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.


 
  • References

  • 1 Assessment of Patients’ Experience of Cancer Care (APECC) Study. National Cancer Institute; 2017 [cited 3/27/2017 3/27/2017]; Available from: https://healthcaredelivery.cancer.gov/apecc
  • 2 Greaves F, Pape UJ, King D, Darzi A, Majeed A, Wachter RM, Millett C. Associations between Web-based patient ratings and objective measures of hospital quality. Arch Intern Med 2012; 172 (05) 435-6.
  • 3 Bardach NS, Asteria-Penaloza R, Boscardin WJ, Dudley RA. The relationship between commercial web-site ratings and traditional hospital performance measures in the USA. BMJ Qual Saf 2013; 22 (03) 194-202.
  • 4 Greaves F, Millett C. Consistently Increasing Numbers of Online Ratings of Healthcare in England. J Med Internet Res 2012; 14 (03) e94.
  • 5 Munson AS, Cavusoglu H, Frisch L, Fels S. Sociotechnical Challenges and Progress in Using Social Media for Health. J Med Internet Res 2013; 15 (10) e226.
  • 6 Greaves F, Ramirez-Cano D, Millett C, Darzi A, Donaldson L. Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online. J Med Internet Res 2013; 15 (11) e239.
  • 7 King D, Ramirez-Cano D, Greaves F, Vlaev I, Beales S, Darzi A. Twitter and the health reforms in the English National Health Service. Health policy (Amsterdam, Netherlands) 2013; 110 2–3 291-7.
  • 8 Greaves F, Ramirez-Cano D, Millett C, Darzi A, Donaldson L. Harnessing the cloud of patient experience: using social media to detect poor quality healthcare. BMJ Quality & Safety 2013; 22 (03) 251-5.
  • 9 Rozenblum R, Greaves F, Bates DW. The role of social media around patient experience and engagement. BMJ Qual Saf. 2017 Apr 20.
  • 10 Rozenblum R, Bates DW. Patient-centred healthcare, social media and the internet: the perfect storm?. BMJ Qual Saf 2013; 22: 183-6.
  • 11 Hawkins JB, Brownstein JS, Tuli G. et al. Measuring patient-perceived quality of care in US hospitals using Twitter. BMJ Qual Saf 2016; 25: 404-13.
  • 12 Rozenblum R, Miller P, Pearson D, Marielli A. Patient-centered healthcare, patient engagement and health information technology: the perfect storm. In: Grando M, Rozenblum R, Bates DW. eds. Information Technology for Patient Empowerment in Healthcare. 1st ed. Berlin: Walter de Gruyter Inc; 2015: 3-22.
  • 13 Gage-Bouchard EA, LaValley S, Mollica M, Beaupin LK. Cancer Communication on Social Media: Examining How Cancer Caregivers Use Facebook for Cancer-Related Communication. Cancer nursing 2017; Publish Ahead of Print.
  • 14 Andre Gohr AH, Rene Schult, Myra Spiliopoulou. Topic evolution in a stream of documents. In SDM 2009; 859-72.
  • 15 Kalyanam J, Mantrach A, Saez-Trumper D, Vahabi H, Lanckriet G. Leveraging Social Context for Modeling Topic Evolution. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Sydney, NSW, Australia. 2783319: ACM 2015: 517-26.
  • 16 Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. J Mach Learn Res 2003; 3: 993-1022.
  • 17 Liang S, Yilmaz E, Kanoulas E. Dynamic Clustering of Streaming Short Documents. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; San Francisco, California, USA. 2939748: ACM 2016: 995-1004.
  • 18 Amoualian H, Clausel M, Gaussier É, Amini M-R. editors. Streaming-LDA: A Copula-based Approach to Modeling Topic Dependencies in Document Streams. KDD 2016
  • 19 Sarioglu E, Yadav K, Choi H-A. editors. Topic Modeling Based Classification of Clinical Reports. ACL (Student Research Workshop) 2013.
  • 20 Arnold CW, El-Saden SM, Bui AA, Taira R. editors. Clinical case-based retrieval using latent topic analysis. AMIA Annual Symposium Proceedings; 2010: American Medical Informatics Association.
  • 21 Howes C, Purver M, Mccabe R. editors. Investigating Topic Modelling for Therapy Dialogue Analysis. Proceedings IWCS Workshop on Computational Semantics in Clinical Text (CSCT) 2013
  • 22 Brody S, Elhadad N. Detecting Salient Aspects in Online Reviews of Health Providers. AMIA Annual Symposium Proceedings 2010; 2010: 202-6.
  • 23 Gao GG, McCullough SJ, Agarwal R, Jha KA. A Changing Landscape of Physician Quality Reporting: Analysis of Patients? Online Ratings of Their Physicians Over a 5-Year Period. J Med Internet Res 2012; 14 (01) e38.
  • 24 Wang T, Huang Z, Gan C. On mining latent topics from healthcare chat logs. J Biomed Inform 2016; 61: 247-59.
  • 25 Yang FC, Lee AJ, Kuo SC. Mining Health Social Media with Sentiment Analysis. J Med Syst 2016; 40 (11) 236.
  • 26 Eysenbach G, Till JE. Ethical issues in qualitative research on internet communities. BMJ 2001; 323 7321 1103-5.
  • 27 Fox C. A stop list for general text. SIGIR Forum 1989; 24 1–2 19-21.
  • 28 Mei Q, Zhai C. Discovering evolutionary theme patterns from text: an exploration of temporal text mining. Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. Chicago, Illinois, USA. 1081895: ACM 2005: 198-207.
  • 29 Zhao W, Chen JJ, Perkins R, Liu Z, Ge W, Ding Y, Zou W. A heuristic approach to determine an appropriate number of topics in topic modeling. BMC Bioinformatics 2015; 16 (Suppl. 13) S8-S.
  • 30 Benesty J, Chen J, Huang Y, Cohen I. Pearson correlation coefficient. Noise reduction in speech processing: Springer 2009; 1-4.