Associations Between Substance Use and Instagram Participation to Inform Social Network-Based Screening Models: Multimodal Cross-Sectional Study

Associations between posting about alcohol on social networking sites and alcohol-induced blackouts in a sample of young adults not in 4-year college

OBJECTIVE

Research among young adults (YA), in samples of majority White college students, indicates links between posting about alcohol on social media and self-reported drinking behavior. We sought to extend this work by examining unique associations between public versus private posting about alcohol and the high-risk outcome of alcohol-related blackouts among a sample of racially/ethnically diverse YA not in 4-year college.

METHOD

A sample of 499 participants (ages 18-29; 52.5% female; 37.5% Black/African American, 26.9% White, 25.3% Hispanic/Latinx) completed an online survey about social media use and drinking behavior.

RESULTS

Across three platforms (Instagram, TikTok, Twitter [now known as "X"]), public posting on Instagram was most common. Adjusting for covariates, a higher frequency of private posting about alcohol was associated with a higher frequency of past-month blackouts. Tests of simple effects of posting on blackouts within racial/ethnic subgroups indicated that private posting about alcohol was significantly associated with past-month blackouts only among those who most strongly identified as Black/African American or White but not among those who most strongly identified as Hispanic/Latinx. Further, public posting was significantly associated with past-month blackouts, though the association was specific to White participants.

CONCLUSIONS

Whether posting about alcohol may be useful in identifying risky drinking behavior may depend on racial/ethnic identification as well as whether private or public posting is being considered. Results have implications for eventual online interventions, which can identify individuals potentially at risk for hazardous drinking based on their social media posting behavior. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Insights from the Twittersphere: a cross-sectional study of public perceptions, usage patterns, and geographical differences of tweets discussing cocaine

Introduction

Cocaine abuse represents a major public health concern. The social perception of cocaine has been changing over the decades, a phenomenon closely tied to its patterns of use and abuse. Twitter is a valuable tool to understand the status of drug use and abuse globally. However, no specific studies discussing cocaine have been conducted on this platform.

Methods

111,508 English and Spanish tweets containing "cocaine" from 2018 to 2022 were analyzed. 550 were manually studied, and the largest subset underwent automated classification. Then, tweets related to cocaine were analyzed to examine their content, types of Twitter users, usage patterns, health effects, and personal experiences. Geolocation data was also considered to understand regional differences.

Results

A total of 71,844 classifiable tweets were obtained. Among these, 15.95% of users discussed the harm of cocaine consumption to health. Media outlets had the highest number of tweets (35.11%) and the most frequent theme was social/political denunciation (67.88%). Regarding the experience related to consumption, there are more tweets with a negative sentiment. The 9.03% of tweets explicitly mention frequent use of the drug. The continent with the highest number of tweets was America (55.44% of the total).

Discussion

The findings underscore the significance of cocaine as a current social and political issue, with a predominant focus on political and social denunciation in the majority of tweets. Notably, the study reveals a concentration of tweets from the United States and South American countries, reflecting the high prevalence of cocaine-related disorders and overdose cases in these regions. Alarmingly, the study highlights the trivialization of cocaine consumption on Twitter, accompanied by a misleading promotion of its health benefits, emphasizing the urgent need for targeted interventions and antidrug content on social media platforms. Finally, the unexpected advocacy for cocaine by healthcare professionals raises concerns about potential drug abuse within this demographic, warranting further investigation.

National trends in alcohol and substance use among adolescents from 2005 to 2021: a Korean serial cross-sectional study of one million adolescents

BACKGROUND

Although previous studies have provided data on early pandemic periods of alcohol and substance use in adolescents, more adequate studies are needed to predict the trends of alcohol and substance use during recent periods, including the mid-pandemic period. This study investigated the changes in alcohol and substance use, except tobacco use, throughout the pre-, early-, and mid-pandemic periods in adolescents using a nationwide serial cross-sectional survey from South Korea.

METHODS

Data on 1,109,776 Korean adolescents aged 13-18 years from 2005 to 2021 were obtained in a survey operated by the Korea Disease Control and Prevention Agency. We evaluated adolescents' alcohol and substance consumption prevalence and compared the slope of alcohol and substance prevalence before and during the COVID-19 pandemic to see the trend changes. We define the pre-COVID-19 period as consisting of four groups of consecutive years (2005-2008, 2009-2012, 2013-2015, and 2016-2019). The COVID-19 pandemic period is composed of 2020 (early-pandemic era) and 2021 (mid-pandemic era).

RESULTS

More than a million adolescents successfully met the inclusion criteria. The weighted prevalence of current alcohol use was 26.8% [95% confidence interval (CI) 26.4-27.1] from 2005 to 2008 and 10.5% (95% CI 10.1-11.0) in 2020 and 2021. The weighted prevalence of substance use was 1.1% (95% CI 1.1-1.2) from 2005 to 2008 and 0.7% (95% CI 0.6-0.7) between 2020 and 2021. From 2005 to 2021, the overall trend of use of both alcohol and drugs was found to decrease, but the decline has slowed since COVID-19 epidemic (current alcohol use: βdiff 0.167; 95% CI 0.150-0.184; substance use: βdiff 0.152; 95% CI 0.110-0.194). The changes in the slope of current alcohol and substance use showed a consistent slowdown with regard to sex, grade, residence area, and smoking status from 2005 to 2021.

