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Journal of Studies on Alcohol and Drugs logoLink to Journal of Studies on Alcohol and Drugs
. 2023 Oct 15;84(5):700–709. doi: 10.15288/jsad.23-00061

Experimental Test of Abstaining-and-Drinking Social Media Content on Adolescent and Young Adult Social Norms and Alcohol Use

Dana M Litt a,,*, Zhengyang Zhou b, Anne M Fairlie c, Katja A Waldron d, Femke Geusens e,,f,, Melissa A Lewis a
PMCID: PMC10600970  PMID: 37306372

Abstract

Objective:

Experimental research has demonstrated that when alcohol-related content is viewed on social media, adolescents and young adults tend to have favorable attitudes toward alcohol use. However, limited research focuses on social media norms for abstaining from alcohol use. The current study examined the role of descriptive and injunctive alcohol-abstaining-and-drinking norms via experimentally manipulated social media profiles. Experimental effects on descriptive and injunctive normative perceptions and subsequent behavior were tested.

Method:

Participants (N = 306; ages 15–20 years) were recruited from the Seattle metropolitan area to complete a baseline survey and view researcher-fabricated social media profiles. Using stratified random assignment (birth sex and age), participants were randomized into one of three conditions: (a) alcohol abstaining and drinking, (b) alcohol abstaining, and (c) attention control.

Results:

The alcohol-abstaining-and-drinking condition reported greater drinking descriptive norms compared with participants in either the alcohol-abstaining or the attention-control conditions at post-experiment and 1-month follow-up. The alcohol-abstaining-and-drinking condition reported lower abstaining descriptive norms (i.e., perceiving fewer peers abstain) compared with those in the alcohol-abstaining condition at post-experiment and lower abstaining injunctive norms compared with those in the attention-control condition at 1-month follow-up.

Conclusions:

Exposure to social media profiles containing both alcohol-drinking and alcohol-abstaining messages was respectively associated with individuals perceiving that peers were consuming alcohol more often and that fewer peers were abstaining. The present findings are consistent with prior experimental research that indicates alcohol displays on social media are associated with riskier drinking cognitions.

In the United States, adolescent and young adult alcohol use remains a major public health concern (Substance Abuse and Mental Health Services Administration [SAMHSA], 2021). Research on the initiation and progression of alcohol use indicates that most adolescents and young adults experiment with alcohol, which can lead to hazardous alcohol use in young adulthood (Bolland et al., 2016; Hingson et al., 2002, 2003). Accordingly, identifying factors associated with alcohol use in this age group may help inform the development and refinement of effective alcohol prevention programs.

Social media and alcohol use

Exposure to social media (e.g., Facebook, Twitter, Insta-gram, Snapchat, TikTok) has been associated with increased adolescent and young adult alcohol use (Litt & Stock, 2011; Litt et al., 2018; Moreno et al., 2016). Social media are platforms on which adolescents and young adults may be exposed to peers’ drinking behavior (Davis et al., 2019; LaBrie et al., 2021; Steers et al., 2019). Most teens (90%) actively use at least one social media platform (American Academy of Child & Adolescent Psychiatry, 2018; LaBrie et al., 2021; Merrill et al., 2020), and most alcohol-related content on social media is favorable toward heavy alcohol use (Litt et al., 2018; Moreno et al., 2016; Russell et al., 2021). Studies demonstrate that viewing alcohol displays on social media is associated with risky drinking cognitions, heavy alcohol use, and harmful consequences (Geusens & Beullens, 2017; Hoffman et al., 2017; Litt et al., 2018; Nesi et al., 2017), suggesting the importance of considering alcohol-related social media exposure as a risk factor for alcohol use among adolescents and young adults. Of note, it is unclear whether exposure to alcohol-abstaining social media content leads to reductions in drinking. We are aware of only one study that explicitly examined abstinence displays on social media (Moreno et al., 2012), indicating that these displays were less common than alcohol displays among college students. Adolescents and young adults are exposed to social media that depicts both alcohol use and abstention, making it crucial to understand whether the presence of abstaining content reduces the negative influence of alcohol content.

Alcohol abstaining and alcohol use norms

Alcohol use during adolescence and young adulthood typically occurs in the context of peers (e.g., Barnes et al., 2006; Lipperman-Kreda et al., 2018). Thus, peer influences are frequently included in theoretical models related to substance use (Lewis et al., 2010b). Specific to adolescents and young adults, the Prototype Willingness Model (Gerrard et al., 2008) aims to explain decisions to engage in health-risk behaviors and conceptualizes descriptive and injunctive norms as key antecedents to behavior. Research indicates that perceived descriptive (e.g., perceptions of how many peers engage in drinking) and injunctive drinking norms (e.g., perceptions of peer approval of drinking) are positively associated with alcohol use (Fairlie et al., 2021; Lewis et al., 2020; Litt et al., 2019). There is limited research examining norms for abstaining from drinking (Lewis et al., 2017; Litt & Lewis, 2015; Meisel et al., 2016). Given that adolescents and young adults hold simultaneous motives to engage in and inhibit their alcohol use (Anderson et al., 2013), research examining normative perceptions should consider norms for using and abstaining from alcohol.

Cross-sectional (Boyle et al., 2018; Litt et al., 2018), longitudinal (Boers et al., 2020; Davis et al., 2019; Geusens et al., 2020), and experimental (Fournier et al., 2013; Litt & Stock, 2011) studies have found associations between exposure to alcohol-related social media posts and normative drinking perceptions. Social media may inherently convey both descriptive norms (e.g., perceptions of how many peers drink alcohol based on how many posts are viewed) and injunctive norms (e.g., perceptions of how many peers approve of drinking based on likes and positive feedback on alcohol posts) (Boyle et al., 2016; Geusens et al., 2020). Social media exposure may increase normative beliefs about alcohol use and in turn contribute to increased alcohol consumption (Boyle et al., 2016; Litt & Stock, 2011; Nesi et al., 2017).

