Authors:
Kai O. Wong
1
;
Faith G. Davis
1
;
Osmar R. Zaïane
1
and
Yutaka Yasui
2
Affiliations:
1
University of Alberta, Canada
;
2
St. Jude Children’s Research Hospital and University of Alberta, United States
Keyword(s):
Cancer Screening, Social Media, Data Visualization, Sentiment Analysis, Spatial Analysis, Twitter.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Computational Intelligence
;
Data Analytics
;
Data Engineering
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Mining Multimedia Data
;
Mining Text and Semi-Structured Data
;
Soft Computing
;
Symbolic Systems
;
Visual Data Mining and Data Visualization
Abstract:
Whether or not U.S. women follow the recommended breast cancer screening guidelines is related to the perceived benefits and harms of the procedure. Twitter is a rich source of subjective information containing individuals’ sentiment towards public health interventions/technologies. Using our modified version of Hutto and Gilbert (2014) sentiment classifier, we described the temporal, geospatial, and thematic patterns of public sentiment towards breast cancer screening with 8 months of tweets (n=64,524) in the U.S. To examine how sentiment was related to screening uptake behaviour, we investigated and identified significant associations between breast cancer screening sentiment (via Twitter) and breast cancer screening uptake (via BRFSS) at the state level.