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. Author manuscript; available in PMC: 2023 Aug 1.
Published in final edited form as: Alcohol Clin Exp Res. 2022 Jul 15;46(8):1592–1602. doi: 10.1111/acer.14899

The association between social network members sharing alcohol-related social media content on alcohol outcomes among college student drinkers

Megan Strowger 1, Abby L Braitman 1,2, Nancy P Barnett 3
PMCID: PMC9427690  NIHMSID: NIHMS1820936  PMID: 35778778

Abstract

Background:

College-aged young adults (e.g., aged 18 to 29 years old) use social media more than any other age group. An emerging body of literature has found higher exposure to alcohol-related social media content is associated with higher alcohol consumption among college students. Most studies assess exposure to peer drinking on social media using global measures, rather than measuring the exposure to alcohol-related posts of identified specific close peers. The current study examined whether having a higher proportion of important peers (i.e., social network members) who post alcohol-related social media content was associated with greater alcohol consumption and consequences. It also investigated the extent to which qualities of network members who share alcohol-related content are associated with participant alcohol outcomes.

Methods:

Participants were 130 college students (86.2% female, 56.9% White) with an average age of 23.39 (SD = 5.63) who had consumed at least one alcoholic drink in the past week. Participants completed measures of their social media use, alcohol consumption, consequences, and characteristics of important peers in their social network, including alcohol-related social media posting.

Results:

Having a higher proportion of social network members who post alcohol-related social media content was positively related to participant drinks per week and peak drinks. Higher network proportions of drinking buddies posting alcohol-related content were also associated with a greater frequency of alcohol use. Having a higher proportion of friends who post alcohol content and who the participants seek advice from was linked to more alcohol-related consequences.

Conclusions:

Having more important peers who post alcohol-related content on social media is associated with alcohol outcomes among college students. Harm-reduction focused alcohol interventions delivered on college campuses may incorporate information about the influence of viewing and sharing alcohol-related content.

Keywords: social media, social network, college drinking, peer influence, alcohol

Each year approximately 1,500 college-aged students die from unintentional alcohol-related injuries (Hingson et al., 2017). In addition, alcohol misuse among college-aged students has been implicated in physical and sexual assault incidents with approximately 700,000 physical assaults and 100,000 sexual assaults being reported each year (Hingson et al., 2005). College students who engage in problematic drinking increase their risk of experiencing academic (e.g., poor grades, missed classes; Conway and DiPlacido, 2015) and physical consequences (e.g., hangovers, blackouts, or increased risk of harming themselves; Hingson et al., 2016; Hingson et al., 2005). Given how prevalent alcohol misuse and related problems are among college students, it is important to understand what factors influence alcohol consumption. The current study sought to examine if exposure to alcohol-related social media content shared by social network members (e.g., identified specific close peers) is associated with alcohol use and related consequences among the college student viewers of this content. Additionally, we examined which qualities of relationships (e.g., drinking buddy status, closeness, concrete support) with social network members sharing alcohol-related content are associated with viewer drinking and consequences.

Social Networks

The social influence of peers in one’s social network is key in understanding alcohol use among college students. Previous research suggests when the close social networks of college students consist of higher proportions of heavier drinkers, individual alcohol consumption levels are higher as well (DiGuiseppi et al., 2018). Similarly, non-drinking students will seek out others who drink at low levels or do not drink (Balestrieri et al., 2018). Further, several other qualities of relationships with social network members are associated with individual drinking behavior, including drinking buddy status (i.e., a friend who you get together with regularly for activities which involve drinking; Lau-Barraco et al., 2012), relationship closeness (Tompsett and Colburn, 2019), and support for drinking (Longabaugh et al., 2010; Lau-Barraco and Linden-Carmichael, 2014; Leonard et al., 2000). Of note, perceptions of social network drinking, more so than actual drinking quantity of social network members, have been consistently associated with individual drinking behavior (for reviews, see Jeon and Goodson, 2015; Knox et al., 2019; Patterson and Goodson, 2019; Rinker et al., 2016). Perceptions of drinking by close friends in one’s social network are also significantly related to individual alcohol consumption, more so than global perceptions of drinking by a typical college student at their university (Kenney et al., 2017; Russell et al., 2020). Taken together, findings from social network college drinking studies suggest that relationship qualities and (perceptions of) network member drinking have important associations with individual alcohol use and related consequences.

Two social influence theories, social learning theory (Bandura and McClelland, 1977) and the theory of normative social behavior (Rimal and Real, 2005), may be relevant to understanding how specific peers in one’s social network affect college student alcohol use and consequences. Social learning theory (Bandura and McClelland, 1977) would suggest that college students receive direct positive social reinforcement and also learn vicariously through observing their peers that drinking is acceptable and will facilitate forming new friendships. Students are then more likely to model the drinking behavior of peers to fit in and make friends in college with those who approve of the behavior. The roles of modelling and social reinforcement of drinking behaviors of peers can be observed in studies finding that drinking levels of individuals are often similar to those of their social networks (Cox et al., 2019; Kenney et al., 2018; Kenney et al., 2017).

