Abstract
Introduction.
Frequent exposure to peer-shared alcohol-related content (ARC) on social media is associated with greater alcohol consumption and related consequences among undergraduates. Social media influencers also share ARC; yet, the effect of exposure to influencer-shared ARC on alcohol outcomes has not been examined. The current study examined whether following influencers who share ARC and the frequency of sharing were associated with alcohol outcomes, and associations between influencer type (e.g., actors) and alcohol outcomes.
Methods.
Undergraduates (N=528) from two universities in the United States completed an online survey assessing demographics, social media use, alcohol use and related consequences. They listed up to five influencers they followed and viewed the most content from. A series of linear regression models were conducted.
Results.
Having a larger proportion of influencers sharing ARC was associated with greater quantity, frequency and peak drinks, but not consequences. Frequency of influencers sharing ARC was associated with greater quantity and peak drinks, but not frequency or consequences. Findings remained significant, even after controlling for peer ARC. Actor ARC, everyday person ARC and “other” type influencer ARC were associated with several alcohol outcomes.
Discussion and Conclusions.
This study added to the literature by examining how following influencers who share ARC, and sharing frequency, were associated with drinking outcomes over and above exposure to peer ARC. It also examined whether ARC content from specific types of influencers was associated with alcohol outcomes. Findings highlight that the source of ARC is relevant when studying the effects of ARC exposure on college drinking.
Keywords: alcohol drinking in college, social media, marketing, social network analysis, social perception
Introduction
College students (i.e., undergraduates) have a high risk of engaging in heavy drinking and experiencing related consequences [1–4]. In fact, 49.3% of undergraduates report past-month alcohol use, 27.4% past-month binge drinking (i.e., consuming five or more drinks for males, four or more drinks for females) and about 13% meet the criteria for an alcohol use disorder [3]. Problematic drinking can interfere with academic success and mental health [4–6]. Determining factors that may influence undergraduate drinking is imperative for identifying and understanding groups at greater risk of problematic alcohol use, and may aid in developing methods to reducing problematic alcohol use.
Social influence plays a key role in undergraduate alcohol use [7–10]. Students who have drinking buddies report greater alcohol misuse [10]. Further, exposure to more frequent alcohol misuse within one’s social context in-person (e.g., living on campus) prospectively relates to drinking [7, 9–11]. Importantly, this may also apply to viewing frequent alcohol-related content (ARC) on social media [12–13]. Indeed, exposure to ARC is associated with greater alcohol consumption, including frequency and quantity, and alcohol-related consequences among young adults [14–15]. The role of ARC in social media posts by influencers is underexamined in the literature.
Social media influencers
Most research examining the effects of exposure to ARC on college drinking focuses on content shared by peers or directly by alcohol brands and companies [16]. However, alcohol brands and companies also use influencers for marketing [17–18]. Although the definition varies across fields (e.g., marketing, psychology), the most common definition for an influencer is a user who has a large following online [19]. For example, they can be celebrities popular in both mass media (e.g., TV, movies) and social media or individuals who are only popular on social media [20].
Content analyses revealed that influencers shared more posts, had larger followings, and their posts (e.g., anti-tobacco, pro-cigar use) across multiple platforms (i.e., Twitter, TikTok) had greater engagement (i.e., likes, shares) than non-influencers [21–22]. An examination of Instagram profiles for 178 popular influencers amongst young adults in the Netherlands found 63.5% of them shared at least one post discussing or displaying alcohol (i.e., alcohol post) in their last 100 posts (average of three to four alcohol posts shared in the last 100 posts [23]). Additionally, adolescents perceive influencers share ARC frequently [24]. These findings suggest substance-specific content is highly prevalent and has the potential to reach more users. However, a gap in this literature is how exposure to these posts from influencers may influence viewers’ alcohol consumption.
Approaches to examining effects of alcohol content on drinking
A robust finding from the drinking norms literature is that normative perceptions of more proximal reference groups (e.g., friends) versus more distal (e.g., same age peers) are stronger predictors of college drinking behaviour [e.g., 25–27]. Similarly, research using a social network design (e.g., participants are asked to list the names of important people in their lives then answer questions about each person) demonstrates that perceptions of how much specific individuals are drinking are stronger predictors of alcohol consumption than perceived drinking behaviours of groups [28–29].
Yet, most ARC studies use a global approach (e.g., ARC sharing of a group [norms]), rather than a social network approach (e.g., ARC sharing of specific individuals important to the participant [15]). Only three studies to date use a social network approach to examine how exposure to ARC shared by specific friends is related to drinking outcomes [30–32]. Of those studies, only one assesses undergraduates [32]. Given that undergraduates represent a population at high risk for problematic alcohol use [1–4], it is important to conduct more research to understand how social network members sharing ARC influences drinking outcomes. The current study builds on this foundation to examine if ARC sharing behaviours of other specific individuals (influencers rather than friends) impacts college drinking, including whether influencer posting impacts consumption and consequences over and above peer posting.
