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. Author manuscript; available in PMC: 2019 Jun 1.
Published in final edited form as: Alcohol Clin Exp Res. 2018 May 22;42(6):978–986. doi: 10.1111/acer.13642

Meta-analysis of the association of alcohol-related social media use with alcohol consumption and alcohol-related problems in adolescents and young adults

Brenda L Curtis 1, Samantha J Lookatch 1,2, Danielle E Ramo 3, James R McKay 1,2, Richard S Feinn 4, Henry R Kranzler 1,2
PMCID: PMC5984178  NIHMSID: NIHMS959577  PMID: 29786874

Abstract

Despite the pervasive use of social media by young adults, there is comparatively little known about whether, and how, engagement in social media influences this group’s drinking patterns and risk of alcohol-related problems. We examined the relations between young adults’ alcohol-related social media engagement (defined as the posting, liking, commenting, and viewing of alcohol-related social media content) and their drinking behavior and problems.

We conducted a systematic review and meta-analysis of studies evaluating the association of alcohol consumption and alcohol-related problems with alcohol-related social media engagement. Summary baseline variables regarding the social media platform used (e.g., Facebook, Twitter), social media measures assessed (e.g., number of alcohol photos posted), alcohol measures (e.g., AUDIT, TLFB), and the number of time points at which data were collected were extracted from each published study. We used the Q statistic to examine heterogeneity in the correlations between alcohol-related social media engagement and both drinking behavior and alcohol-related problems. Because there was significant heterogeneity, we used a random effects model to evaluate the difference from zero of the weighted aggregate correlations. We used meta-regression with study characteristics as moderators to test for moderators of the observed heterogeneity.

Following screening, 19 articles met inclusion criteria for the meta-analysis. The primary findings indicated a statistically significant relationship and moderate effect sizes between alcohol-related social media engagement and both alcohol consumption (r=0.36, 95%CI: 0.29–0.44, p<0.001) and alcohol-related problems (r=0.37, 95%CI: 0.21–0.51, p<0.001). There was significant heterogeneity among studies. Two significant predictors of heterogeneity were 1) whether there was joint measurement of alcohol-related social media engagement and drinking behavior or these were measured on different occasions, and 2) whether measurements were taken by self-report or observation of social media engagement.

We found moderate-sized effects across the 19 studies: greater alcohol-related social media engagement was correlated with both greater self-reported drinking and alcohol-related problems. Further research to determine the causal direction of these associations could provide opportunities for social-media-based interventions with young drinkers aimed at reducing alcohol consumption and alcohol-related adverse consequences.

Keywords: alcohol, social media, underage drinking, adolescents, young adults, meta-analysis

Introduction

Many adolescents and young adults engage in excessive alcohol consumption. The National Survey on Drug Use and Health found that, in 2016, 9% of adolescents and 57% of young adults consumed alcohol in the past month (SAMHSA, 2017). Of these individuals, 4.9% of adolescents and 38.4% of young adults engaged in binge drinking, which is defined as consuming 4 drinks for women, or 5 drinks for men on the same occasion on at least 1 day in the past 30 days. In addition, nearly 1% of adolescents and 10% of young adults engaged in heavy alcohol use, which is defined as binge drinking on 5 or more days in the past 30 days (NIAAA, 2017; SAMHSA, 2017). This pattern of alcohol consumption leads to adverse psychosocial and health-related consequences (Popovici & French, 2013; Wechsler, Lee, Kuo & Lee, 2000) and is associated with poor academic achievement, suicidal behavior, tobacco use, risky sexual behaviors, alcohol-related injuries, and driving while under the influence of alcohol (Miller et al., 2007; Wechsler et al., 1994).