Despite evidence that alcohol content on social media portrays both descriptive and injunctive norms, there remain unanswered questions about the effects of both abstaining and drinking descriptive and injunctive norms for alcohol use on social media. A study that experimentally manipulates or measures the effects of abstaining posts on normative perceptions would provide a more comprehensive understanding of social media's role in young people's drinking. Research shows that correcting overestimated normative perceptions for drinking can lead to decreased norms and drinking (Larimer et al., 2007; Lewis & Neighbors, 2006, 2007; Neighbors et al., 2010; Reid & Carey, 2015). Thus, it is possible that exposure to content that highlights norms for abstaining may serve to correct normative perceptions and in turn reduce drinking behavior. Testing this proposition will provide new perspectives to inform social media intervention strategies.

Current study

The current study aims to (a) examine the role of descriptive and injunctive alcohol-abstaining and drinking norms via experimentally manipulated social media profiles and (b) test experimental effects on descriptive and injunctive normative perceptions and subsequent behavior. We hypothesized significant main effects of the experimental manipulation at immediate post-experiment and 1-month follow-up. First, we hypothesized that participants who are exposed to social media profiles containing both alcohol-abstaining and alcohol-drinking content would report (H1a) higher descriptive norms for drinking and (H1b) higher injunctive norms for drinking compared with participants who are only exposed to profiles containing alcohol-abstaining posts and participants in the attention-control condition at post-experiment and 1-month follow-up. Further, we hypothesized that participants who are exposed to social media profiles containing both alcohol-abstaining and alcohol-drinking content would report (H2a) lower descriptive norms for abstaining and (H2b) lower injunctive norms for abstaining compared with participants who are only exposed to profiles containing alcohol-abstaining posts and participants in the attention-control condition at post-experiment and 1-month follow-up. Finally, we hypothesized that participants who are exposed to social media profiles containing both alcohol-abstaining and alcohol-drinking content would report (H3) heavier drinking behavior compared with participants who are only exposed to profiles containing alcohol-abstaining posts and participants in the attention-control condition at 1-month follow-up.

Method

Participants and procedures

Recruitment was conducted in the Seattle area through various methods including online and in-person recruitment, print advertisements, and friend referrals. Of those who completed the screening survey, 543 of 1,017 (53.4%) met eligibility criteria for the study, which included (a) being 15 to 20 years old; (b) living in the Seattle metro area; (c) if age 18–20, drinking at least once within the past 6 months; (d) using Facebook, Snapchat, or Instagram at least weekly; (e) being willing to attend two in-person sessions; and (f) obtaining parental consent if under age 18. Parental consent was obtained from 93% of those 15- to 17-year-olds who expressed interest in the study.

Of the 543 participants who met screening criteria, 344 (63.3%) successfully completed a phone verification call, and of those, 306 (88.9%) completed the baseline survey and in-person session. At baseline, the mean age was 18.38 years (SD = 1.32) and 53% of the sample identified their birth sex as female. Ethnic and racial representation of the sample was 50.3% non-Hispanic White, 27.5% non-Hispanic Asian, 10.1% more than one race, 6.9% Hispanic, 3.2% non-Hispanic Black, and 2.0% another race. Current education status consisted of 77.1% attending a 4-year university, 19.1% attending high school, 1.6% attending a community college, 1.6% not enrolled in any form of school, 0.3% attending a vocational or technical school, and 0.3% pursuing a General Educational Development (GED) credential.

Procedures

Consent and randomization. At the start of the in-person session, participants were presented with a full information statement, and those who agreed to participate were routed to the baseline survey. Immediately after completing the baseline survey, participants were randomized (using stratified random assignment based on birth sex and age) into one of three conditions: (a) alcohol abstaining and drinking, (b) alcohol abstaining, and (c) attention control.

Experimental manipulation. Participants in each condi tion viewed a series of experimentally manipulated social media profiles with equal numbers of photos, comments, group memberships, and items “liked” across profiles. Both descriptive and injunctive norms were targeted across both alcohol-abstaining-and-drinking and alcohol-abstaining profiles. Descriptive norms were displayed via photos and text that portrayed either abstaining (e.g., a comment on a photo about not needing to drink to have fun or a photo of a clearly identified mocktail) or drinking (e.g., young adults playing drinking games at a party). Injunctive norms were conveyed via the number of reactions (e.g., likes) across posts.

All participants viewed four social media profiles, including one Snapchat profile, one Facebook profile, and one Instagram profile, with the fourth profile's social media platform being randomly selected. Participants assigned to the alcohol-abstaining-and-drinking condition viewed three profiles that contained references to both abstaining from and using alcohol within the same profile and one control profile (75% of the profiles contained both alcohol-abstaining and alcohol-drinking content); abstaining-and-drinking profiles included images of alcohol use (e.g., beer cans, liquor bottles) and non-use (e.g., pictures of clearly identified mocktails), and the content (e.g., posts, “likes”) addressed both alcohol use and non-use. Participants assigned to the alcohol-abstaining condition viewed three profiles showing alcohol non-use and one control profile (75% of the profiles contained alcohol-abstaining references); photos were explicitly of abstaining from alcohol (e.g., holding signs that reflect desire to abstain, “liking” posts about abstaining from alcohol). For those assigned to the attention-control condition, all photos, comments, and group memberships were devoid of any alcohol-related content (drinking or abstention). For both experimental conditions, the order of profiles (experimental vs. control) and the platform type (Snapchat, Instagram, Facebook) were counterbalanced across all profiles. Similar strategies have been used in experimental research examining the impact of social media profiles on substance use among adolescents and young adults to conceal the true purpose of the study and reflect realistic variation in social media content among this age group (Litt & Stock, 2011; Vogel et al., 2021). To control for attractiveness and similarity, the person portrayed in the social media photos was the same across conditions, and participants viewed same-sex profiles in line with their own birth sex (male or female).

After viewing all four profiles, participants rated the profiles on several dimensions (as part of the cover story that we were studying social media, personality, and health), completed a manipulation check, and answered post-experiment questions regarding their alcohol-related cognitions and behaviors. Total time for baseline assessment and experimental manipulation with profile viewing was 45 minutes, and participants were paid $30 by Visa gift card.