Current study
Problematic alcohol use is highly prevalent among undergraduates [1–4]. Social influences are a robust predictor of college drinking [8]. ARC exposure associates with greater alcohol consumption and consequences among undergraduates [14,15], but only focuses on exposure to content shared by peers or alcohol brands/companies [16]. Recent research suggests influencers are another source of ARC exposure which may associate with drinking (e.g., alcohol quantity and related consequences) among undergraduates [23].
Given these prior research findings, the current study had three aims for exploring the effects of viewing influencer ARC on drinking among undergraduates. The first aim was to examine whether following more influencers who shared ARC was associated with greater alcohol consumption and consequences, over and above viewing ARC shared by close friends (i.e., if influencers can impact drinking even after controlling for the proximal influence of close friends). The second aim investigated if higher frequency of influencers sharing ARC was associated with more consumption and consequences, again controlling for frequency of ARC shared by close friends. The last aim was exploratory and sought to understand whether exposure to ARC shared by specific types of influencers (e.g., actors) was associated with alcohol use.
Methods
Participants and procedure
Eligible participants (N=1166) completed the current study from two large, Southeastern and Midwestern public universities in the United States, respectively, (n=760 from data collection site 1; n=406 from data collection site 2). After receiving Institutional Review Board approval, data collection occurred between August 2021-Feburary 2022. Inclusion criteria required that students be between the ages 18–25 and have at least one active social media account. Recruitment occurred via the institutions’ psychology courses in exchange for course credit and through advertisements posted in university-wide daily announcement emails (data collection site 1 only) in exchange for raffle entry (to win one of two $50 Amazon gift cards).
Of the total sample, 864 participants (74.1%) listed following an influencer on social media. For the current examination, this was further narrowed to 556 participants who drink. Three attention checks were included in the cross-sectional online survey. Participants who failed two or more attention checks were removed (n=28). Thus, the final analytic sample size was 528 (Mage=20.19, SD=1.86). From this final analytic sample, 74.6% identified as cisgender women, 8.7% as Hispanic/Latinx, 26.7% as Black, 4.4% as Asian and 69.1% as White (see Table 1 for demographics). After providing informed consent, participants completed an online survey (~30 minutes).
Table 1.
Participant demographic information
Variable | |
---|---|
M (SD) | |
Age | 20.19 (1.86) |
n (%) | |
University | |
Data collection site 1 | 336 (63.6) |
Data collection site 2 | 192 (36.4) |
Gender | |
Cisgender man | 127 (24.1) |
Cisgender woman | 393 (74.6) |
Transgender man | 0 (0.0) |
Transgender woman | 1 (0.2) |
Nonbinary | 5 (0.9) |
Other | 1 (0.2) |
Ethnicity – Hispanic or Latino/a/x | |
Yes | 46 (8.7) |
No | 480 (91.3) |
Race | |
Black | 141 (26.7) |
Asian or Asian American | 23 (4.4) |
Native Hawaiian or Pacific Islander | 6 (1.1) |
White | 365 (69.3) |
Middle Eastern or North African | 3 (0.6) |
Native American | 7 (1.3) |
Other | 14 (2.7) |
More than one race | 30 (5.7) |
Note. Percentages for race do not sum to 100% because participants were allowed to select all that apply.
Measures
Participant characteristics
Alcohol consumption.
We measured alcohol consumption using a modified version of the Daily Drinking Questionnaire [33]. Participants were given the definition of a standard drink. Participants reported their alcohol use over the past 30 days, indicating how many drinks they typically consumed for each day of the week. From this, we calculated total number of drinks consumed in a typical week (quantity), number of drinking days in a typical week (frequency) and the highest number of drinks consumed on a single day (peak drinks).
Alcohol consequences.
The Brief Young Adult Alcohol Consequences Questionnaire [34] assessed problems related to alcohol use. This measure contains 24 items that may happen to people during or after drinking alcohol. Participants indicated whether each item describes something they experienced in the past 30 days. This measure is scored by summing the total number of consequences endorsed.
Social media use. We asked participants to characterise their social media use including the platforms they have accounts on (could select all that apply), how often they check their accounts (across all accounts) from 1=Never to 7=7 or more times a day, and whether they see their close friends post content about alcohol or content in which alcohol is visible. If participants indicated “yes,” they indicated the frequency they see close friends sharing alcohol posts from 1 = Never to 7 = Daily or almost daily. We also asked what platforms participants viewed their close friends sharing ARC on (could select all that apply).