Social learning theory is relevant for formation of college social networks (i.e., selecting who is in one’s network), whereas the theory of normative social behavior may apply more to the complex dynamics of established networks. The theory of normative social behavior (Rimal and Real, 2005) hypothesizes that the relationship between descriptive norms (i.e., perceptions about how much others drink) and individual alcohol consumption can be strengthened or weakened by injunctive norms (i.e., perceptions about how much others approve of drinking), and how closely students identify with members of their social network. These connections have been supported in the social network drinking literature. For example, having a higher proportion of emotionally supportive (i.e., closer) friends in the social network who also engage in binge drinking has been associated with higher alcohol risk (Tompsett and Colburn, 2019). The framework provided by social learning theory and the theory of normative social behaviors help explicate why perceptions of drinking behavior and qualities of in-person social network members are associated with alcohol outcomes. However, social interactions take place in the online environment as well as in face-to-face contexts, potentially influencing drinking behavior. Therefore, social network studies that ignore online interactions may be missing an important element of peer influence.

Social Media

A growing body of research has consistently found associations between exposure to alcohol-related social media content (e.g., photos or videos featuring alcohol) and alcohol consumption of viewers of this content among young populations (for a systematic review, see Gupta et al., 2016). In a meta-analysis of 19 studies, Curtis et al. (2018) observed a medium effect size for the positive association between exposure to alcohol-related social media content and viewer alcohol consumption. It is important to note that the majority of alcohol-related social media content depicts positive (rather than negative) consequences of alcohol use (Russell, Davis, et al., 2021; Cavazos-Regh et al., 2015; Hendriks et al., 2018; Primack et al., 2015). Further, recent research has examined the prevalence and content of posts related to alcohol use disorder recovery (i.e., attempts to quit or cut down on drinking; Russell, Bergman, et al., 2021).

To date, a handful of studies have examined the relationship between non-specific close friends (i.e., assessing close friends in general, not specific individuals) posting alcohol-related content on social media and individual alcohol consumption. In a sample of college students, frequency of exposure to alcohol-related social media content (i.e., pictures and status updates about alcohol, drinking, being drunk, or hung over) on Facebook, Instagram, and Snapchat posted by online peers was associated with alcohol consumption up to 6 months later, even after controlling for both individual and friends’ baseline drinking levels (Boyle et al., 2016). Roberson et al. (2018) found that peer influence (including exposure to alcohol-related social media content [no specific platforms specified]) shared by friends [assessed globally, not about specific individuals], among other indicators) was both directly and indirectly associated with drinking behavior through typical student descriptive and injunctive norms. Exposure in the study by Roberson et al., (2018) was assessed using items created by the researchers at first dichotomously assessing whether participants had seen alcohol-related status updates shared by friends on social media followed by questions about the types of updates they had seen (e.g., photos of the participant or their friends drinking, YouTube videos, alcohol-related websites).

Rather than asking about exposure to peer alcohol-related posts generally, one study collected information on high school students’ seven best friends, and asked participants about these friends’ sharing of risky content on social media (specifically Facebook and Myspace), to examine if exposure to this content was related to individual smoking and drinking behaviors (Huang et al., 2014a; Huang et al., 2014b). For each person listed, participants were asked if that person ever smoked, recently drank alcohol, had talked about partying online, or had posted pictures of partying or drinking online. Having at least one best friend in their social network who smoked or drank was associated with greater likelihood of smoking or drinking themselves (Huang et al., 2014a; Huang et al., 2014b). Findings also revealed exposure to best friends’ pictures of partying or drinking was associated with a significantly greater likelihood of individual smoking or drinking, whereas exposure to best friends’ talking about partying was not. Moreover, the proportion of best friends who drank moderated this association, where exposure to photos featuring partying or drinking on social media had a stronger association with individual drinking among those with fewer best friends that drank (Huang et al., 2014b). That is, the effects of social media exposure were stronger for adolescents with fewer friends they drank with face-to-face.

A limitation of social media research in this area is that it tends to focus on exposure to online content featuring alcohol but not the quality of the relationship between the viewer and the poster. Little is known about how qualities of relationships with specific social network members sharing content are associated with individual drinking or consequences. A study of emerging adults (no educational status information collected) asked participants to list the five people they interacted with most frequently online, and to report on qualities of their relationships (including in-person contact; Cook et al., 2013). More emotional closeness with online social network ties and interacting offline with a greater number of these ties were associated with more frequent drinking.

