Finding meaning in social media: content-based social network analysis of QuitNet to identify new opportunities for health promotion.
Unhealthy behaviors increase individual health risks and are a socioeconomic burden. Harnessing social influence is perceived as fundamental for interventions to influence health-related behaviors. However, the mechanisms through which social influence occurs are poorly understood. Online social networks provide the opportunity to understand these mechanisms as they digitally archive communication between members. In this paper, we present a methodology for content-based social network analysis, combining qualitative coding, automated text analysis, and formal network analysis such that network structure is determined by the content of messages exchanged between members. We apply this approach to characterize the communication between members of QuitNet, an online social network for smoking cessation. Results indicate that the method identifies meaningful theme-based social sub-networks. Modeling social network data using this method can provide us with theme-specific insights such as the identities of opinion leaders and sub-community clusters. Implications for design of targeted social interventions are discussed.
National Center for Cognitive Informatics and Decision Making in Healthcare University of Texas School of Biomedical Informatics at Houston, TX, USA.
Sahiti
S
Myneni
Nathan K
NK
Cobb
Trevor
T
Cohen
D001185:Artificial Intelligence; D057225:Data Mining / Q000706:statistics & numerical data; D015438:Health Behavior; D058015:Health Communication / Q000379:methods*; D006293:Health Promotion / Q000379:methods*; D062527:Meaningful Use; D009323:Natural Language Processing; D016540:Smoking Cessation; D061108:Social Media / Q000706:statistics & numerical data*; D012944:Social Support