Social media platforms are extremely well integrated into the lives of adolescents and young adults. In recent decades, the use of social networking sites (SNSs) like Facebook, Twitter, and Instagram has increased substantially, such that 90% of individuals aged 18-29 in the United States maintain personal SNS accounts (Perrin, 2015). Facebook is the most widely used SNS in the world, with an average of 1.32 billion active users (Facebook, 2017) . It is the third most popular website in the United States (Alexa, 2016), with 79% of U.S. adult internet users having a Facebook account and 76% of those accessing their account daily (Greenwood et al., 2016). SNS engagement is facilitated through information posted on one’s profile, which often includes photographs and a discussion with friends of one’s thoughts, actions, whereabouts, and other important personal information. The content of these posts is skewed toward positive experiences and events (e.g., parties, trips, birth of a child), creating a happy, entertaining social networking presence (Utz, 2015). Users also contribute SNS content by “liking” and sharing content created by others (e.g., sharing a blog post or video, or expressing support for a business).

SNS have evolved from personal sharing platforms to include commercial content. Substance use, particularly alcohol consumption, is frequently advertised, endorsed, and displayed on social media. As a consequence, social media has become an environment in which alcohol consumption (binge drinking in particular) is normalized and glamorized among adolescents and young adults (Griffiths and Casswell, 2010). A content analysis of 225 university undergraduate males’ Facebook profiles found that 85% contained alcohol references (Egan and Moreno, 2011). In a survey of male and female college students’ posting of images depicting alcohol consumption on SNSs, as many as one-third reported posting pictures of themselves drinking alcohol (Morgan et al., 2010). These depictions are often delivered in a comedic fashion and omit consideration of the negative consequences of drinking.

Moreover, alcohol-related social media engagement may influence drinking behavior. A review of the literature on the relations between social media and addictive behaviors in college students concluded that exposure to social media “breed[s] misperceptions regarding acceptance and prevalence of addictive behaviors” (Steers et al., 2016, p. 347). The authors of this review noted that positive social validation for substance use-related posts (conveyed through “likes,” shares, or comments) is likely to increase the frequency and intensity of students’ alcohol consumption. Consistent with this hypothesis, exposure to peers’ alcohol-related content on Facebook, Instagram, and Snapchat during the first six weeks of school in 408 first-year college students predicted alcohol consumption six months later. This finding was present after controlling for drinking during the initial six-week period (Boyle et al., 2016).

To date, reviews have considered drinking behavior in relation to risk behaviors or advertising content rather than focusing specifically on alcohol-related SNS engagement (Groth et al., 2017; Steers et al., 2016). To address this issue systematically, we conducted a meta-analysis of published literature to test the hypothesized positive association of alcohol-related social media engagement (i.e., posting, liking, commenting, and viewing alcohol-related social media content) with both alcohol consumption and alcohol-related problems among young adults.

Materials and Methods

Identification and Screening of articles

To identify articles, we followed a standard protocol outlined in the Preferred Reporting Items for Systematic Reviews & Meta-Analysis (PRISMA, 2015; see Figure 1). We searched MEDLINE (via PubMed), PsycINFO, EMBASE, Scopus, and the Cochrane Library using the MeSH heading terms and selected free-text for “social media,” and the most commonly used platforms, “Facebook,” “Twitter,” “YouTube,” “Snapchat,” or “Instagram.” The search was limited to “alcohol,” English language literature, and articles or reviews published before January 2017. Article titles were compiled across the database findings and duplicates were removed. We reviewed the bibliographies of included articles and applicable reviews for missed publications. Two authors (BLC and SJL) screened the abstracts of studies identified in the search and removed poster abstracts and those that lacked either relevant social media variables (i.e., observational and self-reported SM posting, viewing and interacting with alcohol related material posted by friends or advertisements) or alcohol variables, or were otherwise unrelated to these topics. Studies including SM variables that were not relevant to the current project (e.g., SM content analysis, alcohol advertisement on SM, views on SM posting about alcohol consumption, qualitative studies) were excluded. The two authors resolved disagreements (n=9) through a discussion of the criteria for selection; a third reviewer (DER) helped to resolve persistent disagreements. Studies that assessed social media and alcohol consumption were retained for full-text analysis, and we excluded studies that used social media only as a form of recruitment, focused on a content analysis of social media sites, or measured alcohol advertising.