One-month follow-up. A total of 293 participants (95.8%) completed the 1-month follow-up survey. Participants were invited to complete the 1-month follow-up in the lab, and if they did not schedule a session or missed their session and did not reschedule, they were sent a link to complete the 1-month follow-up survey online. Of participants who completed the 1-month follow-up assessment, 263 (89.8%) completed it in-lab and 30 (10.2%) completed it online. After the assessment, all participants read a debriefing statement that explained the purpose of the study, the necessity of using deception, and contact information if participants wanted to speak with someone about the study. All participants were directed to a brief personalized normative feedback alcohol intervention. Parents of participants ages 15–17 who provided consent were emailed a debriefing statement and a parent manual (Turrisi et al., 2013). Participants who did not complete the 1-month follow-up survey (n = 24) were emailed the debriefing information and a link to complete the personalized normative feedback intervention.

The 1-month assessment, debriefing, and personalized normative feedback took approximately 45 minutes, and participants were paid $45 with a Visa gift card. A Federal Certificate of Confidentiality was obtained to help ensure privacy of research participants. All study procedures were approved by the university's institutional review board, and no adverse events were reported.

Measures

Measures were administered at baseline, post-experiment, and 1 month unless otherwise noted.

Drinking descriptive norm. Participants responded to an item that read, “How many days per month do you think the typical [male/female] your age drinks alcohol?” using an open-ended response capped at 31 days (adapted from SAMHSA, 2019).

Drinking injunctive norm. In a question parallel to the drinking descriptive norm, participants were asked, “How many days per month do you think the typical [male/female] your age thinks are acceptable to drink alcohol?” using an open-ended response capped at 31 days (adapted from SAMHSA, 2019).

Abstaining descriptive norm. Participants were asked, “What percentage of typical [males/females] your age do you think have never drank alcohol?” on a scale from 0 to 100% (Litt & Lewis, 2015; Litt & Stock, 2011).

Abstaining injunctive norm. Participants responded to a single item on a Likert scale (1 = strongly disapprove to 7 = strongly approve) (Lewis et al., 2010a) that asked, “Please indicate to what extent you think the typical [male/female] your age would approve or disapprove of never drinking?”

Drinking outcomes. At baseline and 1 month, participants were asked, “During the last month, on how many days did you drink alcohol?” using an open-ended response capped at 31 days (adapted from SAMHSA, 2019). Participants also responded to a single item from the Daily Drinking Questionnaire (Collins et al., 1985) that assessed drinking frequency: “On average, during the past month, how often have you consumed alcohol?” using an 11-point scale (0 = never to 10 = seven times per week).

Manipulation check. Several questions were asked at post-experiment to test the experimental manipulation. First, participants were asked, “How many profiles did you view?” with options ranging from 1 to 4. Next, participants were asked, “Were the profiles you viewed male (0) or female (1)?” Finally, they were asked, “What % of the profiles you viewed contained references to alcohol?” selecting 0 (0%), 1 (25%), 2 (50%), 3 (75%), or 4 (100%). For each item, correct answers (based on birth sex and condition) were coded as “1” and incorrect answers were coded as “0.” A sum score was created and then dichotomized whereby participants who had a sum score of 0–1 correct were coded as 0 = did not pass manipulation check and those who had a sum of 2–3 correct were coded as 1 = passed manipulation check.

Demographics. Age and birth sex (0 = female, 1 = male) were included as covariates based on associations with substance use (Schulenberg et al., 2018).

Analytic plan

Descriptive statistics were examined separately for each experimental condition at baseline, post-experiment assessment, and 1-month follow-up. Kruskal–Wallis tested for baseline differences in outcomes across the three experimental conditions. To investigate immediate and short-term experimental effects, separate analyses were conducted to evaluate the effects using the post-experiment assessment and 1-month follow-up. The post-experiment analyses had the following four norms-related outcomes: descriptive norms for drinking days per month (H1a), injunctive norms for drinking days per month (H1b), descriptive norms for percentage of same-age peers who never drink alcohol (H2a), and injunctive norms for never drinking (H2b). The analyses for H3 had two drinking behavior outcomes from the 1-month follow-up: number of drinking days per month and drinking frequency. For this three-arm experiment, the alcohol-abstaining-and-drinking condition was set as the reference group because the comparison of interest was between the alcohol-abstaining-and-drinking condition and each of the remaining two conditions (i.e., alcohol-abstaining and attention-control). Multiple linear regression models (for continuous outcomes of perceived norms, H1a–b and H2a–b) and negative binomial regression models (for count outcomes of alcohol use, H3) were estimated to evaluate the experimental effects at the immediate post-experiment assessment and 1-month follow-up. Baseline drinking frequency, baseline level of the corresponding outcome, age, and birth sex were included as covariates to adjust for baseline difference and potential confounding. All analyses were limited to those with complete observations depending on the analysis (see sample size for each model in Tables 2–4).

Table 2.

Drinking norms outcomes: Results of multiple linear regression for evaluating experimental effects at post-experiment assessment and 1-month follow-up (H1a and H1b)

graphic file with name jsad.23-00061tbl2.jpg

Variable Post-experiment assessment 1-month follow-up
Descriptive norms: Percentagenever drinkalcohol ever(n = 303)b (SE) Injunctive norms: Neverdrinking (n = 302) b (SE) Descriptive norms: Percentagenever drinkalcohol ever (n = 277)b (SE) Injunctive norms: Neverdrinking (n = 277) b (SE)
Intercept -0.24 (2.12) -2.57 (2.66) -2.64 (2.88) -9.09 (4.78)
Baseline drinking frequency 0.14 (0.07) 0.01 (0.09) 0.14 (0.10) 0.10 (0.16)
Age 0.10 (0.12) 0.26 (0.15) 0.27 (0.16) 0.67 (0.27)*
Birth sex 0.37 (0.30) 0.24 (0.38) 0.96 (0.40)* 0.59 (0.67)
Attention-control condition vs. Alcohol-abstaining-and-drinking condition -0.77 (0.36)* -0.43 (0.46) -0.99 (0.48)* 0.55 (0.80)
Alcohol-abstaining condition vs. Alcohol-abstaining-and-drinking condition -1.15 (0.36)** -0.64 (0.45) -1.63 (0.47)*** -0.96 (0.79)
Baseline level of outcome 0.79 (0.04)*** 0.69 (0.03)*** 0.60 (0.05)*** 0.52 (0.06)***
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Notes: n = number of observations in the analysis; baseline level of outcome = the level of the outcome variable at baseline.

