Analysis of cross-platform health communication with a network approach.

Abstract

Online health communities (OHCs) provide a platform for patients and those related to share and communicate, making complex medical information more digestible and actionable. Health communication within OHCs can be impacted by other information sources. This study examines cross-platform health communication by mining Breastcancer.org (the largest online breast cancer community) and Twitter (now X). Early analyses of OHCs, Twitter, and other online platforms often adopt simple measures like word frequency, and more recent research has shifted towards word co-occurrence network analysis. Relatively, cross-platform communication analysis is limited, and the adopted techniques have drawbacks. We propose a new cross-platform communication model that collectively analyzes word co-occurrence networks and word frequency vectors. Here, the former describe the structural contents of health communication, and the latter describe the volumes. This model offers a nuanced perspective, accommodates temporal variations, and is examined for its theoretical and numerical properties. Collected from January 2010 to December 2020, the analyzed data contains over 1 395 000 tweets and 517 000 posts. Our analysis suggests that the Twitter's topics on breast cancer significantly impact the contents and volumes in the OHC. Distinct time phases are observed, with notable peaks during 2012-2013 and 2015-2018. This study can provide a venue for better understanding health communication and new insights into two highly important online platforms.