JMIR Publications

ABSTRACT

Background:

Many social media studies have explored the ability of thematic structures, such as hashtags and subreddits, to identify information related to a wide variety of mental health disorders. However, studies and models trained on specific themed communities are often difficult to apply to different social media platforms and related outcomes. A deep learning framework using thematic structures from Reddit and Twitter can have distinct advantages for studying alcohol abuse, particularly among the youth, in the United States.

Objective:

This study proposes a new deep learning pipeline that uses thematic structures to identify alcohol-related content across different platforms. We applied our method on Twitter to determine the association of the prevalence of alcohol-related tweets and alcohol-related outcomes reported from the National Institute of Alcoholism and Alcohol Abuse (NIAAA), Centers for Disease Control Behavioral Risk Factor Surveillance System (CDC BRFSS), County Health Rankings, and the National Industry Classification System (NAICS).

Methods:

A Bidirectional Encoder Representations from Transformers (BERT) neural network learned to classify 1,302,524 Reddit posts as either alcohol-related or control subreddits. The trained model identified 24 alcohol-related hashtags from an unlabeled dataset of 843,769 random tweets. Querying alcohol-related hashtags identified 25,558,846 alcohol-related tweets, including 790,544 location-specific (geotagged) tweets. We calculated the correlation of the prevalence of alcohol-related tweets with alcohol-related outcomes, controlling for confounding effects from age, sex, income, education, and self-reported race, as recorded by the 2013-2018 American Community Survey (ACS).

Results:

Significant associations were observed (1) between alcohol-hashtagged tweets and alcohol consumption (P = .01) and heavy drinking (P = .005), but not binge drinking (P = .37), self-reported at the Metropolitan-Micropolitan Statistical Area level (MMSA); (2) between alcohol-hashtagged tweets and self-reported excessive drinking behavior (P = .03), but not motor vehicle fatalities involving alcohol (P = .21); (3) between alcohol-hashtagged tweets and the number of breweries (P < .001), wineries (P < .001), and beer, wine, and liquor stores (P < .001), but not drinking places (P = .23), per capita at the U.S. county and county-equivalent level; (4) and between alcohol-hashtagged tweets and all gallons of ethanol consumed (P < .001), as well as ethanol consumed from wine (P < .001) and liquor (P = .01) sources, but not beer (P = .63), at the U.S. State level.

Conclusions:

Here, we present a novel natural language processing pipeline developed using Reddit alcohol-related subreddits that identifies highly specific alcohol-related Twitter hashtags. Prevalence of identified hashtags contains interpretable information about alcohol consumption at both coarse (e.g., U.S. State) and fine-grained (e.g., MMSA, County) geographical designations. This approach can expand research and deep learning interventions on alcohol abuse and other behavioral health outcomes.