*

p < .05;

**

p < .01;

***

p < .001.

Table 4.

Drinking behavior outcomes: Results of negative binomial regression for evaluating experimental effects at 1-month follow-up (H3)

graphic file with name jsad.23-00061tbl4.jpg

Variable Drinking days per month (n = 280) b (SE) Drinking frequency (n = 280) b (SE)
Intercept -1.16 (0.75) -1.12 (0.58)
Baseline drinking frequency 0.26 (0.05*** 0.22 (0.02)***
Age 0.07 (0.04) 0.07 (0.03)*
Birth sex 0.12 (0.10) 0.13 (0.07)
Attention-control condition vs. Alcohol-abstaining-and-drinking condition -0.02 (0.12) 0.03 (0.08)
Alcohol-abstaining condition vs. Alcohol-abstaining-and-drinking condition 0.01 (0.12) -0.00 (0.08)
Baseline outcome level 0.03 (0.02) -
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Notes: n = number of observations in the analysis; baseline level of outcome = the level of the outcome variable at baseline.

*

p < .05;

***

p < .001.

Results

Manipulation check and test for baseline differences

Results indicated that 96.4% of participants at immediate post-experiment follow-up passed the manipulation check. The Kruskal–Wallis test did not yield any statistically signifi-cant baseline differences across the experimental conditions for descriptive norms for drinking days per month, percentage of peers who have never consumed alcohol, injunctive norms for drinking days per month, injunctive norms for never drinking, drinking days per month, or drinking frequency in the past month (Table 1).

Table 1.

Descriptive statistics on demographics and all outcomes by experimental condition at baseline, post-experiment assessment, and 1-month follow-up

graphic file with name jsad.23-00061tbl1.jpg

Variable Baseline Post-experiment assessment 1-month follow-up
Sample size - -
 Attention-control condition 99 99 94
 Alcohol-abstaining condition 103 103 98
 Alcohol-abstaining-and-drinking condition 104 104 101
Demographic information
 Age at baseline - -
  Attention-control condition 18.43 (1.30) - -
  Alcohol-abstaining condition 18.32 (1.34) - -
  Alcohol-abstaining-and-drinking condition 18.40 (1.33) - -
 Birth sex (% female)
  Attention-control condition 52.5% 52.5% 53.2%
  Alcohol-abstaining condition 53.4% 53.4% 53.1%
  Alcohol-abstaining-and-drinking condition 52.9% 52.9% 51.5%
Perceived norms
 Descriptive norms for drinking days per month
  Attention-control condition 8.10 (4.00) 7.85 (4.24) 6.97 (3.61)
  Alcohol-abstaining condition 7.30 (4.51) 6.80 (4.05) 5.92 (4.60)
  Alcohol-abstaining-and-drinking condition 7.47 (4.46) 8.12 (4.79) 7.74 (4.51)
 Descriptive norms for % never drink alcohol
  Attention-control condition 19.19 (13.81) 18.29 (12.79) 19.26 (14.98)
  Alcohol-abstaining condition 18.64(17.03) 19.49 (16.78) 20.78 (17.41)
  Alcohol-abstaining-and-drinking condition 18.06 (14.49) 16.32 (13.14) 17.40 (16.19)
 Injunctive norms for drinking days per month
  Attention-control condition 10.05 (6.29) 8.90 (5.70) 9.45 (8.26)
  Alcohol-abstaining condition 8.61 (5.25) 7.63 (4.62) 7.32 (5.08)
  Alcohol-abstaining-and-drinking condition 9.30 (5.57) 8.77 (5.01) 8.68 (5.24)
 Injunctive norms for never drinking
  Attention-control condition 3.77 (1.65) 4.10 (1.76) 4.15 (1.82)
  Alcohol-abstaining condition 4.19 (1.69) 4.32 (1.78) 4.31 (1.60)
  Alcohol-abstaining-and-drinking condition 3.86 (1.52) 4.00 (1.55) 3.76 (1.57)
Alcohol use behavior
 Drinking days per month
  Attention-control condition 5.70 (5.13) - 5.04 (5.02)
  Alcohol-abstaining condition 5.25 (5.56) - 4.89 (5.55)
  Alcohol-abstaining-and-drinking condition 5.13 (4.89) - 4.66 (4.96)
 Drinking frequency in last month
  Attention-control condition 3.40 (2.16) - 3.12 (2.22)
  Alcohol-abstaining condition 3.22 (2.09) - 2.93 (2.24)
  Alcohol-abstaining-and-drinking 3.30 (2.19) - 2.98 (2.19)
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Note: Alcohol use behavior was not assessed immediately post-experiment.

Descriptive information

Table 1 provides the sample size and descriptive statistics for each experimental condition as measured in baseline, the post-experiment survey, and 1-month follow-up.

Tests of H1a and H1b: Experimental effects on descriptive and injunctive norms

Table 2 shows the results for H1a and H1b testing the experimental effects on descriptive and injunctive norms for drinking comparing the alcohol-abstaining-and-drinking condition (reference) to both the alcohol-abstaining and attention-control conditions at post-experiment and 1-month follow-up. In support of H1a, results indicated that participants in the alcohol-abstaining-and-drinking condition reported greater drinking descriptive norms compared with participants in either the alcohol-abstaining or attention-control conditions at post-experiment and 1-month follow-up. However, no support was found for H1b whereby there were no significant differences among conditions for drinking injunctive norms at post-experiment or 1-month follow-up.

Tests of H2a and H2b: Experimental effects on descriptive and injunctive norms for abstaining

Table 3 shows the results for H2a and H2b testing the experimental effects on descriptive and injunctive norms for abstaining comparing the alcohol-abstaining-and-drinking condition (reference) to both the alcohol-abstaining and attention-control conditions at post-experiment and 1-month follow-up. In partial support of H2a, results indicated that participants in the alcohol-abstaining-and-drinking condition reported lower abstaining descriptive norms compared with those in the alcohol-abstaining condition at post-experiment. In partial support of H2b, results indicated that participants in the alcohol-abstaining-and-drinking condition reported lower abstaining injunctive norms compared with those in the attention-control condition at 1-month follow-up. No other comparisons of abstaining descriptive or injunctive norms between conditions were significant.

