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. Author manuscript; available in PMC: 2025 Feb 28.
Published in final edited form as: Subst Use Misuse. 2024 Feb 28;59(7):1039–1046. doi: 10.1080/10826084.2024.2320372

Association between social media use and substance use among middle and high school-aged youth

Jessica Liu 1, Linda Charmaraman 2, David Bickham 3
PMCID: PMC11062178  NIHMSID: NIHMS1978167  PMID: 38419151

Abstract

Purpose

The purpose of our study was to identify whether different aspects of social media use were associated with substance use among middle- and high school-aged youth.

Methods

Participants were recruited from four Northeast U.S. middle schools and invited to complete an online survey in Fall 2019 and Fall 2020. We conducted separate adjusted logistic mixed effects models the substance use outcomes: ever use of alcohol, cannabis, e-cigarettes, tobacco cigarettes, prescription drugs, and multiple substances. Our sample included N=586 participants (52.7% female, 58% White).

Results

Seeing a social media post about drugs/alcohol in the past-12-months was significantly associated with higher odds of ever using alcohol, cannabis, e-cigarettes, and multiple substance use. Total number of social media sites ever used was significantly associated with higher odds of ever using cannabis, cigarettes, e-cigarettes, and multiple substances. Checking social media every hour or more was significantly associated with higher odds of ever using alcohol. Higher problematic internet use score was significantly associated with higher odds of ever using cannabis, e-cigarettes, and multiple substances. Online social support seeking score was not associated substance use.

Conclusions

Our findings support the need for substance use prevention and social media literacy education and screening to begin early, ideally in elementary school before youth are using social media and substances.

Keywords: cannabis, e-cigarette, social media, tobacco, youth

Introduction

Although prevalence of adolescent substance use is high during later adolescence and high school years, substance use begins at an early age, during middle school or even earlier. According to the national 2021 Monitoring the Future Survey, approximately 13% of 10th graders and 7% of 8th graders drank alcohol in the past 30 days.1 The same national survey found that 16% of 10th graders and 9% of 8th graders reported any vaping in the past 30 days, and 10% of 10th graders and 4% of 8th graders reported using cannabis in the past 30 days.1 Although substance use saw a decline during COVID-19,2 substance use remains a problem making it valuable to understanding the forces that contribute to its adoption and maintenance.

Considering that social media platforms can show peer-generated as well as marketing depictions of substance use, these online spaces may be especially fertile grounds for forming norms and behavioral expectations around substance use. Young adolescents spend considerable time connecting online especially in these virtual environments. Research has reported statistics as high as 95% of youth aged 13 to 17 owning smartphones, with no difference between household income and parent education level.3,4 Youth internet and social media use has been increasing in recent years.5 In 2022, 97% of youth aged 13 to 17 reported daily internet use, with YouTube, TikTok, and Instagram being the most popular social media platforms.5 This increase in social media use among youth has mixed impacts, as research has demonstrated that it provides a platform for social support including: a space for social connection, expressing feelings, and engaging with others going through similar health situations.6–8 However, studies have also found links between youth social media use and negative mental health outcomes including: social isolation, increased risk for anxiety and depression, decrease in self-acceptance, and cyberbullying.6,9,10

Early adolescence is a particularly unique time in the developmental process, as youth are transitioning to adulthood and particularly influenced by peer networks, including the rapidly evolving world of social media.11–13 Youth are constantly being exposed to both explicit and implicit messaging around substance use, from both unofficial social media channels and more official advertising from the industry itself.14,15 Youth receive much of their knowledge and interact with their peers online, thus youth are highly influenced by the content they see on social media and this may translate to their in-person social behaviors.16–19 Prior research has found that exposure to this online content around substance use is associated with subsequent substance use among youth and young adults (ages 12–22).20,21 Seeing substance use on social media may normalize substance use among youth, especially with viewing positive messages about substances on YouTube.22 Yet, studies have also demonstrated the power of using social media as a prevention intervention method.23,24 Thus, additional research is needed to understand the positive and/or negative associations between social media use and substance use among youth. Moreover, different types of social media use and engagement may lead to different outcomes, and just observing social media exposure more generally does not capture the details of different aspects of social media use.

Online industry marketing is ubiquitous, appearing online in sites and social media frequented by young people, and results in perceptions of reduced risks and greater social acceptability of substance use.11,25 Despite the Food and Drug Administration (FDA) heightened scrutiny over marketing of products such as e-cigarettes, a recent content analysis found that e-cigarette advertisements still have youth-appealing features.26 In addition to official industry content that youth are seeing online, youth are also seeing content on social media posted by friends or influencers that exhibit and encourage substance use.27 Social media tends to be a place where youth initially learn about or are exposed to substance use, and this social media exposure has been found to increase risk of subsequent substance use.28,29 Social connections, both in person and from social media, facilitate substance use among youth, especially in school settings.27,30 Relationships among peers and social circles influence both e-cigarette use as well as obtaining e-cigarette products.12,31,32 For example, youth might not see official industry advertising for tobacco and cannabis products, but they may see their friends or influencers using on social media.27 This content posted by peers (versus industry advertising) could have a bigger impact on youth’s beliefs and ultimately substance use, as it is presented by people they identify with.33

Little research exists that focuses on substance use among middle school-aged youth and even less examines links between different types of substance use and various specific types of social media use. One longitudinal study found a long-term association between earlier substance-related media exposure (e.g., internet videos, social media pictures, movies and television, billboards, and video games) and later youth substance use.21 Another study, conducted in Italy, found that problematic social media use was associated with substance use in middle school-aged students.34 Among high school students, social media was shown to be associated with peer injunctive norms which, in turn, predicted alcohol use.35 More work that differentiates between specific types of online exposure as well as different types of substance use can help better understand the associations between social media use and substance use in younger adolescents in the U.S.

