Abstract
Objective
This study examined the use of social media for recovery support among individuals with substance use disorder (SUD) with past-year treatment attendance and tested whether demographic and SUD history factors were associated with use of social media for recovery support.
Method
Participants (N = 255; 45 % female, 85 % white, mean age = 41.4 [9.6]) recently treated for SUD completed an online survey. The survey gathered demographics, SUD histories, and social media use data. We report descriptive statistics and logistic regression models testing relationships between social media for recovery support and individual factors.
Results
Forty percent of participants used social media for recovery support, and most believed it beneficial. Being female increased use likelihood (OR = 2.56, 95 % CI [1.49, 4.46]), while older age (50 +) was associated with lower use likelihood than young adults (18−35) (OR = 0.35, 95 % CI [0.14, 0.84]). Social media was used primarily to build support systems and follow recovery-related content. Most found support forums on their own, and engaged with the groups for meaningful amounts of time (>weekly, >15 minutes).
Conclusions
Results highlight how common it is to use social media for recovery support. Given the sparse evidence on its efficacy, more research is urgently needed to establish whether recovery support forums on social media convey actual benefits, and how to shape one’s digital environment to maximize those benefits.
Keywords: Social media, Substance use disorder (SUD) recovery, Digital technology, Peer support, Substance use & addiction, Digital recovery support services (D-RSS)
Highlights
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Social media was commonly used for recovery support, and most believed it beneficial.
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Female participants were more likely to use social media for recovery support.
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Older participants (50 +) were less likely to use social media for recovery support.
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Most found recovery groups alone, without support from professionals or peers.
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Social media was used most to build support networks with both online and in-person relationships.
1. Introduction
A vast body of research shows that a variety of psychosocial and pharmacological therapies are effective in treating substance use disorders (SUDs) (Dellazizzo et al., 2023, Dutra et al., 2008, Jhanjee, 2014, Volkow, 2020). However, a growing literature also shows that nonclinical support such as peer recovery support services (Eddie et al., 2019, Reif et al., 2014) and mutual aid (Donovan et al., 2013, Kelly et al., 2020; White et al., n.d.) also improve SUD outcomes, both independently and above-and-beyond other forms of formal treatment. Until recent years, recovery support services were mostly delivered in face-to-face formats (Marsch et al., 2020). However, a variety of digital tools (e.g., websites, online forums, social media, smartphone apps) are increasingly being used to provide SUD digital recovery support services (Bergman et al., 2018, Gilbert et al., 2022).
Several studies have reported that people in recovery are increasingly accessing recovery-related support broadly on social media platforms. For example, Curtis et al. found that most people in outpatient treatment for SUD ages 18–35 own a social media account (82 %) and use it daily (67.6 %) (Curtis et al., 2019). Other studies have described the emergence of recovery-specific social networking sites such as InTheRooms (Bergman et al., 2017, Bergman and Kelly, 2021, Rubya and Yarosh, 2017), and have analyzed recovery-focused content on platforms like Facebook (Liu et al., 2022), Reddit (D’Agostino et al., 2017), Youtube (Raj et al., 2023), Instagram (Lu, 2023), and TikTok (Russell et al., 2021). A study of Philadelphia outpatient SUD treatment patients showed that nearly half had been exposed to recovery-related content on social media, and two-thirds thought social media would be a good place to get recovery-related information (Ashford et al., 2018).
Qualitative research has also begun to show how those in recovery use social media for support. In interviews with 30 individuals who were currently in SUD treatment, Viera et al. reported that some described using social media to connect with those they had met through in-person treatment, seeking inspirational content, and looking for instrumental support (Viera et al., 2023). Research in other health conditions has found that people use social media to exchange social support (Naslund et al., 2016), learn about self-management of their condition (Årsand et al., 2019), validate health information (McCarthy et al., 2020), and other activities (Chen and Wang, 2021). Research is needed to build on this nascent body of literature to inform how and why those who are in recovery use social media to support their goals around substance use.
