Utilizing Big Data and Twitter to Discover Emergent Online Communities of Cannabis Users

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.

Studying Cannabis Use Behaviors With Facebook and Web Surveys: Methods and Insights

“…A growing body of literature has demonstrated that digital trace data [ 44 ] (ie, records of naturalistic digital behavior and communication) can be used to study cannabis use. For example, Twitter has been used to study new forms of cannabis administration such as dabbing [ 75 , 76 ], vaping [ 77 ], and edibles [ 78 , 79 ]; perceptions, attitudes, and normalization of cannabis use [ 80 - 82 ]; and unique communities of cannabis user subgroups and network structures of cannabis dispensaries [ 83 , 84 ]. Data from other Web-based platforms such as Reddit [ 85 ], Instagram [ 86 ], YouTube [ 87 ], and search engines such as Google [ 88 ] or Bing [ 89 ] are all other potential sources of digital trace data that have been used to study patterns of cannabis use.…”

Social Medicine: Twitter in Healthcare

“…Other clear applications of Twitter to medical research is the application of a multi-tiered approach of sentiment analysis, data-mining, and artificial intelligence to analyze tweets from the general population [ 17 , 18 ]. This real-time information can inform resource allocation for researchers and treatment plans for physicians [ 3 ].…”

“…This real-time information can inform resource allocation for researchers and treatment plans for physicians [ 3 ]. Previous studies have shown the predictive power of Twitter for public health ranging from tracking infectious disease outbreaks, natural disasters, drug use, and more [ 3 , 4 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. While computational power was previously a bottleneck for mining massive datasets, current advances in graphics processing units (GPUs) have enabled efficient instantaneous mining and analysis.…”

Studying Cannabis Use Behaviors With Facebook and Web Surveys: Methods and Insights (Preprint)

“…A growing body of literature has demonstrated that digital trace data [44] (ie, records of naturalistic digital behavior and communication) can be used to study cannabis use. For example, Twitter has been used to study new forms of cannabis administration such as dabbing [75,76], vaping [77], and edibles [78,79]; perceptions, attitudes, and normalization of cannabis use [80][81][82]; and unique communities of cannabis user subgroups and network structures of cannabis dispensaries [83,84]. Data from other Web-based platforms such as Reddit [85], Instagram [86], YouTube [87], and search engines such as Google [88] or Bing [89] are all other potential sources of digital trace data that have been used to study patterns of cannabis use.…”