Table 3.

Abstaining norms outcomes: Results of multiple linear regression for evaluating experimental effects at post-experiment assessment and 1-month follow-up (H2a and H2b)

graphic file with name jsad.23-00061tbl3.jpg

Variable Post-experiment assessment 1-month follow-up
Descriptive norms: Percentagenever drinkalcohol ever(n = 303)b (SE) Injunctive norms: Neverdrinking(n = 302)b (SE) Descriptive norms: Percentagenever drinkalcohol ever(n = 277)b (SE) Injunctive norms: Neverdrinking(n = 277)b (SE)
Intercept 7.31 (8.73) 1.17(1.09) 16.16 (12.61) 2.84(1.19)*
Baseline drinking frequency -0.20 (0.29) 0.04 (0.04) -0.14(0.40) 0.07 (0.04)
Age -0.12 (0.47) 0.00 (0.06) -0.43 (0.68) -0.09 (0.06)
Birth sex -0.51 (1.18) -0.01 (0.15) -2.08 (1.65) -0.33 (0.16)*
Attention-control condition vs. Alcohol-abstaining-and-drinking condition 1.36 (1.42) 0.19(0.18) 0.92 (1.99) 0.40 (0.19)*
Alcohol-abstaining condition vs. Alcohol-abstaining-and-drinking condition 2.93 (1.41)* 0.12(0.18) 2.92 (1.98) 0.33 (0.19)
Baseline level of outcome 0.67 (0.04)*** 0.70 (0.05)*** 0.58 (0.06)*** 0.63 (0.05)***
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Notes: n = number of observations in the analysis; baseline level of outcome = the level of the outcome variable at baseline.

*

p < .05;

***

p < .001.

Tests of H3: Experimental effects on drinking behaviors

Table 4 shows the results for H3 testing the experimental effects on drinking behaviors comparing the alcohol-abstaining-and-drinking condition (reference) to both the alcohol-abstaining and attention-control conditions at 1-month follow-up. Contrary to hypotheses, no significant differences were found on drinking behaviors between conditions.

In all analyses for H1–H3, outcomes were positively associated with their corresponding baseline levels. Being older was significantly associated with higher injunctive norms for drinking days per month at the 1-month follow-up. Being male was significantly associated with higher descriptive norms for drinking days per month and drinking frequency at the 1-month follow-up, but being male was associated with lower injunctive norms for never drinking at the 1-month follow-up. No other covariates were significantly associated with outcomes.

Discussion

Prior longitudinal and experimental research has demonstrated that exposure to alcohol posts on social media affects young adults’ descriptive and injunctive normative perceptions (Geusens et al., 2020; Nesi et al., 2017; Vranken et al., 2020). Our study revealed that exposure to social media profiles containing both alcohol-drinking and alcohol-abstaining messages was associated with individuals perceiving that peers consumed alcohol more often. In addition, exposure to abstaining and drinking messages was associated with perceptions that fewer peers abstained, especially compared with seeing abstaining messages only.

Our findings further indicated that much of the change as a result of the experimental manipulation was on descriptive rather than injunctive norms. Research indicates that descriptive norms are more strongly associated with drinking than injunctive norms (Lac & Donaldson, 2018; Neighbors et al., 2008). It may be easier for adolescents and young adults to notice and recall what others are perceived to be doing rather than what others approve of. For example, if a photo is displayed on social media that shows one's peers at a party with alcohol, it may be easier to determine that others are drinking than to determine how approving others are of drinking from the photo.

Findings from the present study indicated that participants in the alcohol-abstaining-and-drinking condition reported lower abstaining descriptive norms (i.e., perceiving that fewer peers abstain) compared with those in the alcohol-abstaining condition at the post-experiment assessment and 1-month follow-up. Future research is needed to determine if viewing abstaining social media displays can help balance the negative impact of drinking social media displays on adolescent and young adult cognitions. Our study is the first experimental test for this line of research.

Examining abstaining social media displays is important because they may be a potential sign of not using alcohol and motivation to change or reduce current alcohol use. These abstaining displays may identify adolescents or young adults who could benefit from indicated behavioral change interventions. Recent research by Moreno et al. (2023) used social media alcohol displays as a means to deliver a targeted brief online alcohol intervention. Future research should examine potential abstaining alcohol posts as signals of motivation to change alcohol use behaviors.

We did not detect differences in drinking behavior outcomes at the 1-month follow-up. Personalized normative feedback interventions that include a normative component often compare one's own drinking behavior to the perceived behavior of others and others’ actual drinking (Lewis & Neighbors, 2006). The experimental manipulation tested only focused on manipulating normative behavior and not comparisons with one's own drinking, which may be why we did not detect changes in drinking outcomes.

Limitations and future directions

The current research is not without limitations. First, the social media profiles were created by the researchers; thus, exposure to known peers may have yielded stronger effects, as research indicates that similarity to normative referents is an important factor in predicting drinking (Lewis & Neighbors, 2007). This relates to potential limitations pertaining to gender, sexual orientation, race, and ethnicity as our profiles were only matched based on birth sex and therefore participants may not have felt similar to the normative referents used in our study. Further, we did not measure executive functioning and cannot determine whether some participants may have had difficulties understanding the perspective of others or the questions asked in our study. Next, at the time of data collection (2017), TikTok did not exist. Recent research indicates that alcohol and abstaining information is frequent on TikTok (Russell et al., 2021) and thus should be examined. Third, participants were exposed to four social media profiles, which is significantly less than typically encountered in the real world. It is possible that some of our null effects may be attributable to the limited exposure (i.e., dose) in our manipulation. We did not measure alcohol exposure on social media in our baseline survey, and as such could not account for this construct in our analyses. Intensive research designs that capture real-world exposure to both abstaining and drinking content on social media (e.g., ecological momentary assessment) would provide more nuanced data into how abstaining and drinking content may relate to behavior. Next, none of the experimental profiles contained alcohol or liquor brands; given the high rates of alcohol advertising (Barry et al., 2018) and impact on drinking behavior (Alhabash et al., 2016), future research could consider including advertisements as well. Finally, about half of the sample identified as White college students (50.3%) and as such, results may not generalize to more diverse samples in terms of race, ethnicity, and student status.

