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
Date Written: May 1, 2018
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
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