Figure 1.

Figure 1

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Study Flow Diagram

We collected the following data from each study: the social media platform used (e.g., Facebook, Twitter); social media measures assessed (e.g., number of alcohol posts, density scores of alcohol images); alcohol-related measures used [e.g., Alcohol Use Disorders Identification Test (AUDIT), Rutgers Alcohol Problem Index (RAPI), Timeline Follow-back Interview (TLFB)], and the number of time points at which data were collected (See Table 1). The AUDIT assesses drinking behaviors, alcohol consumption and consequences, whereas the RAPI is specific to adolescent and young adult problem drinking; both are considered robust and valid measures of problematic drinking. The TLFB queries the frequency and intensity of alcohol consumption only. We tested five study characteristics as moderators: 1) study design (whether the alcohol-related social media engagement and alcohol consumption were measured jointly at once or individually at two different time points), 2) the social media platform with which alcohol-related social media engagement was assessed (Facebook versus other sites), 3) the method used to measure alcohol consumption (TLFB versus other methods), 4) statistical analysis used (correlation, linear regression, logistic regression, mean differences) and 5) study location (studies conducted outside of the United States versus studies conducted in the US). There were two categories of alcohol-related measures that were consistently evaluated across studies: alcohol consumption (i.e., the amount of alcohol consumed in a given time period) as reported in a single question or on the TLFB; and alcohol-related problems (e.g., regret after drinking, blacking out, sustaining injuries while drinking), which was measured using the AUDIT (though in one study the RAPI was used).

Table 1.

Studies included in Meta-analysis

Author (Year) Population Measurement Recording Sample Size Statistic SNS SNS Measure (Time Period) Alcohol Measure (Time Period) No. of Time points
Fournier (2011) US, college students Observation 68 Correlation Facebook Total alcohol pictures/posts (last 100 tagged/posted photos) Self-report alcohol consumption (unspecified) 1
Glassman (2012) US, college students Self-report 445 Linear regression Facebook Do you post photos of you drinking? (Y/N) Drinks per week (average weekly consumption) 1
Ridout (2012) Australia, college students Self-report 158 Linear regression Facebook Alcohol-identity measured by total alcohol related pictures (all) and posts (past 6 months) RAPI (past year), Graduated Frequency Measure AUDIT (past year) 1
Huang (2014) US, adolescents Observation 1563 Linear regression Facebook, MySpace Friends alcohol postings (past 30 days) Alcohol consumption status score (range 1-5) (past 30 days) 2
Hoffman (2014) US, college students Self-report 637 Linear regression All Social Media Alcohol related Social Media Use Index (past 3 months) Alcohol consumption (past 30 days) 1
D’Angelo (2014) US, college students Observation 312 Correlation Facebook Alcohol displays (past 3 months) TLFB binge drinking (last 28 days) 2
van Hoof (2014) Netherlands, college students Observation 71 Correlation Facebook Density scores for photos (last 20), updates (last 10), info (0-50 items listed) TLFB Quantity & frequency (past 30 days), AUDIT (past year) 1
Westgate (2014) US, college students Self-report 1106 Correlation Facebook How often post alcohol related content (unspecified) RAPI (past 3 months), Daily Drinking Questionnaire (past 3 months), AUDIT (past year) 1
Miller (2014) Australia, female college students Self-report 129 Correlation Facebook Percentage of posts related to Alcohol; unspecified Alcohol consumption as measured by AUDIT-C (unspecified) 1
Moss (2015)* UK, college students Self-report 145 Logistic regression Facebook Dichotomous engagement in a drinking game (neknomination), unspecified AUDIT (past year) 1
Jones (2016) Australia, adolescents & young adults Self-report 283 Logistic regression Facebook Any FB likes, views and interests in alcohol vs. None, unspecified Alcohol use frequency (past year) 1
Geusens (2016) Belgium, adolescents Self-report 3133 Correlation Facebook, Instagram, Snapchat Sharing alcohol references on SM (unspecified) AUDIT alcohol consumption subscale (past year) 1
Cabrera-Nguyen (2016) US, young adults Self-report 587 Logistic regression Twitter Exposure to alcohol related SM (past year) Self-report heavy episodic drinking (past 30 days) 1
Boyle (2016) US, college students Self-report 408 Correlation Facebook, Twitter, Instagram SM alcohol exposure (past year) Drinks per week (last 30 days) 2
Marczinski (2016) US, college students Self-report 146 Linear regression Facebook Alcohol-Related Facebook Activity (past 30 day FB use) TLFB number of drinks (past 30 days), AUDIT (past year) 1
Moreno (2016) US, college students Self-report 94 Logistic regression Facebook, Twitter Total posts and Alcohol references (past 5 months) TLFB drinks (past month) 2
Rodriguez (2016) US, college students Self-report 109 Correlation Facebook Alcohol-related SNS posts (most recent 100 posts) Drinks per week (unspecified) 1
Thompson (2016) US, college students Self-report 364 Correlation Facebook, Twitter, Instagram Alcohol-related SNS use (unspecified) Drinks per week (past 30 days), RAPI (past 3 months) 1
Hormes (2016)* US, college students Self-report 537 Mean difference Facebook Average pattern of daily SNS use (variable time with each question) AUDIT (past year) 1
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*