The current study builds on the findings from a qualitative study (Phelan et al., 2022) that investigated how people in recovery navigated re-entering digital social spaces. The aims of the present exploratory study are to examine: 1) who uses social media for recovery support, 2) what types of support they seek, and 3) perceived helpfulness of recovery support from social media.
2. Methods
2.1. Participants
We recruited 255 participants through flyers and business cards distributed at addiction treatment centers in Rhode Island and Connecticut. Eligibility criteria included: (1) age 18 or older, (2) fluency in English, (3) residency in New England (CT, RI, MA, NH, VT, ME), and (4) a diagnosis of substance use disorder (SUD) with receipt of traditional treatment (e.g., counseling, medication) in the past 12 months.
2.2. Procedures
Recruitment materials included a URL and QR code linking to a study website with a brief overview and an eligibility screening survey in Qualtrics. Eligible participants received detailed study information and provided informed consent. After consenting, they submitted contact information and proceeded to the main survey. The survey took about 45 minutes to complete and was secured with reCAPTCHA 3 and Qualtrics’ fraud protection features. Response validity was checked through congruence checks (e.g., matching age and date of birth). We removed any responses that were incomplete or had a duplicated unique identifier that mapped multiple responses to a single person (n = 96), as well as responses that did not pass response congruence checks, were flagged as potential fraud by Qualtrics, or contained implausible personal information (n = 34). Excluded responses were more likely to be from people who reported as male (73.6 % vs. 54.5 %, χ2(2) = 10.28, p < 0.001) and Hispanic (21.0 % vs. 10.6 %, χ2(1) = 5.15, p = 0.020), and less likely to be in counseling (69.9 % vs. 82.4 %, χ2(1, n = 328) = 4.71, p = 0.03). No other differences in demographics or SUD history were found.
Participants who completed the survey received a $25 prepaid debit card or Amazon e-gift card. Study procedures were approved by the Brown University Institutional Review Board. We report all measures, data exclusions, and analytic decisions. This study was not preregistered.
2.3. Measures
2.3.1. General social media use
The survey included a number of items related to social media use (see Appendix A). These items asked participants if they had social media accounts they used, on what platforms (Facebook, Instagram, TikTok, Snapchat, Twitter/X, Reddit, and/or Other) and how frequently they used each one ([0] once every few months or longer to several times a day [7]. They also reported how often they saw use-positive content (“drug or alcohol cues - things that made you want to use drugs or drink”) and recovery-positive content (“for example, celebrating a milestone or an ad for a treatment center”) on a five-point scale ([0] never to [4] almost every time).
2.3.2. Social media use for recovery support
Participants indicated whether they had used social media to support their recovery in the past 12 months. This item asked about what digital tools they used to help them in their recovery, with items differentiating between “Social media (e.g., Facebook, Instagram, Twitter),” “Videoconferencing/Telemedicine/Zoom (outside of meetings for your traditional treatment),” and other tools. Those who said they used social media for recovery support were asked additional questions. First, participants were asked to select from a list of recovery-related resources they interacted with on social media, “Which of the following resources on social media did you interact with to help you in your recovery?,” with options including recovery support forums (e.g. Facebook groups focused on SUD recovery), recovery influencers/pages, and advice forums. Second, participants were asked, “Which of the following things have you done on social media to help you in your recovery?” and selected from a list of items such as asking for support, celebrating recovery milestones, and searching for treatment information. Recovery-related activities refer to self-initiated behaviors (e.g., posting, searching), while recovery-related resources refer to recovery-related groups or accounts followed.
Participants were also asked about whether they used social media to expand their recovery network with people they met online or in person, how accurate they found recovery content online, and were asked if overall they felt social media helped or hurt their recovery ([0] hindered a lot to [4] helped a lot).