Taken together, this nascent body of research with adolescents and emerging adults suggests that both qualities of peer relationships (e.g., closeness) and social network member posting behaviors may influence viewer problematic alcohol consumption and consequences among college students. However, previous research has examined either associations between network member behaviors (e.g., peer drinking status) and alcohol content posting (Huang et al., 2014a; Huang et al., 2014b) or associations between relationship qualities with online network members and alcohol consumption (e.g., Cook et al., 2013). To date, no study has examined specific network members alcohol-related social media posting, the relationship qualities of these network members and participants, and their association with participant alcohol use.

The Current Study

The current study sought to examine associations between social networks, social media posting, and drinking behavior (e.g., Boyle et al., 2016; Huang et al., 2014b). Aim 1 was to examine whether having social network members who post alcohol-related social media content is associated with individual alcohol consumption and related consequences among college students. We hypothesized that having a higher proportion of network members who post alcohol-related social media content would be positively associated with alcohol consumption and consequences. Aim 2 examined whether specific qualities of relationships with social network members who posted alcohol-related social media content, namely: 1) drinking buddy status, 2) closeness, 3) concrete support, 4) advice seeking, 5) criticism, and 6) mutual support (i.e., relying on each other) were linked to individual drinking and consequences. We hypothesized that relationship qualities indicating a closer relationship (i.e., greater closeness, more concrete support, more advice seeking, lower criticism, and greater mutual support) with network members sharing alcohol-related social media content or more time together in alcohol-salient contexts (i.e., drinking buddy status) would be associated with greater alcohol consumption and consequences.

Materials and Methods

Participants and Procedure

Undergraduate students (N = 170) at a large mid-Atlantic university were recruited through the Psychology Department’s research subject pool. Inclusion criteria included being at least 18 years old and consuming at least one alcoholic drink in the past week.1 After excluding individuals who did not meet eligibility criteria (n = 40), the final sample included N = 130 participants who were 86.2% female, 56.9% White, 23.1% African American or Black, with an average age of 23.39 (SD = 5.63). This study was approved by the Institutional Review Board. Demographic information about the sample is in Table 1.

Table 1.

Sample Descriptive Characteristics

Variable Overall (N = 130)
M (SD)
Age 23.39 (5.63)
Alcohol Use
 Quantity 7.20 (6.72)
 Frequency 2.66 (1.64)
 Peak drinks 3.28 (2.52)
Alcohol-related Consequences 3.83 (4.27
n (%)
Race
 White 74 (56.9)
 African American or Black 30 (23.1)
 Asian 5 (3.8)
 More than one race 15 (11.5)
 Other 6 (4.6)
Ethnicity
 Non-Hispanic 114 (87.7)
 Hispanic 14 (10.8)
 Did not respond 2 (1.5)
Gender
 Male 15 (11.5)
 Female 112 (86.2)
 Trans* 1 (0.8)
 Other 2 (1.5)
Class Standing
 Freshman 31 (23.8)
 Sophomore 14 (10.8)
 Junior 35 (26.9)
 Senior 47 (36.2)
 Graduate 2 (1.5)
 Other 1 (0.8)
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Note. Trans* = Transgender man or woman, quantity = number of drinks per week, frequency = number of drinking days, peak = max number of drinks consumed on a drinking day, alcohol-related consequences = number of negative consequences experienced.

After consenting to participate, participants completed an online survey which was estimated to take 90 minutes (Mdn = 46 minutes). Compensation was provided for participation in the form of research credits. Data were collected between May and August 2020.

Measures

Participant Measures (Reports of Own Behavior)

Social Media Usage.

Items about whether participants posted substance-specific content and what kinds of content they posted were adapted from prior research (Boyle et al., 2016; Nesi et al., 2017), and included “How often do you typically check your social media accounts? (1 = Less than once per week to 5 = 7 or more times per day), “Do you post content where you are…?” (Using marijuana, using tobacco, vaping, juuling, using opioids), and “Do you post any of the following on social media?” (Posted picture of you with alcohol, posted picture of you passed out or vomiting as a result of alcohol, posted picture of friend passed out or vomiting as a result of alcohol, tagged friends in photos with alcohol, status updates about you drinking alcohol, status updates about you drinking alcohol with friends, posted video of you with alcohol, links about drinking alcohol, drink recipes, etc.). Participants were also asked what social media platforms they use and how many minutes they spend per day on social media.

Alcohol Use.

Participant alcohol consumption was assessed using a modified version of the Daily Drinking Questionnaire (DDQ; Collins et al., 1985). Participants were presented with a chart describing standard drinks and a grid that contained the days of the week. They were asked to consider a typical week for the past 30 days, and to enter how many standard drinks they consumed each day. From this measure we calculated number of drinks per week (quantity), number of drinking days (frequency), and peak drinks (i.e., highest number of drinks per day).

Alcohol-related Consequences.