alcohol problems only; alcohol consumption measures query the number of standard drinks on the RAPI, AUDIT, AUDIT-C, and TLFB

Analysis

For meta-analysis, we used r, Pearson’s correlation coefficient following Fisher’s Z transformation as the effect size. For studies reporting other measures, such as a mean difference or odds ratio, we converted the effects to correlations using the methods proposed by Borenstein et al. (2009). We used a random effects model and the Q statistic to determine whether there was significant variability among study effect sizes. The I2 statistic is reported as a measure of the proportion of variance attributed to study heterogeneity. A forest plot (Figure 2) displays the individual studies and the weighted aggregated effect from the random effects model. In the face of significant heterogeneity in effect sizes among the studies, we conducted a meta-regression with study characteristics entered as moderators. Because the meta-analysis was limited to published articles, we also assessed publication bias using the Egger regression. All analyses were conducted in SAS v9.4.

Figure 2.

Figure 2

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Forest Plot for Alcohol Consumption

Results

Figure 2 shows the forest plot from the random effects model for alcohol consumption. There was no evidence for publication bias (t=0.58, p=0.57; see Figure 3). The weighted effect size was r=0.36 (95%CI: 0.29 – 0.44), which was statistically significant (p<0.001) and reflected a moderate effect size (Cohen, 1992) for the correlation between alcohol-related social media engagement and alcohol consumption. As evidenced by the varying size of the correlations and non-overlapping confidence intervals, the heterogeneity between studies exceeded random variation (χ216=7429, p<0.001), accounting for 93% of the variability in correlations (I2=0.93).

Figure 3.

Figure 3

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Alcohol Consumption Funnel Plot

The top part of Table 2 shows the results of the meta-regression, in which study type was a significant moderator (b=−0.22, p=0.02). Studies that assessed both alcohol-related social media engagement and alcohol consumption at a single time point had a weighted correlation of r=0.40, while the same statistic for studies that assessed these measures at separate time points was r=0.20. Also, studies where researchers observed social media use had significantly smaller associations (b= −0.25, p=0.01) than studies that used self-reports, with correlations of 0.15 and 0.40 respectively. In contrast, studies that used only Facebook postings as a measure of alcohol-related social media engagement did not differ from those that used other social media platforms (b= 0.13, p=0.14), studies that used the TLFB to measure alcohol consumption did not differ from studies that used other alcohol consumption measures (b=−0.12, p=0.24), type of analyses used didn’t differ (F2,14 = 2.42, p=0.13), and studies conducted outside the United States did not differ from studies conducted in the US (b= −0.10, p=0.21). Together, timing of assessment and measurement type accounted for 49% of the between study variability, however there is still significant variability (p=0.014).