Demographics.
Participants reported their gender, age, race and ethnicity.
Influencer social network characteristics
We asked participants to name “influencers” or “content creators” they follow and to describe the content they post. We defined influencers/content creators as “a social media user who has a large following including lots of people they may have never met in real life”. Participants listed the top 5 influencers/content creators who they follow and see the most content from, and subsequently indicated whether the influencer shares content where alcohol is present (i.e., alcohol posts). If participants responded “yes,” they reported the frequency of alcohol posts from 1 = Never to 7 = Daily or almost daily. Participants were asked the following question, “Is [influencer name] a/an …?” And chose one of the following response options: actor, musician, professional athlete, politician, everyday person (ex. Micro-celebrity) or other. Participants who selected “other” were provided with space to describe the influencer type. We also asked participants to select which platforms they saw the influencer share ARC on (could select all that apply).
To calculate the proportion of the network of influencers who shared ARC, the number of “yes” responses were coded as 1 and summed, then divided by the total number of influencers listed to reflect the proportion of influencers that post ARC (e.g., two out of five influencers shared ARC means a proportion of 2/5 or 0.4 or 40% of the network). To calculate the frequency of sharing ARC, an average was calculated (i.e., the responses were summed and divided by the number of influencers listed who shared ARC). For example, if the frequency of sharing ARC values were 1, 2 and 3 for a participant who said three out of five influencers listed shared ARC, then the proportion would be 6/3 or an average frequency of 2.0. The denominator reflected the number of influencers who post ARC. To calculate the proportion of influencers sharing ARC on each platform, the number of “yes” responses were coded as 1 and summed, then divided by the number of influencers who shared ARC. To calculate the proportion of specific influencer types sharing ARC, specific influencer types were coded as 1 if they shared ARC and influencers from the same type who did not share ARC were coded as 0. These responses were summed and divided by the total number of influencers listed of that specific type to reflect the proportion of influencers of a specific type sharing ARC (e.g., two of four actors shared ARC means a proportion of 2/4 or 50% of actors).
Analytic approach
To examine preliminary associations between all key variables (i.e., covariates, predictors, outcomes), we conducted two-tailed bivariate correlations. To examine study aims, we conducted a series of linear regression models. There were two models for each alcohol outcome variable with either the proportion of influencers sharing ARC (first aim) or frequency of influencers sharing ARC (second aim) as the predictor. Thus, there were four regressions conducted for the first and second aim (with quantity, frequency, peak drinks and alcohol-related consequences as outcomes in separate models). To examine the third aim, four models for each alcohol outcome variable were conducted with the proportion of each specific influencer type sharing ARC as the predictor. In total, 28 models were run for all three aims. All models controlled for data collection site, age, gender (recoded as dichotomous with 1=female and 0=male), frequency of checking social media accounts and close friends sharing ARC (whether the participant saw their close friends ever share ARC for models addressing the first aim, or the frequency of their close friends sharing ARC for models addressing the second aim). Additionally, the consequences models controlled for quantity.
Given the novelty of examining the influence of ARC shared by influencers on college drinking outcomes, we tested the models while controlling for close friend ARC (yes/no for Aim 1, frequency for Aim 2), and again without controlling for close friend ARC to examine descriptively if/how the strength of associations differed.
Results
All continuous variables were checked for normality and outliers. Quantity, peak drinks, and consequences each had between four to seven outliers that were winsorized. Frequency of drinking had no outliers. After winsorization, these variables were normally distributed. There was no missingness for the outcome variables or the predictor variable of the proportion of influencers sharing ARC. For the analyses which focused on frequency of influencers sharing ARC, participants who said they did not follow any influencers who shared ARC were dropped (n=124). Similarly, for the analyses which focused on the frequency of close friends sharing ARC, participants who said they had no friends that shared ARC were dropped (n=53).
Most participants checked their social media accounts seven or more times a day (60.4%). Additionally, participants indicated they had accounts on 4.87 (SD=1.17) platforms with Instagram (97%) being the most popular. The majority (90.1%) saw their close friends share ARC. In terms of frequency, they perceived that their friends share ARC a couple of times a month (28.2%). Most participants saw their friends share ARC on Snapchat (79.2%). Participants also said they perceived their friends share ARC on 1.91 (SD=0.87) platforms. On average, participants listed 4.10 influencers (SD=1.36; they could list up to five) and reported that about 1.59 influencers (SD=1.35) of those listed shared ARC. This corresponded to 39% of the influencers listed sharing ARC. The average frequency of influencers sharing ARC was 3.41 (SD=1.22) which equated to either every month or once a month. On average, influencers shared ARC on 2.11 (SD=1.15) platforms with the highest proportion sharing on Instagram (M=0.67, SD=0.42). The most reported influencer types who shared ARC were everyday people, musicians and “other” types (e.g., “Youtuber,” “makeup artist and photographer”). See Table 2 for social media descriptive statistics.
