Go to YouTube and See Me Tomorrow: The Role of Social Media in Managing Chronic Conditions

53 Pages Posted: 30 Oct 2017 Last revised: 7 May 2018

Xiao Liu

University of Utah

Bin Zhang

University of Arizona

Anjana Susarla

Michigan State University - The Eli Broad College of Business and The Eli Broad Graduate School of Management

Rema Padman

School of Information Systems and Management, The H. John Heinz III College

Date Written: May 1, 2018

Abstract

Video sharing social media sites, such as YouTube, that host videos providing information on the
pathogenesis, diagnosis, treatments, and prevention of various conditions can be an effective way
to understand medical knowledge and in managing chronic conditions through patient self-care.
However, due to the heterogeneity of the content quality and content helpfulness on visual social
media, healthcare providers and government agencies have expressed concerns about the quality
and reliability of such information. There have been relatively few studies that have identified
interventions to increase the ease with which patients can find helpful health information. We
propose an interdisciplinary lens that synthesizes deep learning methods with themes emphasized
in Information Systems (IS) research and research on healthcare informatics. Using a
bidirectional long short-term memory (BLSTM) method, we extract medical terminology from
videos. We annotate videos using inputs from domain experts and build a logistic regression
based classifier to categorize videos based on whether they encode a high degree of medical
knowledge or not. We identify distinct types of user engagement with videos on YouTube using a
principal components analysis (PCA) approach: user dissonance, popularity based engagement,
and relevance based engagement. We find that medical knowledge encoded in videos matters to
patient engagement; however, popularity-based indicators of engagement indicate that videos that
score high on medical knowledge encoded in videos, are actually less popular than those that are
not. We conduct robustness checks using a convolutional neural network (CNN) to detect the
presence of medical objects in a video. We find that medical terminology embedded in textual
data is more salient to an assessment of medical knowledge encoded in a video, rather than image
analytics. Our results suggest that healthcare practitioners and policymakers need a nuanced
understanding of how users engage with medical knowledge in video format, which has
implications for the role of videos and visual social media in bridging the health literacy gap and
in enabling self-care of chronic conditions.

Keywords: visual social media, healthcare informatics, patient self-care, chronic diseases, deep learning, Bidirectional Long Short-term Memory (BLSTM), Convolutional Neural Network (CNN)

JEL Classification: C31, C20, C39

Suggested Citation

Liu, Xiao and Zhang, Bin and Susarla, Anjana and Padman, Rema, Go to YouTube and See Me Tomorrow: The Role of Social Media in Managing Chronic Conditions (May 1, 2018). Available at SSRN: https://ssrn.com/abstract=3061149 or http://dx.doi.org/10.2139/ssrn.3061149

Xiao Liu

University of Utah ( email )

1645 E. Campus Center
Salt Lake City, UT 84112
United States

Bin Zhang

University of Arizona ( email )

1130 E. Helen St
RM430Z
Tucson, AZ 85721
United States
(520) 626-9239 (Phone)

Anjana Susarla (Contact Author)

Michigan State University - The Eli Broad College of Business and The Eli Broad Graduate School of Management ( email )

East Lansing, MI 48824-1121
United States

Rema Padman

School of Information Systems and Management, The H. John Heinz III College ( email )

Pittsburgh, PA 15213-3890
United States

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