Table 2.

Meta-Regression Results

Moderator Coefficient P-Value
Alcohol Consumption
 Timing of Assessments (cross-sectional)* −0.220 .021
 Measurement (self-report) Observation −0.250 .011
 Facebook (not Facebook) 0.133 .144
 TLFB (not TLFB) −0.125 .238
 Analysis (correlation) .125
  Logistic Regression 0.243
  Linear Regression 0.007
 Foreign (US) 0.124 .211
Alcohol-Related Problems
 Measurement (Self-report) Observation −0.256 .243
 Facebook (not Facebook) −0.191 .364
 Analysis (correlation) .503
  Logistic Regression −0.170
  Linear Regression 0.088
 Mean Difference −0.260
 US (non-US) −0.098 .543
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*

Comparison group in parentheses

Figure 4 shows the forest plot for alcohol-related problems. There was no evidence of publication bias (t=−0.74, p=0.49; see Figure 5). As with alcohol consumption, there was a moderate effect size that was statistically significant (r=0.37, 95%CI: 0.21 – 0.51, p<0.001) between alcohol-related social media engagement and alcohol-related problems. There was also significant heterogeneity in studies of alcohol-related problems (χ26=1794, p<0.001), which accounted for 94% of the variability in the correlations (I2=0.94). The meta-regression results for alcohol-related problems are shown in the bottom part of Table 2. We found no significant moderators of this heterogeneity. Because all of the studies measured alcohol-related social media engagement and alcohol-related problems at the same time point, the timing of the assessments did not account for this variability. The difference between self-report and observational measures was similar to alcohol consumption but the smaller number of studies likely contributed to the non-significant result (p=0.24). One study assessed alcohol-related social media engagement other than Facebook (Thompson et al., 2016) and it did not differ from the others (b=−0.19, p=0.36). Likewise, type of analysis and where the study was conducted were not significant moderators.

Figure 4.

Figure 4

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Forest Plot of Correlations between Social Media Alcohol Exposure and Alcohol-Related Problems

Figure 5.

Figure 5

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Alcohol Problems Funnel Plot

Discussion

Our systematic review and meta-analysis showed a moderate strength of relationship between exposure to alcohol-related social media content and alcohol consumption and consequences. Young adults in the United States and worldwide are very extensive users of SNSs (Pew Research Center, 2017). This age group is also characterized by high rates of alcohol consumption and heavy drinking (SAMHSA, 2017). Thus, as might be expected, young adults frequently discuss their drinking behavior on SNSs, a phenomenon of interest in many published studies over the past six years. Most of the published studies of representations of drinking behavior on SNSs involve comparatively few participants. This systematic examination of 19 published studies provides a more robust measure of the relations between alcohol-related SNS engagement and both drinking behavior and alcohol-related problems.

A growing number of publications have examined the correlation between alcohol-related SNS engagement and both drinking and alcohol-related problems but this does not speak to the direction of the association. In our analysis, we identified 19 reports that met our criteria for inclusion in a meta-analysis. This represented a total of more than 9,000 SNS users for whom data were available. Using random effects modeling to account for significant heterogeneity in correlations, we found a moderate overall effect, with greater alcohol-related social media engagement correlated with greater self-reported drinking and alcohol-related problems. The inclusion of study design and measurement type as moderator variables accounted for a substantial portion of the heterogeneity in the observed correlations involving alcohol consumption. Studies that assessed variables at different points in time or observed social media use showed correlations that were about half the size of the correlations seen in studies in which the key variables were measured at the same point in time or used self-reports. Despite a similarly high level of heterogeneity among studies of the correlation between alcohol-related social media engagement and alcohol-related problems, we found no significant moderators of this variability, possibly because there is greater consistency in the measurement of drinking than for alcohol-related problems, which by their nature are more varied. Or also it could be the reduced power since fewer studies measured alcohol-related problems compared to alcohol consumption.