Table 2.
Participant social media use descriptive statistics
Variable | n (%) | M (SD) |
---|---|---|
Number of social media platforms used by participants | 4.87 (1.17) | |
Social media platforms used by participants | ||
512 (97.0) | ||
Snapchat | 498 (94.3) | |
TikTok | 467 (88.4) | |
380 (72.0) | ||
YouTube | 353 (66.9) | |
350 (66.3) | ||
Other | 14 (2.7) | |
Frequency of checking social media platforms | ||
Never | 0 (0.0) | |
Once a month or less | 1 (0.2) | |
2–3 times a month | 1 (0.2) | |
1–6 times a week | 6 (1.1) | |
1–3 times a day | 57 (10.8) | |
4–6 times a day | 144 (27.3) | |
7 or more times a day | 319 (60.4) | |
Ever see close friends share alcohol content on social media | ||
Yes | 475 (90.1) | |
No | 52 (9.9) | |
Frequency of close friends sharing alcohol content | ||
Never | 3 (0.6) | |
Less than once a month | 66 (13.9) | |
Every month | 62 (13.1) | |
A couple of times a month | 134 (28.2) | |
Every week | 108 (22.7) | |
A couple of times a week | 80 (16.8) | |
Daily or almost daily | 22 (4.6) | |
Number of platforms close friends shared alcohol content on | 1.91 (0.87) | |
Social media platforms close friends shared alcohol content on | ||
317 (60.0) | ||
Snapchat | 418 (79.2) | |
TikTok | 157 (29.7) | |
50 (9.5) | ||
YouTube | 16 (3.0) | |
41 (7.8) | ||
Other | 6 (1.1) | |
Number of social media influencers listed | 4.10 (1.36) | |
Number of social media influencers sharing alcohol content | 1.59 (1.35) | |
Number of social media platforms influencers shared alcohol content on | 2.11 (1.15) | |
Proportion of influencers sharing alcohol content on each platform | ||
0.67 (0.42) | ||
Snapchat | 0.11 (0.26) | |
TikTok | 0.24 (0.38) | |
0.02 (0.12) | ||
YouTube | 0.24 (0.37) | |
0.07 (0.21) | ||
Other | 0.01 (0.08) | |
Proportion of actors sharing alcohol content | 0.39 (0.47) | |
Participants with a proportion of 0.00 | 78 (57.4) | |
Participants with a proportion > 0.00 | 55 (42.6) | |
Proportion of musicians sharing alcohol content | 0.42 (0.41) | |
Participants with a proportion of 0.00 | 81 (40.1) | |
Participants with a proportion > 0.00 | 121 (59.9) | |
Proportion of athletes sharing alcohol content | 0.25 (0.40) | |
Participants with a proportion of 0.00 | 51 (68.0) | |
Participants with a proportion > 0.00 | 24 (32.0) | |
Proportion of politicians sharing alcohol content | 0.44 (0.53) | |
Participants with a proportion of 0.00 | 5 (55.6) | |
Participants with a proportion > 0.00 | 4 (44.4) | |
Proportion of everyday people sharing alcohol content | 0.38 (0.39) | |
Participants with a proportion of 0.00 | 153 (41.4) | |
Participants with a proportion > 0.00 | 217 (58.6) | |
Proportion of other type influencers sharing alcohol content | 0.39 (0.41) | |
Participants with a proportion of 0.00 | 113 (45.0) | |
Participants with a proportion > 0.00 | 138 (55.0) |
Note. Percentages for social media platforms used and platforms close friends shared alcohol on do not sum to 100% because participants were allowed to select all that apply. Specific types of influencers who shared alcohol content were coded as 1s with influencers of the same type not sharing ARC coded as 0s. Participants with a proportion of 0.00 meant that none of the influencers they listed shared alcohol content whereas participants with a proportion of > 0.00 meant that at least one of the influencers they listed shared alcohol content.
Significant positive correlations of interest were observed for frequency of participants checking social media and the frequency of seeing close friends share ARC but not the frequency of seeing influencers share ARC. There were also significant positive correlations between participants seeing their close friends share ARC and seeing more influencers share ARC, as well as between frequency of close friends sharing ARC and frequency of influencers sharing ARC, confirming these are appropriate covariates to include in the models (see Table 3).