Conclusion

Exposure to social media profiles containing both alcohol-consumption and abstaining messages was associated with individuals perceiving that peers were consuming alcohol more often and that fewer peers were abstaining. The present findings confirm prior experimental research indicating that alcohol displays on social media are associated with riskier drinking cognitions. Much of the change attributable to experimental manipulation was on descriptive norms rather than injunctive norms. Perceptions of what others are doing may be easier for individuals to notice and recall compared with perceptions of others’ approval. Last, findings indicated that participants in the alcohol-abstaining-and-drinking condition reported lower abstaining descriptive norms compared with those in the alcohol-abstaining condition. Future research could elucidate whether viewing abstaining social media displays reduces the negative impact that social media alcohol displays have on individuals. Such findings could allow for screening individuals into targeted preventative interventions.

Footnotes

Data collection was supported by National Institute on Alcohol Abuse and Alcoholism (NIAAA) Grant R21AA024163 (awarded to Dana M. Litt). Manuscript preparation was supported by NIAAA Grants R34AA0263323 (awarded to Dana M. Litt) and F31AA029299 (awarded to KatjaA. Waldron). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the NIAAA or the National Institutes of Health.

References

  1. American Academy of Child & Adolescent Psychiatry. Social media and teens. 2018. Retrieved from https://www.aacap.org/AACAP/Families_and_Youth/Facts_for_Families/FFF-Guide/Social-Media-and-Teens-100.aspx.
  2. Alhabash S., McAlister A. R., Kim W., Lou C., Cunningham C., Quilliam E. T., Richards J. I. Saw it on Facebook, drank it at the bar! Effects of exposure to Facebook alcohol ads on alcohol-related behaviors. Journal of Interactive Advertising. 2016;16:44–58. doi:10.1080/15252019.2016.1160330. [Google Scholar]
  3. Anderson K. G., Briggs K. E. L., White H. R. Motives to drink or not to drink: Longitudinal relations among personality, motives, and alcohol use across adolescence and early adulthood. Alcoholism: Clinical and Experimental Research. 2013;37:860–867. doi: 10.1111/acer.12030. doi:10.1111/acer.12030. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Barnes G. M., Hoffman J. H., Welte J. W., Farrell M. P., Dintcheff B. A. Effects of parental monitoring and peer deviance on substance use and delinquency. Journal of Marriage and the Family. 2006;68:1084–1104. doi:10.1111/j.1741-3737.2006.00315.x. [Google Scholar]
  5. Barry A. E., Padon A. A., Whiteman S. D., Hicks K. K., Carreon A. K., Crowell J. R., Merianos A. L. Alcohol advertising on social media: Examining the content of popular alcohol brands on Instagram. Substance Use & Misuse. 2018;53:2413–2420. doi: 10.1080/10826084.2018.1482345. doi:10.1080/10826084.2018.1482345. [DOI] [PubMed] [Google Scholar]
  6. Boers E., Afzali M. H., Conrod P. A longitudinal study on the relationship between screen time and adolescent alcohol use: The mediating role of social norms. Preventive Medicine. 2020;132:105992. doi: 10.1016/j.ypmed.2020.105992. doi:10.1016/j.ypmed.2020.105992. [DOI] [PubMed] [Google Scholar]
  7. Bolland K. A., Bolland J. M., Tomek S., Devereaux R. S., Mrug S., Wimberly J. C. Trajectories of adolescent alcohol use by gender and early initiation status. Youth & Society. 2016;48:3–32. doi:10.1177/0044118X13475639. [Google Scholar]
  8. Boyle S. C., LaBrie J. W., Froidevaux N. M., Witkovic Y. D. Different digital paths to the keg? How exposure to peers’ alcohol-related social media content influences drinking among male and female first-year college students. Addictive Behaviors. 2016;57:21–29. doi: 10.1016/j.addbeh.2016.01.011. doi:10.1016/j.addbeh.2016.01.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Boyle S. C., Smith D. J., Earle A. M., LaBrie J. W. What “likes” have got to do with it: Exposure to peers’ alcohol-related posts and perceptions of injunctive drinking norms. Journal of American College Health. 2018;66:252–258. doi: 10.1080/07448481.2018.1431895. doi:10.1080/07448481.2018.1431895. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Collins R. L., Parks G. A., Marlatt G. A. Social determinants of alcohol consumption: The effects of social interaction and model status on the self-administration of alcohol. Journal of Consulting and Clinical Psychology. 1985;53:189–200. doi: 10.1037//0022-006x.53.2.189. doi:10.1037/0022-006X.53.2.189. [DOI] [PubMed] [Google Scholar]
  11. Davis J. P., Pedersen E. R., Tucker J. S., Dunbar M. S., Seelam R., Shih R., D’Amico E. J. Long-term associations between substance use-related media exposure, descriptive norms, and alcohol use from adolescence to young adulthood. Journal of Youth and Adolescence. 2019;48:1311–1326. doi: 10.1007/s10964-019-01024-z. doi:10.1007/s10964-019-01024-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Fairlie A. M., Lewis M. A., Waldron K. A., Wallace E. C., Lee C. M. Understanding perceived usefulness and actual use of protective behavioral strategies: The role of perceived norms for the reasons that young adult drinkers use protective behavioral strategies. Addictive Behaviors. 2021;112:106585. doi: 10.1016/j.addbeh.2020.106585. doi:10.1016/j.addbeh.2020.106585. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Fournier A. K., Hall E., Ricke P., Storey B. Alcohol and the social network: Online social networking sites and college students’ perceived drinking norms. Psychology of Popular Media Culture. 2013;2:86–95. doi:10.1037/a0032097. [Google Scholar]
  14. Gerrard M., Gibbons F. X., Houlihan A. E., Stock M. L., Pomery E. A. A dual-process approach to health risk decision making: The Prototype Willingness Model. Developmental Review. 2008;28:29–61. doi:10.1016/j.dr.2007.10.001. [Google Scholar]
