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

Abstract

Background Technology-based computational strategies that leverage social network site (SNS) data to detect substance use are promising screening tools but rely on the presence of sufficient data to detect risk if it is present. A better understanding of the association between substance use and SNS participation may inform the utility of these technology-based screening tools. Objective This paper aims to examine associations between substance use and Instagram posts and to test whether such associations differ as a function of age, gender, and race/ethnicity. Methods Participants with an Instagram account were recruited primarily via Clickworker (N=3117). With participant permission and Instagram’s approval, participants’ Instagram photo posts were downloaded with an application program interface. Participants’ past-year substance use was measured with an adapted version of the National Institute on Drug Abuse Quick Screen. At-risk drinking was defined as at least one past-year instance having “had more than a few alcoholic drinks a day,” drug use was defined as any use of nonprescription drugs, and prescription drug use was defined as any nonmedical use of prescription medications. We used logistic regression to examine the associations between substance use and any Instagram posts and negative binomial regression to examine the associations between substance use and number of Instagram posts. We examined whether age (18-25, 26-38, 39+ years), gender, and race/ethnicity moderated associations in both logistic and negative binomial models. All differences noted were significant at the .05 level. Results Compared with no at-risk drinking, any at-risk drinking was associated with both a higher likelihood of any Instagram posts and a higher number of posts, except among Hispanic/Latino individuals, in whom at-risk drinking was associated with a similar number of posts. Compared with no drug use, any drug use was associated with a higher likelihood of any posts but was associated with a similar number of posts. Compared with no prescription drug use, any prescription drug use was associated with a similar likelihood of any posts and was associated with a lower number of posts only among those aged 39 years and older. Of note, main effects showed that being female compared with being male and being Hispanic/Latino compared with being White were significantly associated with both a greater likelihood of any posts and a greater number of posts. Conclusions Researchers developing computational substance use risk detection models using Instagram or other SNS data may wish to consider our findings showing that at-risk drinking and drug use were positively associated with Instagram participation, while prescription drug use was negatively associated with Instagram participation for middle- and older-aged adults. As more is learned about SNS behaviors among those who use substances, researchers may be better positioned to successfully design and interpret innovative risk detection approaches.

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

“…Peer context also exists in the less decreasing trend of alcohol and substance use [ 34 ]. In the context of substance use among adolescents, there are concerns about how social distancing affects their peer reputations [ 34 ], which means that popular adolescents are attempting to uphold group norms via solitary substance use [ 22 ].…”

“…Peer context also exists in the less decreasing trend of alcohol and substance use [ 34 ]. In the context of substance use among adolescents, there are concerns about how social distancing affects their peer reputations [ 34 ], which means that popular adolescents are attempting to uphold group norms via solitary substance use [ 22 ]. Adolescents who are less popular, who may have less confidence that their friendships can withstand social distancing, may be more willing to engage in risky behavior, such as face-to-face substance use during a pandemic, to maintain their social status.…”

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

“…It remains unclear whether our results indicate a bias in our model’s methodological choice (eg, use of hashtags or training procedure) or a difference in social media populations that prefer different alcohol beverages. The difference in correlations might be because of several variables, including the existence of confounding factors related to the prevalence of social media use in the underlying populations that preferentially consume beer over other alcohol types, differences in perceived acceptance of beer consumption behavior, or because of other factors that may confound alcohol and social media posting [ 89 ]. Alcohol preference is an example of having interpretable hashtag representations for a given model that may help identify behavioral differences associated with an outcome of interest.…”

“…Spearman rho, a ranked nonparametric measure that is more robust to outliers than Pearson correlation, is used to report crude (nonadjusted) correlations [ 88 ]. Potential confounding variables previously studied in alcohol and social media use include race and sex distribution, median age, education, and income [ 37 , 89 ]. A linear regression analysis evaluated the relationship between the number of tweets per population and alcohol-related outcomes after including terms to control for confounding effects.…”

Application of Digital Medicine in Addiction

“…Communication records can convey information from social networks. Bergman et al [22] found that compared with people not using addictive substances, anyone who is at risk of drinking or taking drugs (e.g., marijuana, cocaine, and heroin) are more likely to post on Instagram. Wearable devices, such as watches, wristbands, chest straps, and transdermal patches, facilitate real-time collection of various types of data, such as biological samples, location, and physiological changes, to help researchers understand the causes and consequences of substance use [19] .…”