Our current exploratory study was novel in that the main research question was to investigate the association between different elements of social media use (e.g., frequency of use, content consumed, purpose of use) with risk of continued use for various types of substances among a younger population of middle school-aged youth. Previous studies tended to examine associations between one aspect of social media use (e.g., frequency of use), rather than the specific types in this study. Our study uniquely includes multiple types of social media-related exposures, as well as multiple types of substances as outcomes (including poly-use). Our goal was to capture additional granularity between social media use and substance use among a younger population that previous studies have not included. Since our study involved a younger population, our measure for risk of continued substance use was having ever tried a substance in their life, or lifetime use.36 Although our study utilizes data collected from youth at two timepoints, this is not a longitudinal study and therefore can only investigate associations between social media use and lifetime substance use.

Based on our review of the literature, we hypothesized:

  • H1:

    Youth who have seen social media posts about drugs or alcohol will have higher odds of lifetime substance use.

  • H2:

    Youth who use more social media sites will have higher odds of lifetime substance use.

  • H3:

    Youth who check social media more often will have higher odds of lifetime substance use.

  • H4:

    Youth who have higher problematic internet use will have higher odds of lifetime substance use.

  • H5:

    Youth who use social media for online support will have lower odds of lifetime substance use.

Our findings will provide continued evidence for clinicians, educators, and public health practitioners towards the need for media literacy interventions and improved screening to address youth substance use at an early age.

Methods

Study Population and Design

Data were collected as part of a larger ongoing longitudinal study of early adolescent social media use and behavioral health.37 Participants were recruited from four Northeast U.S. schools of varying sizes and diverse demographics within urban and suburban areas. All students in these schools within the qualifying age-range were invited to participate. Parents could opt their children out from participating in the survey study, which was administered during advisory periods or a free period in the afterschool program. Students were invited to provide assent to complete an online survey at two timepoints (Fall 2019 and Fall 2020). In the first timepoint (Fall 2019), N=1033 participated in the survey (average response rate 84% across the sites). In the second timepoint (Fall 2020), N=968 participated in the survey (average response rate 96% across the sites). Further details about study recruitment can be found elsewhere.38,39 Our analytic sample included N=586 participants having completed the survey at both timepoints, for a total of N=1172 observations. The mean age of participants was 12.5 (time 1) and 13.7 (time 2), 52.7% identified as female, and 58% were White (see Table 1 for full sample demographics at both timepoints). Survey measures included questions around social media use, substance use, mental health, and sociodemographic information. This study was approved by the Institutional Review Board of ANONONYZED University.

Table 1:

Participant Demographics at Time 1 and Time 2 (N=586), n (%)

Time 1 Time 2
Gender
Female 309 (52.7%) 309 (52.7%)
Male 276 (47.1%) 276 (47.1%)
Missing 1 (0.2%) 1 (0.2%)
Age, mean (SD) 12.5 (1.2) 13.7 (1.3)
Receive free/reduced lunch at school
Yes 112 (19.1%) 208 (35.5%)
No 471 (80.4%) 376 (64.2%)
Missing 3 (0.5%) 2 (0.3%)
Race/ethnicity
White 340 (58.0%) 340 (58.0%)
Black 53 (9.0%) 53 (9.0%)
Hispanic 86 (14.7%) 86 (14.7%)
Other 107 (18.3%) 107 (18.3%)
Mother/female guardian education
Less than college 112 (19.1%) 133 (22.7%)
College or more 468 (78.9%) 452 (77.1%)
Missing 6 (0.1%) 1 (0.2%)
Sexual orientation
Heterosexual 404 (68.9%) 393 (67.1%)
Another sexual orientation 120 (20.5%) 129 (22.0%)
Missing 62 (10.6%) 64 (10.9%)
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Substance Use Outcomes

Participants were asked about their substance use history with the question “Have you ever tried this?” with the “select all that apply” answer options of: “Alcohol other than a few sips,” “Marijuana (pot or weed),” “Smoking tobacco cigarettes,” “Vaping/e-cigarettes (e.g. juuling),” “Someone else’s prescription drug that is not prescribed to you.” Each type of substance was coded as a separate binary outcome variable (yes, no). Outcome variables were created for each type of substance use as well as for having tried more than one substance. If participants responded “Yes” to two or more of the substances, they were recoded to “Yes” for having ever used more than one substance, and participants who responded “Yes” for one or none of the substances were recoded to “No” for this outcome.

Social Media Exposures

Exposure to substance use information on social media.