Participants who said they accessed recovery support forums were asked additional questions: (a) what social media platforms they used to access the forums; (b) engagement frequency (“Only a time or two ever” [0] to “Several times a day” [7]) and duration per visit (“A few seconds or so” [0] to “2 + hours” [6]); (c) reasons for joining, ranked by importance (e.g., “To find ways to stay motivated to stay sober,” “To help other people who are struggling with addiction”); and (d) how they found the forums.
2.3.3. Alcohol and drug history
Participants reported how long ago they were first diagnosed with SUD ([0] within the past 6 months to [4] 5 + years ago), their “drug of choice,” current use of SUD medication, engagement in counseling, and participation in 12-step meetings.
2.4. Data analysis plan
We first computed descriptive statistics for demographic and SUD history items. Categorical variables were summarized with frequencies and percentages, while continuous variables were summarized with means and standard deviations. We chose a sample size of N = 250 based on a basic a priori power analysis for a logistic regression model in GPower. This analysis suggested that the sample of 250 would be sufficient to detect effects that are at least OR= 2.0, where α= 0.05, power= 0.80. However, the study was primarily exploratory and focused on descriptive survey data.
We divided participants who used any social media into two groups: recovery social media users, who used social media for recovery support; and general social media users, who used social media but not for recovery support.
We used a logistic regression model to test whether specific demographics or SUD history characteristics were associated with recovery social media use. We first coded a binary variable for using social media for recovery support (yes/no), and then used this as the outcome variable in the regression. We then coded binary variables reflecting participants’ demographics. Demographic predictors were coded as follows: biological sex, relationship status (in a relationship [ref.] vs. single), employment status (unemployed [ref.] vs. employed), and income (< $30,000 household income [ref.] vs. > $30,000). There was insufficient representation in our sample to differentiate between racial and sexual minorities, so we grouped marginalized identities (LGBTQ+, BIPOC) together to check if there was any effect of marginalization broadly, dichotomizing these variables into non-marginalized versus marginalized identities, i.e. sexual orientation (straight [ref.] versus LGBTQ+), and race (white [ref.] versus BIPOC). Age was grouped into quartiles: 18–35 [ref.], 35–40, 40–50, and 50 + . SUD history variables were dichotomized: years since diagnosis (<5 years [ref.] vs. 5 +), current SUD medication use, counseling participation, and 12-step meeting participation. Drug of choice was dichotomized as alcohol [ref.] versus other drugs, to reflect the legal difference between alcohol and other drugs that are illegal or, in the case of cannabis, partially legal. We tested multicollinearity using variance inflation factors (VIFs) and excluded variables with VIF > 2. Variables with p < 0.20 in univariate models were retained in the multivariate model, with p < 0.05 indicating statistical significance.
Second, we used ordinal logistic regressions to test whether recovery social media users differed from general social media users in how frequently they reported seeing recovery-positive and use-positive content on social media ([0] never to [4] almost every time). Two models were estimated: one predicting exposure to recovery-positive content and another predicting exposure to use-positive content, with group membership (recovery vs. general social media user) as the independent variable. We confirmed that both models met the proportional odds assumption using the Brant test.
All analyses were performed in R version 4.3.2.
3. Results
Table 1 presents participant demographics and SUD histories. Participants were predominantly middle-aged with an approximately even gender split, but mostly White, with fewer than 7 % Black/African American and 11 % Hispanic/Latino. Participants were also largely unemployed, low income, and single. The most common “drugs of choice” were heroin/fentanyl, followed by cocaine and alcohol, and the vast majority of participants had been diagnosed with SUD more than 5 years ago.
Table 1.