Table 3.
Correlations and descriptive statistics for key study variables
Variable | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
1. Age | – | |||||||||||
2. University | −0.19 ** | – | ||||||||||
3. Gender | 0.12 * | −0.002 | - | |||||||||
4. Social media checking frequency | −0.22 ** | 0.01 | −0.09 * | – | ||||||||
5. Close friends sharing alcohol content | −0.02 | 0.09 * | −0.07 | 0.11 * | – | |||||||
6. Frequency of close friends sharing alcohol content | −0.04 | 0.01 | −0.11 * | 0.20 ** | – | – | ||||||
7. Proportion of influencers sharing alcohol content | 0.05 | −0.01 | −0.03 | 0.07 | 0.13 * | 0.13 * | – | |||||
8. Frequency of influencers sharing alcohol content | −0.03 | 0.03 | 0.03 | 0.08 | 0.15 * | 0.26 ** | 0.21 ** | – | ||||
9. Quantity | −0.03 | 0.22 ** | 0.22 ** | 0.02 | 0.11 * | 0.15 * | 0.15 ** | 0.17 ** | – | |||
10. Frequency | 0.16 ** | 0.05 | 0.13 ** | −0.01 | 0.09 * | 0.11 * | 0.12 * | 0.12 * | 0.69 ** | – | ||
11. Peak drinks | −0.12 * | 0.24 ** | 0.24 ** | 0.04 | 0.12 * | 0.12 * | 0.14 * | 0.16 * | 0.90 ** | 0.43 ** | – | |
12. Consequences | −0.09 * | 0.10 * | 0.02 | 0.13 * | 0.14 ** | 0.15 * | 0.13 * | 0.15 * | 0.55 ** | 0.37 ** | 0.53 ** | – |
M (SD) | 20.19 (1.86) | 0.36 (0.48) | 0.25 (0.43) | 6.46 (0.77) | 0.90 (0.30) | 4.28 (1.41) | 0.39 (0.32) | 3.41 (1.22) | 8.35 (7.78) | 2.31 (1.15) | 4.21 (3.11) | 3.46 (3.62) |
Note. Variables were coded as follows: University (0 = data collection site 1, 1 = data collection site 2). Social media checking frequency (1 = Never to 7 = Daily or almost daily). Gender (0 = Female, 1 = Male). Close friends sharing alcohol content (0 = No, 1 = Yes). Frequency of close friends sharing alcohol content (1 = Never to 7 = Daily or almost daily). The proportion of influencers sharing alcohol content ranged from 0.0 to 1.0. Frequency of influencers sharing content (1 = Never to 7 = Daily or almost daily). Quantity = number of drinks consumed in a typical week, frequency = number of drinking days in a typical week, peak drinks = highest number of drinks consumed on a single day in a typical week, consequences = number of alcohol-related consequences experienced in the past month. The correlation between close friends sharing alcohol content and frequency of close friends sharing alcohol content could not be computed as only participants who said their friends shared alcohol content were asked the follow-up question about frequency. Hence, the frequency value is missing for participants who said their friends did not share alcohol content. Values in bold reflect significant associations.
p <0.05;
p <0.001.
Proportion of influencer network sharing
Controlling for data collection site, age, gender, social media checking frequency and whether participants saw their close friends share ARC on social media, having a larger proportion of listed influencers sharing ARC was positively associated with greater quantity, frequency and peak drinks, but not consequences (see Table 4). These models were also conducted without accounting for close friends sharing ARC and the pattern of significance was identical, with negligible differences in the β estimates and p-values for quantity, β=0.16, p<0.001, frequency, β=0.11, p=0.010, peak drinks, β=0.15, p<0.001 and consequences, β=0.04, p=0.339.
Table 4.