Consumers of alcohol may be more exposed to alcohol-related content on social media by posting it themselves, having drinkers in their online social networks post such content, or as a result of targeting by alcohol industry marketing. Indeed heavy drinkers’ real-life social networks are more likely to include drinking friends who influence the index individual’s drinking behavior (Neighbors et al., 2008), and social media may expand the opportunity for drinking behavior to spread through social networks, providing additional opportunities for exposure (McCreanor et al., 2013). Among light drinkers, drinking may be glamorized on social media to portray a life of excessive fun or glamor (Tucker et al., 2013), and may be more likely than other posts to be shared through the social networks of young adults. Additionally, those who drink are likely to be targeted by alcohol marketing efforts. Although it is not possible to market directly to individuals who exhibit a specific behavior, the most popular social media tools use individuals’ social media data to offer marketers strategies to target those who are most likely to use their products (e.g., Ramo et al., 2014).

Exposure to alcohol content may also increase the likelihood that youth will initiate alcohol consumption. Alcohol marketing is not limited to individuals who drink. One UK-based study showed that 89% of male and 91% of female adolescents and young adults were exposed to alcohol marketing in an average month on the three most common social media sites (Winpenny et al., 2013). Further, many social media channels, such as YouTube, are accessible to all ages with no limitations on subscribers to alcohol brand channels (Barry et al. 2014). Social media marketing is often delivered using strategies that are highly attractive to young audiences (e.g., games; Nicholls, 2012). Longitudinal studies are needed to determine whether social media exposure contributes to young people’s vulnerability to drinking by influencing their cognitions (e.g., by enhancing intentions to drink) or more directly affecting their drinking behavior (e.g., through social modeling).

The meta-analysis was limited by the different ways that social media influence and alcohol consumption were measured among studies. This may help to explain the large degree of heterogeneity of effect sizes and limits the direct comparison among studies. Unfortunately, this potential source of heterogeneity could not be captured in the meta-regression because of the wide variation in methods used to measure these two variables. Another limitation is the different measures of effect that were used among studies. Assumptions need to be made about the underlying distribution of values when converting different effect sizes to a common measure (e.g., in converting odds ratios to correlation coefficients) and we cannot know with certainty whether the assumptions were met. A further limitation of this meta-analysis is that the 19 studies that were included were mostly of young adults or college students and thus the findings may not generalize to other populations. Finally, because our findings are correlational, we cannot draw conclusions regarding the direction of the effects between the measured variables. Thus, further research is needed to understand the nature of these correlations. Future studies that use experimental or quasi-experimental designs to understand whether alcohol-related SNS engagement predisposes to heavy drinking or, alternatively, that heavy drinking young adults are more likely to use SNS, are needed to advance this research effort. This is an important question because ascertaining the nature of the relations between these behaviors could permit social-media-based interventions aimed at reducing heavy drinking and alcohol-related problems in the many adolescents and young adults who use SNS.

Supplementary Material

Supp TableS1

Acknowledgments

Preparation of the manuscript was supported in part by NIDA grant R01DA039457 and the VISN 4 MIRECC, Crescenz VAMC, Philadelphia, Pennsylvania.

Disclosure: Dr. Kranzler has been a consultant, advisory group member, or continuing medical education lecturer for Alkermes, Indivior, and Lundbeck. He is also a member of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative (ACTIVE), which in the last three years was supported by AbbVie, Alkermes, Amygdala Neurosciences, Arbor, Ethypharm, Indivior, Lilly, Lundbeck, Otsuka, and Pfizer.

Footnotes

DR. BRENDA CURTIS (Orcid ID : 0000-0002-2511-3322)

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