Programmatic Theme: Data Science

Abstract: Palliative care is a specialized service with proven efficacy in improving patients’ quality-of-life. Nevertheless, lack of awareness and misunderstanding limits its adoption. Research is urgently needed to understand the determinants (e.g., knowledge) related to its adoption. Traditionally, these determinants are measured with questionnaires. In this study, we explored Twitter to reveal these determinants guided by the Integrated Behavioral Model. A secondary goal is to assess the feasibility of extracting user demographics from Twitter data—a significant shortcoming in existing studies that limits our ability to explore more fine-grained research questions (e.g., gender difference). Thus, we collected, preprocessed, and geocoded palliative care-related tweets from 2013 to 2019 and then built classifiers to: 1) categorize tweets into promotional vs. consumer discussions, and 2) extract user gender. Using topic modeling, we explored whether the topics learned from tweets are comparable to responses of palliative care-related questions in the Health Information National Trends Survey.

Learning Objective: RQ1. What are the commonly discussed topics in promotional information and laypeople discussions on Twitter? Are consumers’ palliative care discussions on Twitter affected by promotional information? (i.e., through assessing the correlations between promotional palliative care-related information and consumers’ discussions in terms of topic distributions)

RQ2. Can the learned topics be mapped to the constructs in the IBM? If so, are the geographic distributions of the learned topics comparable to the determinants measured from HINTS survey?

RQ3. Can we extract user attributes with an initial focus on gender from laypeople’s Twitter postings? If so, are the geographic distributions of the learned topics comparable to the determinants measured from HINTS survey stratified by gender?

Authors:

Yunpeng Zhao (Presenter)
University of Florida

Hansi Zhang, University of Florida
JINHAI HUO, University of Florida
YI GUO, University of Florida
Yonghui Wu, University of Florida
Jiang Bian, University of Florida