The Brief Young Adult Alcohol Consequences Questionnaire (B-YAACQ; Kahler et al., 2005) was used to assess the negative consequences experienced while drinking alcohol in the past 30 days. Items (24) were presented as a checklist, with participants endorsing each with a “yes” or “no” response. A total score was computed by summing the number of endorsed “yes” responses for the 24 items. The B-YAACQ (Kahler et al., 2005) had good internal consistency (α = 0.866).

Demographics.

Information about participants’ age, gender, race, ethnicity, and class year were collected to characterize the sample.

Nominated Network Member Measures (Perceptions of Close Friend Behavior)

Social Network.

Characteristics of participants’ social network members (i.e., identified specific close peers) were assessed using a modified, briefer version of the Important People Interview (IPI; Longabaugh and Zywiak, 2002). Participants were asked the following, “Please list up to ten (10) people who have been important to you since the start of the school year. These might be people you socialized with, studied with, or regularly had fun with. These people might be friends, roommates, people from work, or anyone that you see as having had a significant impact on your life, regardless of whether or not you liked them”. Then, participants were asked to describe each person they listed, answering questions about the quality of their relationship: “How close do you feel to this person?” (closeness; 1 = Not very close to 3 = Very close), “Is this person critical of you?” (criticism; 1 = Hardly ever to 3 = Almost always), “Could you rely on this person for advice?” (advice seeking; 1 = Hardly ever to 3 = Almost always), “Is this person available for concrete support?” (concrete support; 1 = Hardly ever to 3 = Almost Always), and “What is the direction of help?” (mutual support; 1 = Go both ways, 2 = You to them, 3 = They to you). This item was rescored to indicate receiving support from them (0 = they to you, you to them; 1 = go both ways). Participants were also asked to describe this person’s alcohol use (1 = Abstainer, 2 = Light drinker, 3 = Moderate drinker, 4 = Heavy drinker, 5 = Not known). This item was recoded to reflect the proportion of moderate and heavy drinkers in a participant’s social network (0 = Abstainer/Light drinker; 1 = Moderate drinker/Heavy drinker). Then participants were asked to indicate if this person was a drinking buddy (1= yes, 0 = no), if the participant was connected with this person on social media (1= yes, 0 = no), and if so, whether that person posted content where they were using alcohol on social media (1= yes, 0 = no).

Coding of Social Network Variables.

To create the social network quality variables, two approaches were used. For dichotomous (yes/no) responses (posting alcohol-related content on social media sites, drinking buddy status), proportions of individuals’ social networks (i.e., proportion of the ten people listed in the survey as important) that engaged in behaviors of interest or revealed characteristics of the relationships were calculated. For example, if the participant listed 10 important individuals, and four of them were drinking buddies, the drinking buddy proportion variable for that participant was calculated as 0.40 (4 out of 10 individuals). Calculating proportions of networks is a common practice in analyzing social network data (for a review, see Rinker et al., 2016).

Relationship qualities between the participant and social network members (i.e., ten people listed in the survey as important in participants’ lives) who post alcohol-related social media content were similarly created but focused only on the relevant social network members posting this content. For dichotomous network member qualities, we calculated the proportion of the network who had the quality (e.g., were considered a drinking buddy), among the important individuals who posted alcohol-related content rather than the number in the whole network. This reflected the proportion of network members who post alcohol-related content on social media who were drinking buddies. For example, if a participant listed six important individuals, but indicated that four of them posted alcohol-related content on social media and that two of these four members were also drinking buddies, the proportion variable was calculated as 0.5 or half of network members sharing alcohol content being drinking buddies (2 out of 4 individuals). This technique was used for drinking buddy status and receiving mutual support. For closeness, criticism, advice-seeking, and concrete support (all of which were ordered/continuous response options), the mean was calculated only for those network members who posted alcohol-related content on social media, yielding an average rating only for the individuals in a participants’ social network who posted alcohol-related content on social media.

Analysis Approach

SPSS 28 was used for all analyses. Outcome variables (quantity, frequency, peak drinks, consequences) were checked for normality and for outliers. Extreme values that were more than 1.5 interquartile ranges (IQRs) away from the third quartile were winsorized (four values for alcohol quantity and three for peak drinks). All outcome variables were thereafter normally distributed and there were no missing data.

Aim 1 was analyzed using linear regression in which each alcohol outcome (quantity, frequency, peak drinks, consequences) was regressed on the proportion of network members posting alcohol-related content in separate models. Aim 2 was analyzed in a similar way with each alcohol outcome regressed on social network relationship qualities. Additionally, we ran four separate omnibus models (one for each outcome) where we included all social network qualities (drinking buddy status, closeness, concrete support, mutual support, advice seeking, criticism). The pattern of findings was similar, so we only present the omnibus model findings. We included participant gender and frequency of checking social media as covariates in all models, as these have been documented as relevant control variables in previous research (Boyle et al., 2016; Nesi et al., 2017). In the Aim 1 and 2 models that examined alcohol-related consequences, we included typical alcohol quantity as a covariate to allow us to determine the unique effect of exposure to alcohol posting on consequences after controlling for alcohol use. Additionally, given the precedent in the literature for the drinking status of the social network (e.g., moderate, heavy drinkers; DiGuiseppi et al., 2018) being a relevant predictor of college drinking, the aim 1 analyses were also rerun with the proportion of moderate and heavy drinkers in the network included as an additional covariate along with gender and frequency of social media checking.