Associations between influencer content and alcohol outcomes
Quantity | Frequency | Peak | Consequences | |||||
---|---|---|---|---|---|---|---|---|
Variable | β | p | β | p | β | p | β | p |
Model 1: Exposure (Yes/No) | ||||||||
University | 0.20 ** | <0.001 | 0.08† | 0.084 | 0.21 ** | <0.001 | −0.04 | 0.307 |
Age | −0.03 | 0.474 | 0.16 ** | <0.001 | −0.11 * | 0.008 | −0.05 | 0.231 |
Gender | 0.24 ** | <0.001 | 0.12 * | 0.006 | 0.26 ** | <0.001 | −0.10 * | 0.015 |
Social media check frequency | −0.001 | 0.999 | 0.01 | 0.780 | 0.01 | 0.797 | 0.10 * | 0.009 |
Quantity | – | – | – | – | – | – | 0.57 ** | <0.001 |
Close friends sharing alcohol content | 0.10 * | 0.024 | 0.10 * | 0.028 | 0.10 * | 0.015 | 0.06 | 0.123 |
Proportion of influencers sharing alcohol content | 0.15 ** | <0.001 | 0.10 * | 0.021 | 0.14 ** | <0.001 | 0.03 | 0.437 |
Model 2: Frequency of exposure | ||||||||
University | 0.23 ** | <0.001 | 0.12 * | 0.023 | 0.22 ** | <0.001 | −0.03 | 0.590 |
Age | −0.04 | 0.385 | 0.17 * | 0.002 | −0.12 * | 0.015 | −0.03 | 0.533 |
Gender | 0.27 ** | <0.001 | 0.16 * | 0.002 | 0.30 ** | <0.001 | −0.10 * | 0.037 |
Social media check frequency | −0.06 | 0.264 | 0.01 | 0.892 | −0.07 | 0.173 | 0.10 * | 0.037 |
Quantity | – | – | – | – | – | – | 0.56 ** | <0.001 |
Frequency of close friends sharing alcohol content | 0.15 * | 0.003 | 0.13 * | 0.020 | 0.12 * | 0.015 | 0.01 | 0.844 |
Frequency of influencers sharing alcohol content | 0.12 * | 0.013 | 0.05 | 0.313 | 0.11 * | 0.021 | 0.04 | 0.344 |
Note. Variables were coded as follows: University (0 = data collection site 1, 1 = data collection site 2). Social media checking frequency (1 = Never to 7 = Daily or almost daily). Gender (0 = Female, 1 = Male). Close friends sharing alcohol content (0 = No, 1 = Yes). Frequency of close friends sharing alcohol content (1 = Never to 7 = Daily or almost daily). The proportion of influencers sharing alcohol content ranged from 0.0 to 1.0. Frequency of influencers sharing content (1 = Never to 7 = Daily or almost daily). Quantity = number of drinks consumed in a typical week, frequency = number of drinking days in a typical week, peak drinks = highest number of drinks consumed on a single day in a typical week, consequences = number of alcohol-related consequences experienced in the past month. Values in bold represent significant associations,
p <0.10 (marginal significance),
p <0.05,
p <0.001.
Frequency of influencers sharing
As seen in Table 4, controlling for data collection site, age, gender, social media checking frequency and the frequency of participants’ close friends sharing ARC, frequency of influencers sharing ARC was positively associated with greater quantity and peak drinks, but not frequency or consequences. These models were also conducted without accounting for close friends sharing ARC, and the pattern of findings were similar with the key difference being that frequency was not significant in the models accounting for close friends sharing ARC but was significant in the models without this covariate, β=0.11, p=0.025. Otherwise, quantity, β=0.16, p<0.001, peak drinks, β=0.15, p=0.002, and consequences, β=0.05, p=0.228, all had similar β estimates and p-values.
Specific types of influencers sharing
After controlling for data collection site, age, gender, social media checking frequency and whether participants saw their close friends share ARC, actor ARC was positively associated with quantity (not frequency, peak drinks, consequences), everyday person ARC was positively associated with quantity and frequency (not peak drinks, consequences), and “other” type influencer ARC was positively associated with quantity, frequency, and peak drinks (not consequences). Several marginal (i.e., ps < .10) positive associations of note were between actor ARC and frequency, athlete ARC and frequency, and everyday person ARC and peak drinks (see Table 5).
Table 5.
