Utilizing Big Data and Twitter to Discover Emergent Online Communities of Cannabis Users
Year: 2017
Type: article
Abstract: Large shifts in medical, recreational, and illicit cannabis consumption in the United States have implications for personalizing treatment and prevention programs to a wide variety of populations. As such, considerable research has investigated clinical presentations of cannabis users in clinical and population-based samples. Studies leveraging big data, social media, and social network analysis have emerged as a promising mechanism to generate timely insights that can inform treatment and prevention research. This study extends a novel method called stochastic block modeling to derive communities of cannabis consumers as part of a complex social network on Twitter. A set of examples illustrate how this method can ascertain candidate samples of medical, recreational, and illicit cannabis users. Implications for research planning, intervention design, and public health surveillance are discussed.
Authors: Peter Baumgartner, Nicholas Peiper
Institution: RTI International
Language: English
Subfield: Pharmacology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,650
OpenAlex ID: W2623583491