Results

Descriptive Statistics

The demographic characteristics of the sample as well as the means and standard deviations for the alcohol outcomes assessed (i.e., quantity, frequency, peak drinks, consequences) can be found in Table 1. Of the social media platforms assessed, Instagram (92.3%), Snapchat (82.3%) and Facebook (79.1%) were most frequently endorsed. Almost half (49.2%) of participants indicated they checked their social media platforms seven or more times per day, and participants reported spending a little over two hours per day on social media on average. When individuals were asked about what types of alcohol-related content they shared on social media, 40% reported sharing photos of themselves with alcohol on their platforms. The most popular substance other than alcohol to be featured in social media posts by participants was marijuana (13.2% of participants). More details on social media usage behaviors in the sample are in Table 2. The average number of social network nominations was 7.07 (SD = 2.98), and participants reported that almost one quarter of network members shared alcohol-related social media posts (M = 0.23, SD = 0.26). See Table 3 for more details about average social network qualities.

Table 2.

Sample Social Media Usage

Variable Overall (N = 130)
M (SD)
Minutes per day spent on social media 140.49 (142.35)
n (%)
Social media platforms useda
 Instagram 120 (92.3)
 Snapchat 107 (82.3)
 Facebook 103 (79.2)
 YouTube 100 (76.9)
 Twitter 72 (55.4)
 TikTok 69 (53.1)
 Pinterest 65 (50.0)
 Whatsapp 21 (16.2)
 Reddit 21 (16.2)
 Tumblr 11 (8.5)
 LinkedIn 1 (0.8)
Frequency of checking social media accounts
 7 or more times per day 64 (49.2)
 4–6 times per day 30 (23.1)
 1–3 times per day 25 (19.2)
 1–6 times per week 8 (6.2)
 Less than once per week 2 (1.5)
 Did not respond 1 (0.8)
Types of alcohol-related content shared on social mediaa
 Posted picture of you with alcohol 52 (40)
 Tagged friends in photos with alcohol 38 (29.2)
 Posted video of you with alcohol 35 (26.9)
 Links about drinking alcohol, drink recipes, etc. 27 (20.8)
 Status updates about you drinking alcohol with friends 26 (20)
 Status updates about you drinking alcohol 21 (16.2)
 Posted picture of friend passed out or vomiting as a result of alcohol 10 (7.7)
 Posted picture of you passed out or vomiting as a result of alcohol 3 (2.3)
Other types of substance-related content shared on social mediaa
 Marijuana 17 (13.2)
 Vaping 14 (10.8)
 Juuling 12 (9.2)
 Tobacco 8 (6.2)
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a

For these questions, participants were allowed to select more than one response, therefore, not all category percentages add up to 100%.

Table 3.

Social Network-level Descriptive Characteristics

Social Network Information n M (SD) Range
General Network Characteristics
Average size of network 130 7.07 (2.98) 1–10
Relationship Qualities of Full SN
Average closeness 130 2.52 (0.39) 1–3
Average concrete support received 130 2.49 (0.37) 1.33–3
Average advice seeking frequency 130 2.51 (0.37) 1.33–3
Average amount of criticism 130 1.69 (0.57) 1–3
Proportion of mutual support 130 0.86 (0.19) 0.00 to 1.00
Proportion share alcohol-related content 130 0.32 (0.33) 0.00 – 1.00
Proportion drinking buddies 130 0.37 (0.32) 0.00 – 1.00
Proportion moderate or heavy drinkers 130 0.45 (0.29) 0.00 – 1.00
Relationship Qualities of SN Sharing Alcohol-related Content
Average closeness 85 2.52 (0.49) 1–3
Average concrete support received 85 2.46 (0.49) 1–3
Average advice seeking frequency 85 2.52 (0.47) 1–3
Average amount of criticism 85 1.70 (0.64) 1–3
Proportion of mutual support 84 0.84 (0.29) 0.00 to 1.00
Proportion of drinking buddies 85 0.54 (0.40) 0.00 – 1.00
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Note. SN = Social Network.