Associations between specific types of influencers sharing alcohol-related content and alcohol outcomes
Quantity | Frequency | Peak | Consequences | |||||
---|---|---|---|---|---|---|---|---|
Variable | β | p | β | p | β | p | β | p |
Model 1: Actor ARC (n =129) | ||||||||
Close friends sharing alcohol content | 0.18 * | 0.027 | 0.16† | 0.050 | 0.17 * | 0.033 | 0.05 | 0.417 |
Proportion of actors sharing alcohol content | 0.17 * | 0.042 | 0.17† | 0.051 | 0.14† | 0.089 | 0.07 | 0.346 |
Model 2: Musician ARC (n = 199) | ||||||||
Close friends sharing alcohol content | 0.17 * | 0.014 | 0.15 * | 0.031 | 0.16 * | 0.020 | 0.09 | 0.158 |
Proportion of musicians sharing alcohol content | −0.01 | 0.892 | −0.03 | 0.687 | 0.03 | 0.639 | −0.02 | 0.705 |
Model 3: Athlete ARC (n = 75) | ||||||||
Close friends sharing alcohol content | 0.19 | 0.103 | 0.25 * | 0.035 | 0.13 | 0.256 | −0.02 | 0.882 |
Proportion of athletes sharing alcohol content | 0.19 | 0.117 | 0.23† | 0.054 | 0.14 | 0.225 | −0.08 | 0.473 |
Model 4: Everyday person ARC (n = 363) | ||||||||
Close friends sharing alcohol content | 0.09† | 0.093 | 0.04 | 0.460 | 0.11 * | 0.034 | 0.05 | 0.294 |
Proportion of everyday people sharing alcohol content | 0.13 * | 0.012 | 0.13 * | 0.014 | 0.10† | 0.052 | −0.003 | 0.953 |
Model 5: Other Influencer Type ARC (n = 246) | ||||||||
Close friends sharing alcohol content | 0.11† | 0.058 | 0.18 * | 0.003 | 0.12 * | 0.034 | 0.07 | 0.185 |
Proportion of other influencer types sharing alcohol content | 0.23 ** | <0.001 | 0.15 * | 0.012 | 0.21 ** | <0.001 | −0.01 | 0.927 |
Note. Although not listed in the table, all models controlled for university, social media checking frequency, gender, and quantity (in the consequences models only). The proportion of specific influencers sharing alcohol content ranged from 0.0 to 1.0. Quantity = number of drinks consumed in a typical week, frequency = number of drinking days in a typical week, peak drinks = highest number of drinks consumed on a single day in a typical week, consequences = number of alcohol-related consequences experienced in the past month. Values in bold represent significant associations,
p <0.10 (marginal significance),
p <0.05,
p <0.001.
Discussion
This study had three aims. The first aim examined whether following more influencers who shared ARC was associated with greater alcohol consumption and consequences, over and above viewing ARC shared by close friends. The second aim investigated if higher frequency of influencers sharing ARC was associated with more consumption and consequences, again controlling for frequency of ARC shared by close friends. The third exploratory aim examined whether exposure to ARC shared by specific types of influencers was associated with alcohol use. Following influencers who share ARC was associated with greater alcohol consumption (quantity, frequency, peak drinks), but not consequences (after controlling for consumption). Similarly, seeing this content more frequently was associated with greater quantity and peak drinks, but not drinking more frequently or consequences (after controlling for consumption). Although both following influencers who share ARC and seeing ARC more frequently demonstrated bivariate associations with consequences, these associations disappeared after controlling for relevant covariates, particularly consumption, suggesting viewing ARC was not inherently problematic over and above any potential impact on consumption. Further, following actors, everyday people, and “other” type influencers who share ARC was also associated with consumption (quantity, frequency, peak drinks) but not consequences.
Exposure to influencer ARC as well as frequency of sharing were slightly stronger correlates of college drinking outcomes than close friend ARC or frequency of sharing across most of the models. These findings are mostly in line with prior research in that greater exposure to ARC both globally (no specific source) and from specific groups of individuals (e.g., friends) is associated with greater consumption [14,16]. Exposure to influencer ARC may be more strongly associated with drinking than close friend ARC because the content is unique whereas content shared by friends may either feature the participant or be discussing or displaying a drinking event that the participant attended. Additionally, influencers are increasingly being sponsored by alcohol companies [17]. This sponsorship may further differentiate their influencer content from close friend ARC. For example, if individuals already liked specific brands of alcohol featured in sponsored influencer ARC, this could increase exposure via greater salience and engagement. Nascent research has found that adolescents positively evaluate influencer ARC featuring specific brands [24].
Another possible explanation for influencer ARC being a stronger predictor of drinking than close friend ARC could be due to the social network approach used to assess sharing and frequency of sharing ARC for specific influencers rather than influencers globally. In contrast, close friend sharing and frequency of sharing was assessed globally which could have dampened the effects because this approach does not differentiate between one of five friends versus all five friends. Unlike the findings in the current study, peer ARC exposure has been previously found to be associated with consequences [35]. However, this study [35] did not control for consumption unlike the current examination. It is interesting that the frequency of checking social media was the only social media-related variable directly associated with consequences. This finding suggests there may be other facets of social media use (e.g., motives, social comparison, expectancies) which could be associated with greater consequences above and beyond the ARC viewed.