Demographics of all participants, participants who had social media for any purpose, and the subset of social media users who used social media for recovery support.
| Characteristic | Mean (SD) or N (%) |
||
|---|---|---|---|
| All | Any social media | Recovery social media | |
| Total | 255 (100) | 200 (78.4) | 100 (39.2) |
| Age (Range: 18 – 66) | 41.4 (9.6) | 40.0 (9.3) | 39.5 (7.9) |
| 18–35 | 67 (26.3) | 59 (29.5) | 28 (28.0) |
| 35–40 | 53 (20.8) | 48 (24.0) | 28 (28.0) |
| 40–50 | 74 (29.0) | 55 (27.5) | 29 (29.0) |
| 50 + | 46 (18.0) | 27 (13.5) | 9 (9.0) |
| Gender | |||
| Woman | 113 (44.3) | 99 (49.5) | 59 (59.0) |
| Man | 133 (52.2) | 97 (48.5) | 40 (40.0) |
| Non-binary | 3 (1.2) | 1 (0.5) | 1 (1.0) |
| Transwoman | 2 (0.8) | 1 (0.5) | 0 (0) |
| Transman | 0 (0) | 0 (0) | 0 (0) |
| Gender non-conforming | 2 (0.8) | 1 (0.5) | 0 (0) |
| Other | 1 (0.4) | 1 (0.5) | 0 (0) |
| Biological sex | |||
| Female | 114 (44.7) | 99 (49.5) | 59 (59.0) |
| Male | 139 (54.5) | 100 (50) | 41 (41.0) |
| Other/prefer not to say | 2 (0.8) | 1 (0.5) | 0 (0) |
| Race | |||
| White | 217 (85.1) | 174 (87.0) | 88 (88.0) |
| Black or African American | 17 (6.7) | 13 (6.5) | 5 (5.0) |
| American Indian/Alaska Native | 3 (1.2) | 0 (0) | 0 (0) |
| Asian | 0 (0) | 0 (0) | 0 (0) |
| Pacific Islander | 1 (0.4) | 1 (0.4) | 0 (0) |
| Multiracial | 12 (4.7) | 9 (4.5) | 6 (6.0) |
| Prefer not to say | 5 (2.0) | 3 (1.5) | 1 (1.0) |
| Ethnicity (Hispanic or Latino) | 27 (10.6) | 19 (9.5) | 8 (8.0) |
| Single relationship status | 112 (43.9) | 85 (42.5) | 40 (40.0) |
| ≥ Some college | 103 (40.4) | 86 (43.0) | 43 (43.0) |
| Unemployed | 172 (63.0) | 129 (64.5) | 64 (64.0) |
| Low income (< $30,000) | 212 (83.1) | 164 (82.0) | 80 (80.0) |
| Sexual orientation | |||
| Heterosexual/straight | 205 (80.4) | 158 (79.0) | 78 (78.0) |
| Gay/Lesbian | 10 (3.9) | 7 (3.5) | 2 (2.0) |
| Bisexual | 28 (11.0) | 27 (13.5) | 16 (16.0) |
| Other | 5 (2.0) | 5 (2.5) | 2 (2.0) |
| Prefer not to say | 7 (2.7) | 3 (1.5) | 2 (2.0) |
| AUDIT score | 17.4 (12.9) | 16.5 (13.1) | 17.4 (13.1) |
| DUDIT score | 24.5 (15.0) | 25.0 (15.2) | 24.5 (15.2) |
| Drug of choice | |||
| Heroin/Fentanyl | 81 (31.8) | 63 (31.5) | 28 (28.0) |
| Cocaine | 59 (23.1) | 42 (21.0) | 20 (20.0) |
| Alcohol | 54 (21.2) | 38 (19.0) | 23 (23.0) |
| Prescription opioids | 16 (6.3) | 16 (8.0) | 6 (6.0) |
| Methamphetamine | 14 (5.5) | 13 (6.5) | 6 (6.0) |
| Prescription sedatives | 13 (5.1) | 13 (6.5) | 8 (8.0) |
| Marijuana | 12 (4.7) | 10 (5.0) | 6 (6.0) |
| Prescription stimulants | 3 (1.2) | 3 (1.5) | 2 (2.0) |
| Ecstasy | 2 (0.8) | 1 (0.5) | 1 (1.0) |
| Hallucinogens | 1 (0.4) | 1 (0.5) | 0 (0) |
| Time since diagnosed with SUD | |||
| < 6 months | 10 (3.9) | 7 (3.5) | 3 (3.0) |
| 6 months−1 year | 10 (3.9) | 8 (4.0) | 3 (3.0) |
| 1–2 years ago | 19 (7.5) | 16 (8.0) | 9 (9.0) |
| 3–5 years ago | 29 (11.4) | 23 (11.5) | 13 (13.0) |
| 5 + years ago | 163 (63.9) | 127 (63.5) | 62 (62.0) |
Of the 255 survey respondents, 78 % (n = 200) reported using social media for any purpose. Most (79 %, n = 158 of 200) said they used social media at least once a day, and 60 % had accounts on more than one platform. Table 2 summarizes platform usage among social media users.