Aim 1: Proportion of Network Posting Alcohol-Related Content

As seen in the top portion of Table 4, controlling for participant gender and frequency of checking social media accounts, having a higher proportion of network members who posted alcohol-related content was significantly associated with greater drinks per week, β = 0.19, p = .031, and peak drinks, β = 0.24, p = .007, but not number of drinking days per week (frequency), β = 0.08, p = .398, or experiencing more alcohol-related consequences, β = 0.14, p = .071 (controlling for typical alcohol quantity). Although the proportion of network members posting alcohol-related social media content was not a significant predictor of alcohol-related consequences, the frequency of checking social media, β = 0.18, p = .025, and drinks per week, β = 0.44, p < .001, were associated with greater consequences.

Table 4.

Multiple Regressions Examining Associations Between Social Network Variables and Alcohol Outcomes

Quantity Freq. Peak Conseq.
Variable β p β p β p β p
Aim 1 models
Gender 0.15 .099 0.09 .332 0.12 .181 −0.03 .748
Freq. checking social media 0.07 .435 −0.11 .224 0.14 .113 0.18 * .025
Quantity - - - - - - 0.44 * <.001
Prop. share alcohol content 0.19 * .031 0.08 .398 0.24 * .007 0.14 .071
Aim 2 models
Gender 0.11 .375 0.14 .267 0.03 .799 0.019 .863
Freq. checking social media −0.02 .867 −0.18 .115 0.03 .778 0.30 .005 *
Quantity - - - - - - 0.46 * <.001
Prop. share alcohol content −0.03 .847 −0.04 .750 −0.001 .993 0.01 .905
Prop. drinking buddies (share SN) 0.02 .944 0.45 * .046 −0.24 .262 0.27 .178
Mean closeness (share SN) −0.02 .936 0.01 .961 −0.11 .691 0.31 .230
Mean concrete support (share SN) 0.14 .631 −0.02 .947 0.20 .464 −0.47 .067
Mean advice (share SN) −0.33 .170 −0.42 .084 −0.19 .405 0.46 * .038 *
Mean criticism (share SN) 0.21 .340 0.13 .535 0.31 .129 −0.11 .560
Prop. mutual support (share SN) 0.24 .132 0.29 .061 0.08 .598 −0.13 .373
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Note. Prop. = proportion, quantity = number of drinks per week, freq. = number of drinking days peak = max number of drinks consumed on a drinking day, conseq. = number of consequences, and (share SN) = social network members sharing alcohol-related social media content. Analyses for each outcome were conducted separately. All models controlled for gender, frequency of checking social media, and typical alcohol quantity (for models examining consequences).

*

Bold values are significant at p < .05

After controlling for the additional covariate of the proportion of moderate or heavy drinkers in the network, the proportion of network members who posted alcohol-related content was still significantly associated with peak drinks, β = 0.20, p = .029, but not drinks per week, β = 0.16, p = .085. As in the original aim 1 analyses, the proportion of network members posting alcohol-related social media content was not a significant predictor of the number of drinking days per week (frequency), β = 0.05, p = .587, or alcohol-related consequences, β = 0.15, p = .075 (controlling for typical alcohol quantity). Similarly, the frequency of checking social media, β = 0.17, p = .037, and drinks per week, β = 0.44, p <.001, were linked to greater consequences.

Aim 2: Relationship Qualities of Social Network Members Posting Alcohol-Related Social Media Content

As seen in the lower portion of Table 4, the proportion of social network members who post alcohol-related content on social media that were drinking buddies was significantly associated with the number of drinking days per week, β = 0.45, p = .046. Additionally, the proportion of social network members who post alcohol-related content on social media who the participant sought advice from often was significantly associated with alcohol consequences, β = 0.46, p = .038. Participant frequency of checking social media, β = 0.30, p = .005, and drinks per week, β = 0.46, p < .001, were also significant predictors of alcohol-related consequences. There were no other significant associations between relationship qualities of social network members posting alcohol-related social media content and alcohol outcomes.

Discussion

Examining how close peers influence alcohol consumption and consequences via the alcohol-related social media content they share is important to examine given the robust evidence on how social influences from close peers affect college drinking (Borsari & Carey, 2001) and growing body of research linking exposure to alcohol-related content to alcohol outcomes (Curtis et al., 2018). However, prior research has commonly assessed exposure to alcohol-related content using global measures (e.g., descriptive norms) which do not adequately elucidate how content shared by named specific close peers (e.g., members of a social network or friend group) is linked to viewer alcohol consumption and consequences (for a review see Strowger & Braitman, 2022). As such, the current study built upon the findings of previous studies by examining how exposure to alcohol-related social media content shared by social network members was associated with participant alcohol consumption and consequences (Aim 1) and how qualities of relationships with social network members sharing this content were associated with alcohol outcomes (Aim 2). Overall, we found that having a higher proportion of network members who post alcohol-related content on social media was associated with greater alcohol consumption (quantity for the week and peak drinks). Although many social network qualities of network members sharing alcohol-related content were not associated with consumption and consequences, there were two notable findings – having a higher proportion of friends who post alcohol content that are drinking buddies was associated with drinking on more days per week and having a higher proportion of friends who post alcohol content and who the participants seek advice from was associated with more alcohol-related consequences.