  15. Geusens F., Beullens K. The reciprocal associations between sharing alcohol references on social networking sites and binge drinking: A longitudinal study among late adolescents. Computers in Human Behavior. 2017;73:499–506. doi:10.1016/j.chb.2017.03.062. [Google Scholar]
  16. Geusens F., Beullens K. The association between social networking sites and alcohol abuse among Belgian adolescents. Journal of Media Psychology. 2018;30:207–216. doi:10.1027/1864-1105/a000196. [Google Scholar]
  17. Geusens F., Bigman-Galimore C. A., Beullens K. A cross-cultural comparison of the processes underlying the associations between sharing of and exposure to alcohol references and drinking intentions. New Media & Society. 2020;22:49–69. doi:10.1177/1461444819860057. [Google Scholar]
  18. Hingson R. W., Heeren T., Zakocs R. C., Kopstein A., Wechsler H. Magnitude of alcohol-related mortality and morbidity among U.S. college students ages 18–24. Journal of Studies on Alcohol. 2002;63:136–144. doi: 10.15288/jsa.2002.63.136. doi:10.15288/jsa.2002.63.136. [DOI] [PubMed] [Google Scholar]
  19. Hingson R., Heeren T., Zakocs R., Winter M., Wechsler H. Age of first intoxication, heavy drinking, driving after drinking and risk of unintentional injury among U.S. college students. Journal of Studies on Alcohol. 2003;64:23–31. doi: 10.15288/jsa.2003.64.23. doi:10.15288/jsa.2003.64.23. [DOI] [PubMed] [Google Scholar]
  20. Hoffman E. W., Austin E. W., Pinkleton B. E., Austin B. W. An exploration of the associations of alcohol-related social media use and message interpretation outcomes to problem drinking among college students. Health Communication. 2017;32:864–871. doi: 10.1080/10410236.2016.1195677. doi:10.1080/10410236.2016.1195677. [DOI] [PubMed] [Google Scholar]
  21. LaBrie J. W., Trager B. M., Boyle S. C., Davis J. P., Earle A. M., Morgan R. M. An examination of the prospective associations between objectively assessed exposure to alcohol-related Instagram content, alcohol-specific cognitions, and first-year college drinking. Addictive Behaviors. 2021;119:106948. doi: 10.1016/j.addbeh.2021.106948. doi:10.1016/j.addbeh.2021.106948. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Lac A., Donaldson C. D. Testing competing models of injunctive and descriptive norms for proximal and distal reference groups on alcohol attitudes and behavior. Addictive Behaviors. 2018;78:153–159. doi: 10.1016/j.addbeh.2017.11.024. doi:10.1016/j.addbeh.2017.11.024. [DOI] [PubMed] [Google Scholar]
  23. Larimer M. E., Lee C. M., Kilmer J. R., Fabiano P. M., Stark C. B., Geisner I. M., Neighbors C. Personalized mailed feedback for college drinking prevention: A randomized clinical trial. Journal of Consulting and Clinical Psychology. 2007;75:285–293. doi: 10.1037/0022-006X.75.2.285. doi:10.1037/0022-006X.75.2.285. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Lewis M. A., Litt D. M., King K. M., Fairlie A. M., Waldron K. A., Garcia T. A., Lee C. M. Examining the ecological validity of the prototype willingness model for adolescent and young adult alcohol use. Psychology of Addictive Behaviors. 2020;34:293–302. doi: 10.1037/adb0000533. doi:10.1037/adb0000533. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Lewis M. A., Litt D. M., Tomkins M., Neighbors C. Prototype willingness model drinking cognitions mediate personalized normative feedback efficacy. Prevention Science. 2017;18:373–381. doi: 10.1007/s11121-016-0742-4. doi:10.1007/s11121-016-0742-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Lewis M. A., Neighbors C. Social norms approaches using descriptive drinking norms education: A review of the research on personalized normative feedback. Journal of American College Health. 2006;54:213–218. doi: 10.3200/JACH.54.4.213-218. doi:10.3200/JACH.54.4.213-218. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Lewis M. A., Neighbors C. Optimizing personalized normative feedback: The use of gender-specific referents. Journal of Studies on Alcohol and Drugs. 2007;68:228–237. doi: 10.15288/jsad.2007.68.228. doi:10.15288/jsad.2007.68.228. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Lewis M. A., Neighbors C., Geisner I. M., Lee C. M., Kilmer J. R., Atkins D. C. Examining the associations among severity of injunctive drinking norms, alcohol consumption, and alcohol-related negative consequences: The moderating roles of alcohol consumption and identity. Psychology of Addictive Behaviors. 2010a;24:177–189. doi: 10.1037/a0018302. doi:10.1037/a0018302. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Lewis M. A., Neighbors C., Lindgren K. P., Buckingham K. G., Hoang M. Hauppauge, NY: Nova Science Publishers, Inc; 2010b. Theories of social influence on adolescent and young adult alcohol use. [Google Scholar]
  30. Lipperman-Kreda S., Gruenewald P. J., Grube J. W., Bersamin M. Adolescents, alcohol, and marijuana: Context characteristics and problems associated with simultaneous use. Drug and Alcohol Dependence. 2017;179:55–60. doi: 10.1016/j.drugalcdep.2017.06.023. doi:10.1016/j.drugalcdep.2017.06.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Litt D. M., Lewis M. A. Examining the role of abstainer prototype favorability as a mediator of the abstainer-norms-drinking-behavior relationship. Psychology of Addictive Behaviors. 2015;29:467–472. doi: 10.1037/adb0000042. doi:10.1037/adb0000042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Litt D. M., Lewis M. A., Spiro E. S., Aulck L., Waldron K. A., Head-Corliss M. K., Swanson A. #drunktwitter: Examining the relations between alcohol-related Twitter content and alcohol willingness and use among underage young adults. Drug and Alcohol Dependence. 2018;193:75–82. doi: 10.1016/j.drugalcdep.2018.08.021. doi:10.1016/j.drugalcdep.2018.08.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Litt D. M., Stock M. L. Adolescent alcohol-related risk cognitions: The roles of social norms and social networking sites. Psychology of Addictive Behaviors. 2011;25:708–713. doi: 10.1037/a0024226. doi:10.1037/a0024226. [DOI] [PubMed] [Google Scholar]