Table 2.
Prevalence of participant social media accounts by platform, among those with social media.
| Platform |
Any social media |
Recovery support |
||
|---|---|---|---|---|
| Counts | Prevalence | Counts | Prevalence | |
| 177 | 89 % | 91 | 91 % | |
| 95 | 48 % | 51 | 51 % | |
| Twitter/X | 24 | 12 % | 13 | 13 % |
| TikTok | 66 | 33 % | 36 | 36 % |
| Snapchat | 53 | 27 % | 24 | 24 % |
| 24 | 12 % | 13 | 13 % | |
| Other | 2 | 1 % | 2 | 2 % |
3.1. Prevalence and demographic correlated of recovery social media use
Among social media users, 50 % (n = 100 of 200) reported using social media for recovery support in the past year (39 % of the overall sample, N = 255). Of these, 82 % (n = 82 of 100) said they use social media at least once a day. Throughout, we refer to participants who used social media for recovery as “recovery social media users,” and those who used social media for other purposes as “general social media users.”
Univariate logistic regression models tested associations between demographic and SUD history variables, and recovery social media use. Age and sex met criteria for inclusion in the multivariate model (p < .20). No variable had a VIF > 2, indicating a low risk of multicollinearity. In the multivariate model comparing recovery social media users to the overall sample, being female was associated with higher odds of recovery social media use, while being age 50 or older was associated with lower odds compared to the reference group (18–35 years). However, the 50 + group included only nine recovery social media users. See Table 3.
Table 3.
Logistic regression of using social media for recovery support by various demographic and behavioral factors (N = 255).
| Characteristic |
Univariate |
Multivariate |
||||||
|---|---|---|---|---|---|---|---|---|
| OR | SE | p | 95 % CI | OR | SE | p | 95 % CI | |
| Age (ref 18–35) | ||||||||
| 35–40 | 1.56 | 0.37 | 0.23 | 0.76, 3.24 | 1.64 | 0.38 | 0.19 | 0.78, 3.50 |
| 41–50 | 0.9 | 0.34 | 0.75 | 0.46, 1.76 | 0.87 | 0.35 | 0.7 | 0.44, 1.74 |
| 50 + | 0.34 | 0.45 | 0.02 | 0.14, 0.79 | 0.35 | 0.46 | 0.02 | 0.14, 0.84 |
| Female | 2.52 | 0.26 | 0.0005 | 1.51, 4.24 | 2.56 | 0.28 | 0.0008 | 1.49, 4.46 |
| LGBTQ+ | 1.43 | 0.33 | 0.27 | 0.75, 2.72 | - | - | - | - |
| BIPOC | 0.68 | 0.38 | 0.3 | 0.31, 1.39 | - | - | - | - |
| Unemployed | 1.32 | 0.27 | 0.31 | 0.77, 2.25 | - | - | - | - |
| Low income | 1.5 | 0.34 | 0.23 | 0.77, 2.93 | - | - | - | - |
| AUDIT | 1 | 0.01 | 0.99 | 0.98, 1.02 | - | - | - | - |
| DUDIT | 1 | 0.01 | 0.98 | 0.98, 1.02 | - | - | - | - |
| Drug of choice | 0.84 | 0.31 | 0.57 | 0.46, 1.56 | - | - | - | - |
| Time since diagnosis | 0.88 | 0.29 | 0.66 | 0.49, 1.57 | - | - | - | - |
| Currently in counseling | 1.54 | 0.35 | 0.22 | 0.78, 3.14 | - | - | - | - |
When comparing recovery social media users (n = 100) to general social media users (n = 100), only being female remained a significant predictor (OR=2.19, 95 % CI=[1.2,4.0], SE=0.30, p = 0.01). There were no significant differences in social media platforms used or frequency of use between recovery social media users and general social media users.