The support for our primary hypothesis (Aim 1) that participants with a greater proportion of their network posting alcohol-related content was associated with higher alcohol consumption among college students is consistent with prior findings that high school students who had a more friends sharing alcohol-related social media content were more likely to report using alcohol and tobacco themselves (Huang et al., 2014a, Huang et al., 2014b). Further, these findings are also in line with prior research which used global measures to assess exposure to alcohol-related content (i.e., did not identify specific important individuals). These studies have documented that greater exposure to alcohol-related content on social media platforms is related to increased consumption among college students (Boyle et al., 2016). Consistent with our hypothesis for Aim 2, we also found that having a higher proportion of drinking buddies who post alcohol-related social media content was associated with participant drinking. Generally, having more drinking buddies in a social network is prospectively associated with typical and heavy drinking among emerging adults and these associations are mediated by social alcohol expectancies (Lau-Barraco et al., 2012). Moreover, motivation to drink socially has been found to mediate the relationship between exposure to alcohol-related content and individual alcohol consumption (Boyle et al., 2016) and may explain why having a higher proportion of drinking buddies who posted this content was associated with more frequent alcohol consumption. It is interesting to note that having a larger proportion of network members sharing alcohol-related content was not associated with more frequent drinking but having a higher proportion of drinking buddies among those who shared this content was linked to frequency. This finding suggests a more specific relationship with network members sharing alcohol content may matter when it comes to individual drinking behaviors.

Most other indicators of relationship closeness with social network members sharing alcohol-related content (e.g., closeness, concrete support, advice-seeking, mutual support) were not associated with alcohol consumption or consequences; only advice seeking from network members who post alcohol-related social media content was associated with alcohol-related consequences. Frequency of going to friends for advice and its link to drinking in the social network (i.e., specific important friends) context has not been previously explored but it could be that network members whom others go to for advice are also looked up to or viewed as someone others can learn from. In this sense, others are more likely to model the behavior of these network members they also go to for advice. As such, it follows that exposure to alcohol-related content shared by these network members might be linked to viewer alcohol-related consequences. Previous research has generally found that greater relationship closeness with social network members who drink is associated with higher individual alcohol consumption (Cook et al., 2013; Tompsett and Colburn, 2019). Relationship closeness may interact with other variables including relationship type (e.g., friend, romantic relationship, family member) which may exert difference influences on alcohol use and consequences. Similarly, the other relationship qualities (e.g., concrete support, mutual support) examined in the current study may only be relevant when combined with other characteristics. Overall, these findings suggest that the most relevant qualities of close friends sharing alcohol-related social media content may be those that reflect shared experiences with alcohol use.

Some design-related limitations should be noted. First, this study was cross-sectional in nature and thus was unable to establish causal relationships. Although we have established that exposure to alcohol posts among social network ties is associated with individual drinking, we cannot conclude that relationships with peers sharing alcohol posts are influencing drinking, that participant drinking levels influence relationships with peers sharing alcohol posts, or if the relationships are bidirectional. Second, this study used the time frame of “since the start of the school year” when asking participants to list ten important people in their social network. It is possible that participants who completed the study in the summer were thinking about the start of the previous school year (up to nine months ago) while those completing the study in the fall may have been thinking about important people since that start of that current semester (up to four months ago). Third, although our study assessed which social media platforms participants had active accounts on, we did not assess which platforms they shared or viewed alcohol-related content on. Although we felt broad exposure regardless of platform viewed on was important to assess, limited research has found that exposure to alcohol-related content on some platforms has a stronger association with alcohol use than others (e.g., stronger associations for Snapchat versus Facebook, Instagram, or WhatsApp; Boyle et al., 2016; Vranken et al., 2020). Fourth, whether participants shared or viewed alcohol-content was dichotomously assessed. Findings from previous studies have revealed that the frequency of sharing or exposure (e.g., Boyle et al., 2016; Erevik et al., 2017) and how engaged participants were with the content (e.g., liking; Kurten et al., 2022) are also associated with alcohol use but have not been explored using social network approaches.