Participants were asked about their exposure to substance use on social media with the question: “In the past 12 months, have you seen websites or social media posts where other people discussed the following…(check any that apply)” with one of the answer options being “sharing experiences of taking drugs or drinking.”

Total number of social media sites used.

Participants were asked to report about the number of social media sites they use with the question: “Please check any social media sites below that you have ever used or joined” with the answer options of “None, I have never used any of these sites,” followed by a list of 20 common social media sites, such as Instagram, Snapchat, YouTube, TikTok, etc. A total number of social media sites ever used or joined variable was created by summing the total number of sites they indicated they used or joined (range 0–20).

Frequency of checking social media.

Participants were asked about their frequency of social media use: “On a typical school week, how often do you check Social media (like Instagram),” with the answer options of “Never/Does not apply to me,” “Every few days,” “Once a day,” “Every few hours,” “Every hour,” and “More than every hour.” This was recoded to a binary variable (every hour or more, less than every hour).

Problematic Internet Use.

We used a 3-item problematic internet use scale to measure problematic internet use score (mean score 1–5; Cronbach’s alpha: 0.724).40 Participants were asked: “Think about when you spend time online (like going on YouTube, playing online games, searching for information, etc.) How often do you: 1) Lose motivation to do other things that need to get done because of the internet?; 2) - Feel nervous or anxious when you’re NOT online?; 3) Become moody or depressed when you’re not online?” with the possible response options of a five-point Likert scale ranging from “Never” (score of 1) to “Very Often” (score of 5).

Online social support seeking.

To measure online social support seeking, we adapted a 3-item scale from the Perceived Support Subscale of the Facebook Measure of Social Support (mean score 1–5; Cronbach’s alpha: 0.819), found to have high convergent validity with two traditional measures of social support.41 Participants were asked: “Answer the next questions thinking of your social media sites that have friends/followers: 1) When I am stressed out, I turn to my friends for help on this site; 2) The support I get on this site makes me feel better; 3) This site makes me feel close to people” with the possible response options of a five-point Likert scale ranging from “Strongly Disagree” (score of 1) to “Strongly Agree” (score of 5).

Covariates

Analyses were adjusted for participant demographics including gender (male/female), age, whether they receive free/reduced lunch at school (yes/no), race/ethnicity (White/Black/Hispanic/other), education of mother/female guardian (completed college/did not complete college), and sexual orientation (heterosexual, not-heterosexual). We also included mental health factors, including self-reported depression (yes/no), social anxiety (mean score 1–3), and loneliness (mean score 1–3). We used a 3-item scale to measure social anxiety (mean score 1–4; Cronbach’s alpha: 0.762). Participants were asked: “Is this true for you? 1) I get nervous when I meet new people; 2) It’s hard for me to ask others to do things with me; 3) I worry about doing something new in front of others” with the possible response options of a five-point Likert scale ranging from “Mostly Disagree” (score of 1) to “Mostly Agree” (score of 4).42 We used a 3-item scale to measure loneliness (mean score 1–3; Cronbach’s alpha: 0.800). Participants were asked: “Tell me how often you feel this way. 1) How often do you feel that you lack companionship?; 2) How often do you feel left out?; 3) How often do you feel isolated from others?” with the possible response options of a five-point Likert scale ranging from “Hardly ever” (score of 1) to “Often” (score of 3).43

Analyses

For each substance use outcome, we conducted separate logistic mixed effects models (to account for data being nested by wave) in R using the lme4 package,44 adjusting for all social media exposures and covariates. The analytic sample for the logistic mixed effects models included individuals who provided full responses to substance use, demographic, and mental health covariates included in the models, and resulted in approximately 20% missingness.45 Thus, we conducted a complete-case analysis to handle missing data (see Supplemental Table 1 for demographics of our full sample versus included sample in the regressions versus excluded sample in the regressions). Significance was evaluated at the alpha=0.05 level.

Results

At Time 1, participants used an average of 5.3 (SD 3.0) social media sites, 16.4% had in the past 12 months seen a social media post about drugs or alcohol, 55.5% checked their social media more than once per day, had a mean problematic internet use score of 1.7 (SD: 0.7), and mean online social support seeking score of 2.3 (SD: 0.9) (Table 2). At Time 2, participants used an average of 7.0 (SD 3.2) social media sites, 19.8% had in the past 12 months seen a social media post about drugs or alcohol, 42.4% checked their social media more than once per day, had a mean problematic internet use score of 1.9 (SD: 0.9), and mean online social support seeking score of 2.4 (SD: 0.9) (Table 1). For both Time 1 and 2, ever use of alcohol was the most prevalent type of substance use (Time 1: 7.7%, Time 2: 16.2%) followed by e-cigarettes (Time 1: 6.5%, Time 2: 9.6%).