Exposure to ARC from specific types of influencers including actors, everyday people, and “other type” influencers were also positively associated with several alcohol outcomes (quantity, frequency, peak drinks) with athlete ARC marginally positively associated with frequency. To date, no study has previously examined whether exposure to ARC shared by specific types of influencers followed by college students is associated with alcohol outcomes adding to the novelty of these examinations. It is of note that the “other” types described either did not provide enough detail to create a new category (e.g., “Youtuber”), reflected a category with membership too small to examine analytically (e.g., 24 “gamers”), or were true others and likely could be more than one type (e.g., “makeup artist and photographer”). Further, it is possible that some of the marginally significant associations were underpowered due to low numbers of participants reporting specific influencer types sharing ARC (e.g., actors, athletes, politicians). Overall, these findings suggest that select influencer types may have more influence on drinking behaviour than others and future studies may wish to qualitatively examine the alcohol content being shared by these influencer types, particularly the “other” type influencers who participants felt did not neatly fit one influencer type.
The current study had several limitations. We used a convenience sample consisting of college students from two institutions in different regions of the United States. It is possible that these findings may not replicate in samples of participants who are not from the United States. The sample was not diverse across gender, with 75% of students identifying as cisgender women. As such these findings are possibly more representative of cisgender women than students who identify with other gender identities. The study was cross-sectional; prohibiting temporal conclusions. Longitudinal studies would help with understanding the temporal order as to whether greater drinking is associated with increased exposure to influencer ARC or is greater exposure associated with increased drinking. Limited evidence supports a unidirectional association between greater use and increased exposure to friend ARC over time [36–37]. Future longitudinal research should explore unidirectional versus bidirectional associations between exposure to influencer ARC and alcohol use.
Another limitation was that our measures for the perception of frequency of checking social media and ARC exposure were self-report. There is strong evidence to suggest that individuals either over- or under-estimate their perceptions of time spent on social media and how often they share ARC when compared to objective estimates [38–39]. However, perceived frequency of sharing ARC is a stronger predictor of drinking behaviour than the objective frequency, suggesting perceptions may matter more [40]. Future studies of ARC should aim to include both objective and subjective estimates when feasible to better understand their association with alcohol use.
We also did not focus on ARC on a specific platform (e.g., Facebook) but rather focused on ARC shared across multiple platforms. In contrast, social media platforms have different ARC cultures (e.g., Instagram for glamorised content, Snapchat for casual content) [41–42]. Further, greater use of Instagram and Snapchat associates with greater consumption and sharing ARC cross-sectionally [43–45] and over time [46–47]. To examine platform-specific exposure, estimates for all platforms are typically included as simultaneous predictors in the model. Using this approach, it may be possible to determine which platform(s) have stronger associations with drinking. However, this may not be ecologically valid as the wholistic approach used in the current examination, as participants are using multiple platforms and likely seeing content on each. Future research could examine influencers’ platform-specific ARC by assessing the platform most used to share this content to understand if there are differences in exposure and alcohol outcomes.
Given that the current study found positive associations between exposure to influencer ARC and alcohol use (or that college drinking is associated with alcohol-related content they see from social media influencers), it is possible that influencers could be recruited to promote alcohol harm reducing messages for public service announcements to help reduce alcohol use among young adults. Perhaps content promoting responsible choices by social media influencers will similarly impact college drinking, yielding reductions. Promising evidence suggests that influencer campaigns focused on promoting flu vaccines, particularly amongst young adults [48] have an impact. Public health agencies also recruit influencers to assist with spreading anti-substance use messages (e.g., truth ® campaign). Influencer campaigns associate with more tweets and reach per day than non-influencer campaigns [21]. However, no research to date evaluates the effects of influencer public health campaigns on alcohol use among young adults. More research is needed to evaluate the effectiveness of influencer campaigns on reducing substance use among young adults.
Conclusions
The current study examines how exposure to ARC shared by specific influencers on social media (using a social network approach) is associated with drinking outcomes over and above peer ARC exposure. These findings highlight that the source of ARC is important to examine and that influencers are an important factor to consider when studying the effects of ARC exposure on college drinking. Further, ARC shared by specific types of influencers may be more influential. It is recommended that researchers examine how viewing ARC from influencers affects drinking intentions and behaviours longitudinally to better understand the temporal order.
Key point summary.
Exposure to ARC posted by specific influencers on social media associates with drinking outcomes over and above peer ARC exposure.
Frequency of influencers sharing ARC also correlates with drinking outcomes when controlling for peer ARC exposure.
Specific types of influencers (e.g., actors as opposed to politicians) had stronger links to drinking outcomes than others.
These findings also have important public health implications for increasing the impact of public service announcements about alcohol use among young adults, given the strong impact influencers can have on student behaviours.
Acknowledgements
Megan Strowger is supported by an individual fellowship award from the National Institute of Alcohol Abuse and Alcoholism (F31AA029945; PI: Strowger). Abby L. Braitman is supported by a career award from the National Institute on Alcohol Abuse and Alcoholism (K01AA023849; 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
Conflicts of Interest
The authors have no conflict of interests to declare.
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