Linguistic Predictors of Problematic Drinking in Alcohol-related Facebook Posts

Swol, Lyn M. Van; Chang, Chen-Ting; Kerr, Bradley; Moreno, Megan A.

doi:10.1080/10810730.2020.1731632

Cited by 7 publications

(13 citation statements)

References 65 publications

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“…Consistent with prior studies (Kornfield et al, 2018;Marengo et al, 2019;Moreno et al, 2016;van Swol et al, 2020), we also found that high-risk drinkers not only talk more about drinking alcohol or partying/going out but they also use more informal and swear language. Our general word clouds, along with our top LIWC categories and LDA topics, echo this finding.…”

Section: Discussionsupporting

confidence: 90%

“…Finally, to provide the linguistic insight we focus on the specific words or phrases that are associated with excessive drinking in our large sample of respondents. We expect to find positive associations between excessive drinking and negative emotion words, swear words, alcohol words, and/or references to nightlife/sporting events (Marengo et al, 2019;van Swol et al, 2020).…”

Section: Current Studymentioning

confidence: 98%

“…Facebook and Twitter language have also been connected to alcohol use behaviors, especially in relation to college students. Results indicate that college students who use more negative emotion words, swear words, and refer to alcohol in posts are more likely to have drunk alcohol recently, report negative consequences as a result of drinking, score higher on the AUDIT, and experience alcohol cravings (Moreno et al, 2016;van Swol et al, 2020;Westgate et al, 2014). A follow-up study of AUD sufferers who completed a mobile intervention (Addiction-Comprehensive Health Enhancement Support System or A-CHESS) similarly found that swear, negative emotion, and inhibition words (like "stop" or "block") used in forum posts predicted relapse at follow-up (Kornfield et al, 2018).…”

Section: Advantages Of Using Social Mediamentioning

confidence: 99%

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Using Facebook language to predict and describe excessive alcohol use

Jose

Matero

Sherman

et al. 2022

Alcoholism Clin & Exp Res

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Background Assessing risk for excessive alcohol use is important for applications ranging from recruitment into research studies to targeted public health messaging. Social media language provides an ecologically embedded source of information for assessing individuals who may be at risk for harmful drinking. Methods Using data collected on 3664 respondents from the general population, we examine how accurately language used on social media classifies individuals as at‐risk for alcohol problems based on Alcohol Use Disorder Identification Test‐Consumption score benchmarks. Results We find that social media language is moderately accurate (area under the curve = 0.75) at identifying individuals at risk for alcohol problems (i.e., hazardous drinking/alcohol use disorders) when used with models based on contextual word embeddings. High‐risk alcohol use was predicted by individuals’ usage of words related to alcohol, partying, informal expressions, swearing, and anger. Low‐risk alcohol use was predicted by individuals’ usage of social, affiliative, and faith‐based words. Conclusions The use of social media data to study drinking behavior in the general public is promising and could eventually support primary and secondary prevention efforts among Americans whose at‐risk drinking may have otherwise gone “under the radar.”

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Section: Discussionsupporting

confidence: 90%

Section: Current Studymentioning

confidence: 98%

Section: Advantages Of Using Social Mediamentioning

confidence: 99%

See 1 more Smart Citation

Using Facebook language to predict and describe excessive alcohol use

Jose

Matero

Sherman

et al. 2022

Alcoholism Clin & Exp Res

View full text Add to dashboard Cite

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“…Many social media platforms are directly searchable using hashtags, allowing the ability to gather many highly specific posts instead of gathering a large number of nonspecific posts to identify relevant hashtags, keywords, or alcohol content based on available prevalence data. Although the latter approach has shown success in studying alcohol-related behavior previously, methods to extend the analysis to alternative but potentially related outcomes, platforms, or different geographical designations remain unclear [ 39 , 40 , 108 ]. The generalization of such models necessitates the creation of individual models for each particular outcome and geographic area.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Identification of Alcohol-Related Content on Social Media (Reddit and Twitter): Exploratory Analysis of Alcohol-Related Outcomes

Ricard

Hassanpour

2021

J Med Internet Res

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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 apply our method on Twitter to determine the association of the prevalence of alcohol-related tweets with alcohol-related outcomes reported from the National Institute of Alcoholism and Alcohol Abuse, Centers for Disease Control Behavioral Risk Factor Surveillance System, county health rankings, and the National Industry Classification System. Methods The Bidirectional Encoder Representations From Transformers 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 data set 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 between the prevalence of alcohol-related tweets and alcohol-related outcomes, controlling for confounding effects of age, sex, income, education, and self-reported race, as recorded by the 2013-2018 American Community Survey. Results Significant associations were observed: 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; between alcohol-hashtagged tweets and self-reported excessive drinking behavior (P=.03) but not motor vehicle fatalities involving alcohol (P=.21); 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 US county and county-equivalent level; 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 US state level. Conclusions Here, we present a novel natural language processing pipeline developed using Reddit’s alcohol-related subreddits that identify highly specific alcohol-related Twitter hashtags. The prevalence of identified hashtags contains interpretable information about alcohol consumption at both coarse (eg, US state) and fine-grained (eg, metropolitan-micropolitan statistical area level and county) geographical designations. This approach can expand research and deep learning interventions on alcohol abuse and other behavioral health outcomes.

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“…Various applications of automated social media analytic methods to address substance use-related research questions have been reported in the literature. Facebook data has been used to investigate public attitudes towards synthetic opioids (Beletsky et al, 2020), to discover the emotional valence regarding alcohol use among college students (Van Swol et al, 2020), and to investigate and characterize substance use among new mothers (Oram et al, 2018). In addition to Facebook, online communities (including Reddit) have shown utility as a data source for investigating substance use behaviour.…”

Section: Introductionmentioning

confidence: 99%