Understanding the Public’s Emotions about Cancer: Analysis of Social Media Data

Seul Ki Park, Hyeoun-Ae Park and Jooyun Lee
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Seul Ki Park: College of Nursing and Research Institute of Nursing Science, Seoul National University, Seoul 03080, Korea
Hyeoun-Ae Park: College of Nursing and Research Institute of Nursing Science, Seoul National University, Seoul 03080, Korea
Jooyun Lee: College of Nursing, Gachon University, Incheon 21936, Korea

IJERPH, 2020, vol. 17, issue 19, 1-14

Abstract: Cancer survivors suffer from emotional distress, which varies depending on several factors. However, existing emotion management programs are insufficient and do not take into consideration all of the factors. Social media provides a platform for understanding the emotions of the public. The aim of this study was to explore the relationship between the public’s emotions about cancer and factors affecting emotions using social media data. We used 321,339 posts on cancer and emotions relating to cancer extracted from 22 social media channels between 1 January 2014, and 30 June 2017. The factors affecting emotions were analyzed using association rule mining and social network analysis. Hope/gratitude was the most frequently mentioned emotion group on social media followed by fear/anxiety/overwhelmed, sadness/depression/loneliness/guilt, and anger/denial. Acute survival stage, treatment method, and breast cancer were associated with hope/gratitude. Early stage, gastrointestinal problems, fatigue/pain/fever, and pancreatic cancer were associated with fear/anxiety/overwhelmed. Surgery, hair loss/skin problems, and fatigue/pain/fever were associated with sadness/depression/loneliness/guilt. Acute survival stage and hair loss/skin problems were associated with anger/denial. We found that emotions concerning cancer differed depending on the cancer type, cancer stage, survival stage, treatment, and symptoms. These findings could guide the development of tailored emotional management programs for cancer survivors that meet the public’s needs more effectively.

Keywords: social media; emotional analysis; cancer; association rule mining; social network analysis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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