  34. Litt D. M., Waldron K. A., Wallace E. C., Lewis M. A. Alcohol-specific social comparison as a moderator of the norms-behavior association for young adult alcohol use. Addictive Behaviors. 2019;90:92–98. doi: 10.1016/j.addbeh.2018.10.029. doi:10.1016/j.addbeh.2018.10.029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Meisel S. N., Colder C. R., Read J. P. Addressing inconsistencies in the social norms drinking literature: Development of Injunctive Norms Drinking and Abstaining Behaviors Questionnaire. Alcoholism: Clinical and Experimental Research. 2016;40:2218–2228. doi: 10.1111/acer.13202. doi:10.1111/acer.13202. [DOI] [PubMed] [Google Scholar]
  36. Merrill J. E., Ward R. M., Riordan B. C. Posting post-blackout: A qualitative examination of the positive and negative valence of tweets posted after “blackout” drinking. Journal of Health Communication. 2020;25:150–158. doi: 10.1080/10810730.2020.1719242. doi:10.1080/10810730.2020.1719242. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Moreno M. A., Arseniev-Koehler A., Litt D., Christakis D. Evaluating college students’ displayed alcohol references on Facebook and Twitter. Journal of Adolescent Health. 2016;58:527–532. doi: 10.1016/j.jadohealth.2016.01.005. doi:10.1016/j.jadohealth.2016.01.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Moreno M. A., Christakis D. A., Egan K. G., Brockman L. N., Becker T. Associations between displayed alcohol references on Facebook and problem drinking among college students. Archives of Pediatrics & Adolescent Medicine. 2012;166:157–163. doi: 10.1001/archpediatrics.2011.180. doi:10.1001/archpediatrics.2011.180. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Moreno M. A., Kerr B., Fairlie A. M., Lewis M. A. Feasibility and acceptability of the social media–Brief Alcohol Screening and Intervention for College Students intervention. Journal of Adolescent Health. 2023;72:943–949. doi: 10.1016/j.jadohealth.2023.01.014. doi:10.1016/j.adohealth.2023.01.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Neighbors C., LaBrie J. W., Hummer J. F., Lewis M. A., Lee C. M., Desai S., Larimer M. E. Group identification as a moderator of the relationship between perceived social norms and alcohol consumption. Psychology of Addictive Behaviors. 2010;24:522–528. doi: 10.1037/a0019944. doi:10.1037/a0019944. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Neighbors C., O’Connor R. M., Lewis M. A., Chawla N., Lee C. M., Fossos N. The relative impact of injunctive norms on college student drinking: The role of reference group. Psychology of Addictive Behaviors. 2008;22:576–581. doi: 10.1037/a0013043. doi:10.1037/a0013043. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Nesi J., Rothenberg W. A., Hussong A. M., Jackson K. M. Friends’ alcohol-related social networking site activity predicts escalations in adolescents drinking: Mediation by peer norms. Journal of Adolescent Health. 2017;60:641–647. doi: 10.1016/j.jadohealth.2017.01.009. doi:10.1016/j.jadohealth.2017.01.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Reid A. E., Carey K. B. Interventions to reduce college student drinking: State of the evidence for mechanisms of behavior change. Clinical Psychology Review. 2015;40:213–224. doi: 10.1016/j.cpr.2015.06.006. doi:10.1016/j.cpr.2015.06.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Russell A. M., Davis R. E., Ortega J. M., Colditz J. B., Primack B., Barry A. E. #Alcohol: Portrayals of alcohol in top videos on TikTok. Journal of Studies on Alcohol and Drugs. 2021;82:615–622. doi: 10.15288/jsad.2021.82.615. doi:10.15288/jsad.2021.82.615. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Schulenberg J. E., Johnston L. D., O’Malley P. M., Bachman J. G., Miech R. A., Patrick M. E.2018Monitoring the Future national survey results on drug use 1975-2017. Volume IICollege students and adults ages 19-55 Ann Arbor, MI: Institute for Social Research, The University of Michigan [Google Scholar]
  46. Steers M. N., Neighbors C., Wickham R. E., Petit W. E., Kerr B., Moreno M. A. My friends, I’m #SOTALLYTOBER: A longitudinal examination of college students’ drinking, friends’ approval of drinking, and Facebook alcohol-related posts. Digital Health. 2019;5:2055207619845449. doi: 10.1177/2055207619845449. doi:10.1177/2055207619845449. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Substance Abuse and Mental Health Services Administration. Rockville, MD: Author; 2019. 2020 National Survey on Drug Use and Health (NSDUH): Final CAI Specifications for Programming (English Version) Retrieved from https://www.samhsa.gov/data/sites/default/files/reports/rpt23244/NSDUHmrbCAISpecs2020.pdf. [Google Scholar]
  48. Substance Abuse and Mental Health Services Administration. Key substance use and mental health indicators in the United States: Results from the 2021 National Survey on Drug Use and Health. 2021. (HHS Publication No. PEP22-07-01-005, NSDUH Series H-57). Retrieved from https://www.samhsa.gov/data/sites/default/files/reports/rpt39443/2021NSDUHFFRRev010323.pdf.
  49. Turrisi R., Mallett K. A., Cleveland M. J., Varvil-Weld L., Abar C., Scaglione N., Hultgren B. Evaluation of timing and dosage of a parent-based intervention to minimize college students’ alcohol consumption. Journal of Studies on Alcohol and Drugs. 2013;74:30–40. doi: 10.15288/jsad.2013.74.30. doi:10.15288/jsad.2013.74.30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Vogel E. A., Ramo D. E., Rubinstein M. L., Delucchi K. L., Darrow S. M., Costello C., Prochaska J. J. Effects of social media on adolescents’ willingness and intention to use e-cigarettes: An experimental investigation. Nicotine & Tobacco Research. 2021;23:694–701. doi: 10.1093/ntr/ntaa003. doi:10.1093/ntr/ntaa003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Vranken S., Geusens F., Meeus A., Beullens K. The platform is the message? Exploring the relation between different social networking sites and different forms of alcohol use. Health & New Media Research. 2020;4:135–168. doi:10.22720/HNMR.2020.4.2.135. [Google Scholar]

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