3.2. Recovery-related resources and activities on social media
Social media recovery users (n = 100) were asked about the types of recovery-related resources they accessed and activities they engaged in on social media (see Fig. 1 for all possible responses). They were also asked if they used social media to connect with people in recovery they met through social media and in-person contacts.
Fig. 1.
Recovery-related resources (top) and activities (bottom) accessed by people who used social media for recovery (n = 100).
Social support and making connections. Among the 100 recovery social media users, 62 % accessed recovery support forums, 54 % connected with supportive friends or family, and 51 % followed recovery-focused creators or pages. Similarly, when respondents were asked what kind of recovery-related activities they engaged in on social media, the most common activities were related to social support. See Fig. 1.
A majority of recovery social media users added new recovery contacts on their social media (88 %, n = 88 of 100). They reported connecting with a mix of people they knew in person and met online: 77 % connected with people in recovery they had already met in person, while 73 % met new recovery peers online through social media. Overall, 63 % added both types of contacts, 10 % only added social media contacts, and 14 % only added in-person contacts.
Information-seeking. Information-seeking was common but less prevalent than social support: about half of recovery social media users (54 %, 54 of 100) searched for some kind of recovery-related information on social media. See Fig. 1 for details.
3.3. Recovery support forums on social media
This section focuses on the 62 recovery social media users who joined a support forum, the most frequently reported recovery-related activity on social media.
Support forum engagement. Among recovery support forum users, Facebook was the host of online support forums for nearly all (94 %, n = 58 of 62). Instagram support forums were used by 38 %, and TikTok support forums by 35 %. Use of support forums on other platforms was uncommon: Snapchat (13 %), Reddit (11 %), and Twitter (10 %). Half of forum users (47 %, n = 29 of 62) visited forums at least once per day, and 84 % visited at least weekly. Most support forum users (60 %) spent 15 minutes or more per visit.
Support forum discovery and motivation. Most forum users (81 %, n = 50 of 62) found the forums on their own instead of by recommendation of a friend or therapist. Of the 62 forum users, 35 completed a ranking question on their reasons for joining (see Table 4). The most commonly selected reasons were seeking motivation or advice. Although only 31 % selected practical resource access (e.g., food banks, job support), this was ranked highest in importance among those who selected it.
Table 4.
Reasons for joining online support forums (OSF), by frequency chosen and the median ranking of the reasons’ importance.
| Reason for joining OSF, ranked | freq | % | Median ranking |
|---|---|---|---|
| Get motivation | 22 | 63 % | 2.5 |
| Get advice | 20 | 57 % | 3.1 |
| SUD Information | 19 | 54 % | 2.7 |
| Connect with people | 17 | 49 % | 2.4 |
| Help others | 12 | 34 % | 2.4 |
| Help accessing resources | 11 | 31 % | 2.0 |
3.4. Perceived helpfulness of recovery social media use
Among recovery social media users (n = 100), most said they believed social media had been helpful in their recovery (72 % helpful, 16 % harmful, 12 % neutral).
Exposure to recovery-positive and use-positive content. We compared exposure to content types between recovery social media users (n = 100) and general social media users (n = 100). Both groups reported widespread exposure to both recovery-positive and use-positive content, see Fig. 2.
