Dataset Information

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.

SUBMITTER: Baumgartner P

PROVIDER: S-EPMC5462814 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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

Baumgartner Peter P Peiper Nicholas N

Substance abuse : research and treatment 20170606

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 preve ...[more]

PMID: 28615950

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Project description:Past research has attributed the circulation of online news to two main factors-individual characteristics (e.g., a person's information literacy) and social media effects (e.g., algorithm-mediated information diffusion)-and has overlooked a third one: the critical mass created by the offline self-segregation of Americans into like-minded geographical regions such as states (a phenomenon called 'The Big Sort'). We hypothesized that this latter factor matters for the online spreading of news not least because online interactions, despite having the potential of being global, end up being localized: interaction probability is known to rapidly decay with distance. Upon analysis of more than 8M Reddit comments containing news links spanning four years, from January 2016 to December 2019, we found that Reddit did not work as an 'hype machine' for news (as opposed to what previous work reported for other platforms, circulation was not mainly caused by platform-facilitated network effects). Rather, news circulation in Reddit worked as a supply-and-demand system: news items scaled linearly with the number of users in each state (with a scaling exponent [Formula: see text] [Formula: see text], and a goodness of fit [Formula: see text]). Furthermore, deviations from such a universal pattern were best explained by state-level personality and cultural factors ([Formula: see text]), rather than socioeconomic conditions ([Formula: see text]) or political characteristics ([Formula: see text]). Higher-than-expected circulation of any type of news was found in states characterised by residents who tend to be less diligent in terms of their personality (low in conscientiousness) and by loose cultures understating the importance of adherence to norms (low in cultural tightness). Interestingly, the combination of those factors with low levels of education was then associated with the circulation of a particular type of news, that is, misinformation. These results suggest that online interactions are geographically bounded and, as such, news circulation cannot be studied purely as an Internet phenomenon but should be grounded into a user's offline cultural environment, which has become increasingly segregated over the decades, and is admittedly hard to change.

Dataset Information

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

Publications

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

Similar Datasets

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