Table 2:

Substance Use, Social Media Use, and Mental Health at Time 1 and Time 2 (N=586), n (%)

Time 1 (N=586) Time 2 (N=586)
Substance Use
Ever tried alcohol
Yes 45 (7.7%) 95 (16.2%)
No 512 (87.4%) 426 (72.7%)
Missing 29 (4.9%) 65 (11.1%)
Ever tried marijuana
Yes 22 (3.8%) 32 (5.5%)
No 536 (91.5%) 488 (83.3%)
Missing 28 (4.8%) 66 (11.3%)
Ever tried cigarettes
Yes 12 (2.0%) 14 (2.4%)
No 543 (92.7%) 505 (86.2%)
Missing 31 (5.3%) 67 (11.4%)
Ever vaped
Yes 38 (6.5%) 56 (9.6%)
No 516 (88.1%) 460 (78.5%)
Missing 32 (5.5%) 70 (11.9%)
Ever tried prescription drug
Yes 4 (0.7%) 9 (1.5%)
No 552 (94.2%) 509 (86.9%)
Missing 30 (5.1%) 68 (11.6%)
Ever used multiple substances
Yes 29 (4.9%) 45 (7.7%)
No 523 (89.2%) 469 (80.0%)
Missing 34 (5.8%) 72 (12.3%)
Social Media Use 94 (9.1%)
Seen social media post about drugs or alcohol 5.3 (3.4)
Yes 96 (16.4%) 116 (19.8%)
No 452 (77.1%) 389 (66.4%)
Missing 96 (6.5%) 81 (13.8%)
Total number of social media sites used (range 0–20), mean (SD) 5.3 (3.0) 7.0 (3.2)
Check social media more than once per day
Yes 325 (55.5%) 249 (42.4%)
No 254 (43.3%) 326 (55.6)
Missing 7 (1.2%) 11 (1.9%)
Problematic Internet Use score, (scale 1–5) mean (SD) 1.7 (0.7) 1.9 (0.9)
Online validation/ support, (scale 1–5) mean (SD) 2.3 (0.9) 2.4 (0.9)
Mental Health
Self-reported Depressive Symptoms
Yes 109 (18.6%) 204 (34.8%)
No 446 (76.1%) 332 (56.7%)
Missing 31 (5.3%) 50 (8.5%)
Social Anxiety (score 0–4), mean (SD) 2.4 (0.8) 2.6 (0.9)
Loneliness (score 0–3), mean (SD) 1.4 (0.5) 1.6 (0.6)
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Seeing social media posts about drugs or alcohol.

We found that having seen a social media post in the past 12 months about drugs or alcohol was significantly associated with higher odds of ever using alcohol (adjusted odds ratio [aOR]: 2.76, 95% confidence interval [CI]: 1.56–4.87), cannabis (aOR: 3.61, CI: 1.52–8.59), e-cigarettes (aOR: 2.03, CI: 1.03–4.01), and multiple substance use (aOR: 6.30, CI: 2.32–17.09) (Table 3).

Table 3:

Adjusted Regression Results for Each Substance Use Outcome (N=586 participants, N=1172 observations), aOR, 95% CI, p-value

