
Online cancer communities help members support one another, provide new perspectives about living with cancer, normalize experiences, and reduce isolation. The American Cancer Society’s 166000-member Cancer Survivors Network (CSN) is the largest online peer support community for cancer patients, survivors, and caregivers. Sentiment analysis and topic modeling were applied to CSN breast and colorectal cancer discussion posts from 2005 to 2010 to examine how sentiment change of thread initiators, a measure of social support, varies by discussion topic. The support provided in CSN is highest for medical, lifestyle, and treatment issues. Threads related to 1) treatments and side effects, surgery, mastectomy and reconstruction, and decision making for breast cancer, 2) lung scans, and 3) treatment drugs in colon cancer initiate with high negative sentiment and produce high average sentiment change. Using text mining tools to assess sentiment, sentiment change, and thread topics provides new insights that community managers can use to facilitate member interactions and enhance support outcomes.
Details
- Title: Subtitle
- Understanding Topics and Sentiment in an Online Cancer Survivor Community
- Creators
- Kenneth Portier - American Cancer SocietyGreta E Greer - American Cancer SocietyLior Rokach - Ben-Gurion University of the NegevNir Ofek - Ben-Gurion University of the NegevYafei Wang - Pennsylvania State UniversityPrakhar Biyani - Pennsylvania State UniversityMo Yu - Pennsylvania State UniversitySiddhartha Banerjee - Pennsylvania State UniversityKang Zhao - University of IowaPrasenjit Mitra - Pennsylvania State UniversityJohn Yen - Pennsylvania State University
- Resource Type
- Journal article
- Publication Details
- Journal of the National Cancer Institute. Monographs, Vol.2013(47), pp.195-198
- Publisher
- Oxford University Press
- DOI
- 10.1093/jncimonographs/lgt025
- PMID
- 24395991
- ISSN
- 1052-6773
- eISSN
- 1745-6614
- Language
- English
- Date published
- 12/2013
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380499502771
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