CONCLUSION

The overall prevalence of alcohol consumption and substance use among over one million Korean adolescents from the early and mid-stage (2020-2021) of the COVID-19 pandemic showed a slower decline than expected given the increase during the prepandemic period (2005-2019).

Analysis of Wastewater Samples to Explore Community Substance Use in the United States: Pilot Correlative and Machine Learning Study

BACKGROUND

Substance use disorder and associated deaths have increased in the United States, but methods for detecting and monitoring substance use using rapid and unbiased techniques are lacking. Wastewater-based surveillance is a cost-effective method for monitoring community drug use. However, the examination of the results often focuses on descriptive analysis.

OBJECTIVE

The objective of this study was to explore community substance use in the United States by analyzing wastewater samples. Geographic differences and commonalities of substance use were explored.

METHODS

Wastewater was sampled across the United States (n=12). Selected drugs with misuse potential, prescriptions, and over-the-counter drugs and their metabolites were tested across geographic locations for 7 days. Methods used included wastewater assessment of substances and metabolites paired with machine learning, specifically discriminant analysis and cluster analysis, to explore similarities and differences in wastewater measures.

RESULTS

Geographic variations in the wastewater drug or metabolite levels were found. Results revealed a higher use of methamphetamine (z=-2.27, P=.02) and opioids-to-methadone ratios (oxycodone-to-methadone: z=-1.95, P=.05; hydrocodone-to-methadone: z=-1.95, P=.05) in states west of the Mississippi River compared to the east. Discriminant analysis suggested temazepam and methadone were significant predictors of geographical locations. Precision, sensitivity, specificity, and F1-scores were 0.88, 1, 0.80, and 0.93, respectively. Finally, cluster analysis revealed similarities in substance use among communities.

CONCLUSIONS

These findings suggest that wastewater-based surveillance has the potential to become an effective form of surveillance for substance use. Further, advanced analytical techniques may help uncover geographical patterns and detect communities with similar needs for resources to address substance use disorders. Using automated analytics, these advanced surveillance techniques may help communities develop timely, tailored treatment and prevention efforts.

Using Machine Learning of Online Expression to Explain Recovery Trajectories: Content Analytic Approach to Studying a Substance Use Disorder Forum

BACKGROUND

Smartphone-based apps are increasingly used to prevent relapse among those with substance use disorders (SUDs). These systems collect a wealth of data from participants, including the content of messages exchanged in peer-to-peer support forums. How individuals self-disclose and exchange social support in these forums may provide insight into their recovery course, but a manual review of a large corpus of text by human coders is inefficient.

OBJECTIVE

The study sought to evaluate the feasibility of applying supervised machine learning (ML) to perform large-scale content analysis of an online peer-to-peer discussion forum. Machine-coded data were also used to understand how communication styles relate to writers' substance use and well-being outcomes.

METHODS

Data were collected from a smartphone app that connects patients with SUDs to online peer support via a discussion forum. Overall, 268 adult patients with SUD diagnoses were recruited from 3 federally qualified health centers in the United States beginning in 2014. Two waves of survey data were collected to measure demographic characteristics and study outcomes: at baseline (before accessing the app) and after 6 months of using the app. Messages were downloaded from the peer-to-peer forum and subjected to manual content analysis. These data were used to train supervised ML algorithms using features extracted from the Linguistic Inquiry and Word Count (LIWC) system to automatically identify the types of expression relevant to peer-to-peer support. Regression analyses examined how each expression type was associated with recovery outcomes.

RESULTS

Our manual content analysis identified 7 expression types relevant to the recovery process (emotional support, informational support, negative affect, change talk, insightful disclosure, gratitude, and universality disclosure). Over 6 months of app use, 86.2% (231/268) of participants posted on the app's support forum. Of these participants, 93.5% (216/231) posted at least 1 message in the content categories of interest, generating 10,503 messages. Supervised ML algorithms were trained on the hand-coded data, achieving F1-scores ranging from 0.57 to 0.85. Regression analyses revealed that a greater proportion of the messages giving emotional support to peers was related to reduced substance use. For self-disclosure, a greater proportion of the messages expressing universality was related to improved quality of life, whereas a greater proportion of the negative affect expressions was negatively related to quality of life and mood.

CONCLUSIONS

This study highlights a method of natural language processing with potential to provide real-time insights into peer-to-peer communication dynamics. First, we found that our ML approach allowed for large-scale content coding while retaining moderate-to-high levels of accuracy. Second, individuals' expression styles were associated with recovery outcomes. The expression types of emotional support, universality disclosure, and negative affect were significantly related to recovery outcomes, and attending to these dynamics may be important for appropriate intervention.

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

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.