Y = Alcohol Y = Cannabis Y = Cigarettes Y = E-cigarettes Y = Prescription Y = Multiple substances
Female 0.59 (0.33–1.04), 0.068 0.51 (0.19–1.38), 0.184 0.21 (0.05–0.90), 0.035 1.14 (0.54–2.39), 0.737 0.66 (0.17–2.59), 0.548 0.81 (0.31–2.15), 0.675
Age 1.72 (1.35–2.20), <0.001 1.48 (1.02–2.15), 0.039 1.14 (0.66–1.99), 0.637 1.23 (0.92–1.63), 0.164 1.24 (0.74–2.08), 0.417 1.13 (0.78–1.62), 0.521
Receive free/reduced lunch at school 0.95 (0.53–1.70), 0.856 0.64 (0.24–1.70), 0.373 0.32 (0.07–1.42), 0.136 0.84 (0.39–1.81), 0.660 1.79 (0.49–6.48), 0.377 0.35 (0.12–1.05), 0.062
Race
Black 1.27 (0.50–3.23), 0.617 2.71 (0.71–10.41), 0.146 1.47 (0.20–10.49), 0.704 0.98 (0.31–3.11), 0.968 1.89 (0.30–11.98), 0.498 1.87 (0.41–8.52), 0.418
Hispanic 1.48 (0.68–3.25), 0.325 4.46 (1.27–15.59), 0.019 1.74 (0.35–8.66), 0.496 1.24 (0.47–3.27), 0.661 2.55 (0.52–12.41), 0.246 1.65 (0.47–5.88), 0.436
Other 1.27 (0.61–2.64), 0.524 0.08 (0.00–1.38), 0.082 0.35 (0.04–3.23), 0.354 0.19 (0.04–0.83), 0.027 1.62 (0.28–9.24), 0.697 0.34 (0.08–1.74), 0.196
Parent complete college 0.66 (0.35–1.22), 0.183 0.61 (0.23–1.61), 0.322 0.33 (0.09–1.19), 0.091 0.31 (0.13–0.73), 0.008 0.77 (0.20–2.94), 0.697 0.26 (0.09–0.81), 0.019
Not-heterosexual 1.38 (0.72–2.67), 0.334 1.19 (0.41–3.40), 0.749 1.32 (0.34–5.08), 0.683 1.68 (0.74–0.381), 0.218 0.94 (0.21–4.22), 0.935 0.87 (0.28–2.70), 0.813
Seen social media post about drugs or alcohol 2.76 (1.56–4.87), <0.001 3.61 (1.52–8.59), 0.004 0.74 (0.19–2.94), 0.666 2.03 (1.03–4.01), 0.042 1.02 (0.26–4.03), 0.972 6.30 (2.32–17.09), <0.001
Total number of social media sites 1.09 (0.99–1.19), 0.086 1.22 (1.05–1.42), 0.010 1.30 (1.05–1.61), 0.016 1.26 (1.11–1.42), <0.001 1.18 (0.95–1.46), 0.128 1.27 (1.08–1.50), 0.004
Check social media every hour or more 1.89 (1.08–3.32), 0.026 1.57 (0.60–4.08), 0.357 1.52 (0.37–6.24), 0.564 1.36 (0.65–2.82), 0.414 1.72 (0.42–7.080), 0.451 1.12 (0.41–3.04), 0.831
Online support 0.89 (0.67–1.20), 0.457 1.02 (0.62–1.68), 0.948 1.47 (0.69–3.15), 0.316 1.25 (0.83–1.88), 0.287 1.06 (0.50–2.22), 0.884 1.30 (0.73–2.29), 0.372
Self-reported Depressive Symptoms 1.87 (0.96–3.65), 0.065 3.76 (1.18–11.97), 0.025 3.01 (0.59–15.35), 0.186 1.70 (0.76–3.84), 0.198 2.08 (0.41–10.54), 0.378 1.47 (0.51–4.26), 0.476
Social Anxiety 0.71 (0.51–1.00), 0.049 0.47 (0.26–0.84), 0.011 0.38 (0.16–0.87), 0.022 0.58 (0.37–0.90), 0.015 0.43 (0.19–0.99), 0.046 0.49 (0.27–0.88), 0.017
Loneliness 0.80 (0.47–1.38), 0.432 0.79 (0.33–1.86), 0.584 1.16 (0.36–3.77), 0.801 0.79 (0.40–1.58), 0.511 1.12 (0.33–3.86), 0.857 0.55 (0.21–1.44), 0.223
Prob Internet Use Score 1.44 (0.99–2.11), 0.058 1.84 (1.01–3.36), 0.048 2.05 (0.97–4.34), 0.061 2.11 (1.27–3.50), 0.004 1.68 (0.73–3.84), 0.229 3.41 (1.47–7.90), 0.004
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Total number of social media sites used.

Total number of social media sites used was significantly associated with higher odds of ever using cannabis (aOR: 1.22, CI: 1.05–1.42), cigarettes (aOR: 1.30, CI: 1.05–1.61), e-cigarettes (aOR: 1.26, CI: 1.11–1.42), and multiple substance use (aOR: 1.27, CI: 1.08–1.50).

Frequency of checking social media.

Checking social media every hour or more was significantly associated with higher odds of ever using alcohol (aOR: 1.89, CI: 1.08–3.32).

Problematic Internet Use.

Problematic internet use score was significantly associated with higher odds of ever using cannabis (aOR: 1.84, CI: 1.01–3.36), e-cigarettes (aOR: 2.11, CI: 1.27–3.50), and multiple substance use (aOR: 3.41, CI: 1.47–7.90).

Online social support seeking.

Online social support seeking score was not significantly associated with any of the substance use outcomes.

Conclusions

Overall, each of our different measures of social media use was positively associated with a higher odds of having ever used different substances, with the exception of online social support seeking. Across the various substance use outcomes, there are more similarities than differences in what is predicted, and total social media use seems to matter as much as seeing social media posts about drugs or drinking. This is likely due to the varying social contexts and unique cultures that adolescents associate with specific types of substances.30 For example, adolescents have different peer and social settings and in which they use different types of substances. Identifying the social media-related risk factors for each of the difference substance use outcomes can help with designing and tailoring interventions to address different types of youth substance use.20

Overall, we did find that social media use and substance use are closely linked behaviors among youth. This may be due to the fact that those who are more connected to their peers are likely to also participate in engage in activities similar to those peers.46,47 Youth who use substances are likely to have peers who use substances, and therefore will see it on social media.27,48 We also found that risk factors for different substance use outcomes were not the same across the various substances. For example, seeing social media posts about drugs or alcohol was associated with increased odds of ever using alcohol, cannabis, e-cigarettes, and poly-use, but not for cigarettes or prescription drugs. Different types of substances may be discussed differently online and have varying appeal towards youth, supporting previous qualitative studies.27,30

Even thought our study does not address causality, the direction of exposure to behavior is well supported theoretically and in other media effects work.11,14,21,33 It seems relatively unlikely that substance use would somehow drive social media use, but a third variable explanation is certainly reasonable. Interest in substances, existing exposures to substances through family and friends, and other similar variables could explain both social media use that includes substances use, and substance use itself.49,50 Even in this case, the social media exposure would support the driving beliefs thereby potentially strengthening them and encouraging behavior. Future research should continue to investigate the complex interplay among these variables and interventions should recognize how existing attitudes about substance use likely alter the effectiveness of the programs.