In addition to design-related limitations, there were also a few sample-related limitations. First, these data were collected during the COVID-19 pandemic. A study that took place pre-COVID found that 34.4% of adolescents had at least one friend that talked about partying online and 31% had at least one friend who posted party/drink pictures (Huang et al., 2014b), whereas 17.7% of our participants reported that at least one member of their social network shared alcohol-related content. The encouragement of physical distancing measures and closure of bars and restaurants during the pandemic could have played a role in how much alcohol-related content was being shared among social networks. Average frequency of drinking among U.S. adults increased during the pandemic (Pollard et al., 2020), but due to stay-at-home and social distancing guidelines, drinking was likely not in typical social settings (e.g., bars, parties) and given social media posts are commonly made in social situations, this behavior may have been suppressed. However, with so few studies examining the link between specific social network members posting alcohol-related social media content and drinking (Huang et al., 2014b and the current study), it is impossible to know whether the behavior being suppressed during the pandemic affected this association. Although college-aged adults (ages 18–29) stayed connected socially online during the pandemic more than older age groups (48% held a virtual party or social gathering online with their friends or family; Vogels, 2020), the decrease in going out to drink (i.e., social drinking occasions) could be associated with less alcohol-related content being shared on social media platforms. Second, this study used a convenience sample of college students recruited from the Psychology Department’s research subject pool. Although the current sample had stronger representation for female participants (86.2% vs. 56.8% at the institution), age and race were relatively representative of the student body. Along these lines, future research should examine whether the association between exposure to alcohol-content shared by social network members and alcohol use and related consequences is moderated by age, as underclassmen (e.g., freshmen) have been found to be more vulnerable to the effects of peer influence on their drinking than upperclassmen (e.g., juniors; DeMartini et al., 2011; Pedersen et al., 2010).

Understanding factors which influence the relationship between alcohol-related content exposure and alcohol outcomes (e.g., consumption, consequences) has implications for tailoring existing social media-delivered social network drinking interventions. Social network interventions (SNIs) more commonly are used to deliver existing health behavior change interventions to complete specific social networks (i.e., an entire sorority house) with the goal of diffusion and adoption of the intervention across all network members (for reviews see Hunter et al., 2019; Shelton et al., 2019). SNIs have demonstrated some short term and long-term effectiveness for health behavior change such as reducing substance use (Hunter et al., 2019) suggesting they may be efficacious for behaviors that are susceptible to social influence. However, a simulation study by Hallgren et al. (2017), modeled the complex dynamics of social network drinking and how four interventions may affect alcohol outcomes. This study found that the intervention which was most effective was one where susceptibility to social selection and influence processes were reduced for adolescents with more heavy drinkers in their networks (Hallgren et al., 2017). This suggests some interventions may be effective in promoting resistance toward harmful social influence. Findings from the current study suggest that interventionists may wish to identify students who check social media frequently and have more individuals in their friend group (i.e., social network) who are heavy drinkers and/or share alcohol-related social media content as high-risk individuals most in need of an intervention.

SNIs have also been delivered through social media, often using personalized normative feedback (PNF) to elicit reductions in alcohol use. These approaches generally evidence short-term effectiveness for reducing consumption (similar to other online alcohol interventions; for reviews see Moreno et al., 2016; Ridout, 2016; Steers et al., 2016; for a review and meta-analysis see Vannucci et al., 2020). Additionally, several studies have found that exposure to alcohol-related social media content is linked to higher close friend descriptive drinking norms (Strowger & Braitman, 2022). Thus, interventionists may wish to include PNF showing the actual drinking levels and approval for drinking behaviors of specific influential social network members rather than peers or friends more generally. However, a missing piece in existing social media-delivered SNIs for reducing alcohol use and consequences is a lack of emphasis on social media literacy. Social media literacy could be used to develop or strengthen resistance to the influence from social media. For example, interventionists could include content that teaches deconstruction skills whereby students learn to recognize that when members of their social network) share alcohol content it is often not showing the full experience (e.g., only showing the glamor, not the negative consequences). Given the findings from the current study, interventionists could further specify that students evaluate more critically content shared by drinking buddies or friends students often go to for advice. Last, a social media-delivered SNI alcohol intervention could also promote alcohol-free programming on campus that members of the social network could attend.

Conclusion

The current study was the first to measure specific social network members and how their sharing of alcohol-related social media content is linked to individual alcohol outcomes among college students. Specifically, having a higher proportion of social network members that post this kind of content was associated with greater individual drinking quantity, and peak drinks. Moreover, having a higher proportion of drinking buddies among one’s social network members who post alcohol-related content was associated with greater frequency of drinking. Having a higher proportion of network members who post alcohol-related content and whom participants go to for advice was also linked to greater alcohol-related consequences. These findings may have implications for online brief alcohol interventions commonly administered on college campuses. Universities may want to consider including content in these interventions where the influence of social media is discussed.

Acknowledgments

Abby L. Braitman is supported by a career award from the National Institute on Alcohol Abuse and Alcoholism (K01-AA023849; PI: Braitman). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Alcohol Abuse and Alcoholism.

Footnotes

There are no known conflicts of interest to disclose.

1

Sponcil and Gitumu (2013) had 100% of their sample active on social media despite it not being inclusion criteria, and Boyle et al. (2017) did not include active use of social media as an inclusion criterion but found that the majority of participants were active on the specific platforms they assessed (98% Facebook, 94% Instagram, 95% Snapchat). With prior research showing such high rates we thought it unnecessary to have social media activity as an inclusion criterion.

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