Results from our study can help inform substance use prevention efforts. Education interventions in this early should begin early, ideally in elementary school before youth are using social media and substances.51,52 These programs will likely be most effective if they include units addressing the role social media can play in encouraging initiation and perpetuation of substance use. By starting at an early age and including modern influences of substance use, contemporary approaches to interventions can hopefully help reduce any potential negative impacts of social media on children’s use of illicit substances.

Although our study is novel in looking at middle and high school-aged youth’s social media and substance use, it does have its limitations. We only asked if participants had ever tried a substance, so we do not have more granular information of when they last used the substance or if they were currently using. Moreover, the social media exposure variable related to seeing social media posts about drugs or alcohol asked about past 12-month exposure, which did not align with our timeframe of lifetime substance use. For any specific participant, their reported substance, therefore, could have preceded their reported exposure to social media posts about substance use, also limiting our ability to conduct a longitudinal analysis with the two timepoints. While our study provides valuable observations about the associations between social media use and substance use, the key direction for future research is to utilize more precise measures of use and exposure and more thoroughly explore the causal relationship between these variables. We acknowledge that in treating our sample as a single population, we are unable to account for individual differences related to substance use, such as immigration status and cultural context. However, we did adjust for race, ethnicity, and parental education in our models, as factors that may influence youth substance use. There may have been an influence of the study period, which included the COVID-19 pandemic. We also do not know if the type of peer mattered for the social media exposures, that is, older peers or those with weak ties. Finally, although our exploratory study included data from two timepoints, we did not assess the order and timing of substance use outcomes with social media exposures, thereby limiting our ability to identify a temporal relationship that could be stronger evidence of a causal relationship between different types of social media and substance use.

The consistent link between social media use and substance use is enough to encourage screening efforts to identify youth at greatest risk for potentially initiating substance use. Clinicians should continue to partner with schools to adequately carry out this screening and better connect youth with the resources they need to navigate adolescents and live healthier lifestyles. Our findings highlight the need for continued research in this area to better understand the causal direction of social media use and substance use.

Supplementary Material

Supplemental Table 1

Footnotes

Declarations of Interest Statement:

All authors report no conflicts of interest.

References:

  • 1.National Institute on Drug Abuse (NIDA). Monitoring the Future. Published online 2021. [Google Scholar]
  • 2.Miech R, Patrick ME, Keyes K, O’Malley PM, Johnston L. Adolescent drug use before and during U.S. national COVID-19 social distancing policies. Drug Alcohol Depend. 2021;226:108822. doi: 10.1016/j.drugalcdep.2021.108822 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Villanti AC, Johnson AL, Ilakkuvan V, Jacobs MA, Graham AL, Rath JM. Social Media Use and Access to Digital Technology in US Young Adults in 2016. J Med Internet Res. 2017;19(6):e196. doi: 10.2196/jmir.7303 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Anderson Monica; Teens Jiang J., Social Media & Technology 2018 | Pew Research Center. Published 2018. Accessed March 6, 2020. https://www.pewresearch.org/internet/2018/05/31/teens-social-media-technology-2018/
  • 5.Vogels EA, Gelles-Watnick R, Massarat N. Teens, Social Media and Technology 2022. Pew Research Center. [Google Scholar]
  • 6.Cipolletta S, Malighetti C, Cenedese C, Spoto A. How Can Adolescents Benefit from the Use of Social Networks? The iGeneration on Instagram. Int J Environ Res Public Health. 2020;17(19). doi: 10.3390/ijerph17196952 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Hamilton JL, Nesi J, Choukas-Bradley S. Reexamining Social Media and Socioemotional Well-Being Among Adolescents Through the Lens of the COVID-19 Pandemic: A Theoretical Review and Directions for Future Research. Perspect Psychol Sci. 2022;17(3):662–679. doi: 10.1177/17456916211014189 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Fergie G, Hunt K, Hilton S. Social media as a space for support: Young adults’ perspectives on producing and consuming user-generated content about diabetes and mental health. Soc Sci Med. 2016;170:46–54. doi: 10.1016/j.socscimed.2016.10.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Abi-Jaoude E, Naylor KT, Pignatiello A. Smartphones, social media use and youth mental health. CMAJ. 2020;192(6):E136–E141. doi: 10.1503/cmaj.190434 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Schønning V, Hjetland GJ, Aarø LE, Skogen JC. Social Media Use and Mental Health and Well-Being Among Adolescents - A Scoping Review. Front Psychol. 2020;11:1949. doi: 10.3389/fpsyg.2020.01949 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Pokhrel P, Fagan P, Herzog TA, et al. Social media e-cigarette exposure and e-cigarette expectancies and use among young adults. Addictive Behaviors. 2018;78:51–58. doi: 10.1016/j.addbeh.2017.10.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Schaefer DR, Adams J, Haas SA. Social networks and smoking: exploring the effects of peer influence and smoker popularity through simulations. Health Educ Behav. 2013;40(1 Suppl):24S–32S. doi: 10.1177/1090198113493091 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Crosnoe R, Johnson MK. Research on Adolescence in the Twenty-First Century. Annu Rev Sociol. 2011;37:439–460. doi: 10.1146/annurev-soc-081309-150008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Camenga D, Gutierrez KM, Kong G, Cavallo D, Simon P, Krishnan-Sarin S. E-cigarette advertising exposure in e-cigarette naïve adolescents and subsequent e-cigarette use: A longitudinal cohort study. Addictive Behaviors. 2018;81:78–83. doi: 10.1016/j.addbeh.2018.02.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Ling PM, Glantz SA. Why and How the Tobacco Industry Sells Cigarettes to Young Adults: Evidence From Industry Documents. Am J Public Health. 2002;92(6):908. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Riehm KE, Feder KA, Tormohlen KN, et al. Associations between Time Spent Using Social Media and Internalizing and Externalizing Problems among US Youth. JAMA Psychiatry. 2019;76(12):1266–1273. doi: 10.1001/jamapsychiatry.2019.2325 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Lenhart A, Perrin A, Assistant R, Stepler R, Rainie L, Parker K. About This Report; 2015. www.pewresearch.orghttp://www.pewinternet.org/2015/04/09/teens-social-media-technology-2015.
  • 18.Nesi J, Choukas-Bradley S, Prinstein MJ. Transformation of Adolescent Peer Relations in the Social Media Context: Part 2—Application to Peer Group Processes and Future Directions for Research. Clin Child Fam Psychol Rev. 2018;21(3):295–319. doi: 10.1007/s10567-018-0262-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Crone EA, Konijn EA. Media use and brain development during adolescence. Nat Commun. 2018;9(1). doi: 10.1038/s41467-018-03126-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Jackson KM, Janssen T, Gabrielli J. Media/Marketing Influences on Adolescent and Young Adult Substance Abuse. Curr Addict Rep. 2018;5(2):146–157. doi: 10.1007/s40429-018-0199-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Davis JP, Pedersen ER, Tucker JS, et al. Long-term Associations Between Substance Use-Related Media Exposure, Descriptive Norms, and Alcohol Use from Adolescence to Young Adulthood. J Youth Adolesc. 2019;48(7):1311–1326. doi: 10.1007/s10964-019-01024-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Romer D, Moreno M. Digital Media and Risks for Adolescent Substance Abuse and Problematic Gambling. Vol 140.; 2017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Evans W, Andrade E, Goldmeer S, Smith M, Snider J, Girardo G. The living the example social media substance use prevention program: A pilot evaluation. JMIR Ment Health. 2017;4(2). doi: 10.2196/mental.7839 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Evans W, Andrade E, Pratt M, et al. Peer-to-peer social media as an effective prevention strategy: Quasi-experimental evaluation. JMIR Mhealth Uhealth. 2020;8(5). doi: 10.2196/16207 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Kavuluru R, Han S, Hahn EJ. On the popularity of the USB flash drive-shaped electronic cigarette Juul. Tob Control. 2019;28(1):110–112. doi: 10.1136/tobaccocontrol-2018-054259 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Liu J, Vázquez-Otero C, Berman ML, Stevens EM. Youth-appealing features in popular e-cigarette brand advertising in the USA after heightened scrutiny in 2018. Tob Control. Published online 2021. doi: 10.1136/tobaccocontrol-2021-056720 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Liu J, McLaughlin S, Lazaro A, Halpern-Felsher B. What Does It Meme? A Qualitative Analysis of Adolescents’ Perceptions of Tobacco and Marijuana Messaging. Public Health Reports. Published online 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Geusens F, Lewis MA, Dumas TM, Litt DM. First Comes Substance Use, Then Comes Social Media Posts? Examining the Temporal Ordering and Relative Strength of Relations Across Alcohol, Tobacco and Marijuana Use and Posting Behavior. Health Commun. Published online 2023. doi: 10.1080/10410236.2023.2207241 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Bhatia D, Lewis B, Farrior H, Moore A, Nixon SJ. Substance familiarity in middle childhood and adolescent substance use. Drug Alcohol Depend. 2023;250. doi: 10.1016/j.drugalcdep.2023.110892 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Liu J, Ramamurthi D, Halpern-Felsher B. Inside the adolescent voice: A qualitative analysis of the appeal of different tobacco products. Tob Induc Dis. 2021;19:1–10. doi: 10.18332/TID/132856 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Tanski S, Emond J, Stanton C, et al. Youth Access to Tobacco Products in the United States: Findings From Wave 1 (2013–2014) of the Population Assessment of Tobacco and Health Study. Nicotine Tob Res. 2019;21(12):1695–1699. doi: 10.1093/ntr/nty238 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Bernat D, Gasquet N, Wilson KOD, Porter L, Choi K. Electronic Cigarette Harm and Benefit Perceptions and Use Among Youth. Am J Prev Med. 2018;55(3):361–367. doi: 10.1016/j.amepre.2018.04.043 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Huang GC, Unger JB, Soto D, et al. Peer influences: The impact of online and offline friendship networks on adolescent smoking and alcohol use. Journal of Adolescent Health. 2014;54(5):508–514. doi: 10.1016/j.jadohealth.2013.07.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Buja A, Gallimberti L, Chindamo S, et al. Problematic social networking site usage and substance use by young adolescents. BMC Pediatr. 2018;18(1):367. doi: 10.1186/s12887-018-1316-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Nesi J, Rothenberg WA, Hussong AM, Jackson KM. Friends’ Alcohol-Related Social Networking Site Activity Predicts Escalations in Adolescent Drinking: Mediation by Peer Norms. J Adolesc Health. 2017;60(6):641–647. doi: 10.1016/j.jadohealth.2017.01.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Sullivan RM, Wade NE, Wallace AL, et al. Substance use patterns in 9 to 13-year-olds: Longitudinal findings from the Adolescent Brain Cognitive Development (ABCD) study. Drug and Alcohol Dependence Reports. 2022;5:100120. doi: 10.1016/j.dadr.2022.100120 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Charmaraman L, Lynch AD, Richer AM, Grossman JM. Associations of early social media initiation on digital behaviors and the moderating role of limiting use. Comput Human Behav. 2022;127:107053. doi: 10.1016/j.chb.2021.107053 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Charmaraman L, Lynch AD, Richer AM, Zhai E. Examining Early Adolescent Positive and Negative Social Technology Behaviors and Well-Being During the COVID-19 Pandemic. Technology, Mind, and Behavior. 2022;3(1). doi: 10.1037/tmb0000062 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Charmaraman L, Hodes R, Richer AM. Young sexual minority adolescent experiences of self-expression and isolation on social media: Cross-sectional survey study. JMIR Ment Health. 2021;8(9). doi: 10.2196/26207 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Moreno MA, Arseniev-Koehler A, Selkie E. Development and Testing of a 3-Item Screening Tool for Problematic Internet Use. J Pediatr. 2016;176:167–172.e1. doi: 10.1016/j.jpeds.2016.05.067 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.McCloskey W, Iwanicki S, Lauterbach D, Giammittorio DM, Maxwell K. Are Facebook “friends” Helpful? Development of a Facebook-Based Measure of Social Support and Examination of Relationships among Depression, Quality of Life, and Social Support. Cyberpsychol Behav Soc Netw. 2015;18(9):499–505. doi: 10.1089/cyber.2014.0538 [DOI] [PubMed] [Google Scholar]
  • 42.Nelemans SA, Meeus WHJ, Branje SJT, et al. Social Anxiety Scale for Adolescents (SAS-A) Short Form: Longitudinal Measurement Invariance in Two Community Samples of Youth. Assessment. 2019;26(2):235–248. doi: 10.1177/1073191116685808 [DOI] [PubMed] [Google Scholar]
  • 43.Hughes ME, Waite LJ, Hawkley LC, Cacioppo JT. A Short Scale for Measuring Loneliness in Large Surveys: Results From Two Population-Based Studies NIH Public Access. Vol 26.; 1999. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.R Team Core. R: A language and environment for statistical computing. Published online 2020. [Google Scholar]
  • 45.Dziura JD, Post LA, Zhao Q, Fu Z, Peduzzi P. Strategies for dealing with missing data in clinical trials: from design to analysis. Yale J Biol Med. 2013;86(3):343–358. [PMC free article] [PubMed] [Google Scholar]
  • 46.Gardner M, Steinberg L. Peer influence on risk taking, risk preference, and risky decision making in adolescence and adulthood: an experimental study. Dev Psychol. 2005;41(4):625–635. doi: 10.1037/0012-1649.41.4.625 [DOI] [PubMed] [Google Scholar]
  • 47.Kaur N, Rutherford CG, Martins SS, Keyes KM. Associations between digital technology and substance use among U.S. adolescents: Results from the 2018 Monitoring the Future survey. Drug Alcohol Depend. 2020;213:108124. doi: 10.1016/j.drugalcdep.2020.108124 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Litt DM, Stock ML. Adolescent alcohol-related risk cognitions: the roles of social norms and social networking sites. Psychol Addict Behav. 2011;25(4):708–713. doi: 10.1037/a0024226 [DOI] [PubMed] [Google Scholar]
  • 49.Liu J, Tan ASL, Winickoff JP, Rees VW. Correlates of adolescent sole-, dual- and poly-use of cannabis, vaped nicotine, and combusted tobacco. Addictive Behaviors. 2023;146. doi: 10.1016/j.addbeh.2023.107804 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Herold R, Boykan R, Eliscu A, Alcalá HE, Goniewicz ML. Association between Friends’ Use of Nicotine and Cannabis and Intake of both Substances among Adolescents. Int J Environ Res Public Health. 2021;18(2):695. doi: 10.3390/ijerph18020695 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Liu J, Gaiha SM, Halpern-Felsher B. A Breath of Knowledge: Overview of Current Adolescent E-cigarette Prevention and Cessation Programs. Curr Addict Rep. 2020;7(4):520–532. doi: 10.1007/s40429-020-00345-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Liu J, Gaiha SM, Halpern-Felsher B. School-based programs to prevent adolescent e-cigarette use: A report card. Curr Probl Pediatr Adolesc Health Care. 2022;52(6):101204. doi: 10.1016/j.cppeds.2022.101204 [DOI] [PMC free article] [PubMed] [Google Scholar]

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Supplemental Table 1
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