Fig. 2.
Frequency rating of seeing recovery-positive content (left) and use-positive (right) among recovery social media users (“Recovery SM”), and general social media users (“General SM”). The differences in distributions between the two groups were significant for recovery-positive content, but not use-positive content.
Ordinal logistic regression showed that recovery social media use was associated with significantly higher odds of seeing recovery-positive content (b = 0.65, SE = 0.26, t = 2.47, p = 0.014). Recovery social media users had 1.92 times the odds of reporting more frequent exposure, 95 % CI [1.14, 3.22]. One in three (36 %) of recovery social media users reported seeing recovery-positive content more than half the time, compared to one in five (15 %) for general social media users.
However, there was no evidence that recovery social media users were exposed to less use-positive content than general social media users. A second ordinal logistic regression found no significant difference between groups in exposure to use-positive content (b = 0.13, SE = 0.26, t = 0.49, p = 0.62). One in five (22 %) recovery social media users reported seeing use-positive content more than half the time, compared to one in six (16 %) for general social media users.
4. Discussion
This study investigated how and why individuals in recovery use social media to support their goals, with the aim of better understanding the growing role of digital platforms in SUD recovery. Nearly half of adults with SUD and past-year treatment experience recruited from treatment clinics in the northeastern U.S. reported using social media for recovery support. This rate is higher than in past research (Bergman et al., 2018, Gilbert et al., 2022).
These findings suggest that many with SUD seek online support despite unclear evidence of its benefits. One small cross-sectional study found patients reported lower rates of abstinence relative when using online recovery support compared to when they used face-to-face support (e.g., 12-step groups) (Grant and Dill-Shackleford, 2017), and another found negative associations between outcomes and online recovery support for men who had resolved an alcohol problem (Gilbert et al., 2022). However, this evidence is not strong. A longitudinal study found that those attending online meetings were less likely to be abstinent at baseline but had comparable rates one year later (Timko et al., 2022). No studies to date have rigorously tested whether online recovery support improves outcomes beyond traditional treatment or in-person peer support. While our results add that SUD patients at least perceive that social media support benefitted their recovery, more research is urgently needed to inform clinicians’ recommendations about digital support tools while in treatment and afterward.
Our findings provide preliminary insight into who is using social media for recovery support. Women were significantly more likely than men to do so, and adults over age 50 were significantly less likely than younger adults (ages 18–35) to engage in this way. Although the age finding is based on a small subgroup and should be interpreted cautiously, it aligns with broader trends in social media use, which skews younger and more female (Pew Research Center., 2024). Notably, women in our sample were more likely to use social media for recovery even among general social media users. Previous research found a similar, though not statistically significant, trend among those with resolved alcohol problems (Gilbert et al., 2022).
We also examined how participants used social media to support recovery. Consistent with past qualitative studies (Viera et al., 2023), most participants reported using social media to strengthen in-person treatment connections. Our findings extend this work by showing that many also sought new recovery-related relationships, followed recovery-related content creators, and elicited support from family and friends. These behaviors may reflect an intentional strategy to reshape one’s digital environment in ways that support recovery during or after formal treatment (Phelan et al., 2022). Participants also used social media to boost motivation and seek recovery-focused information, echoing findings from earlier qualitative studies (Viera et al., 2023). We found that participants reported celebrating recovery milestones at a comparable rate to a previous text analysis of TikTok recovery content (Russell et al., 2021), suggesting a degree of alignment between text analysis and self-report data.
Our results extend past work by showing that many participants joined structured recovery forums: groups with relatively stable memberships that allow for consistent exchange of recovery-related content and support (usually Facebook groups). These types of groups may better mirror the mutuality of in-person support services than more passive behaviors, such as following influencers or organizations. Many participants in these online groups reported high levels of engagement, including weekly participation and sessions lasting at least 15 minutes. This suggests that many interact with recovery-focused groups at levels comparable to in-person support, though more research is needed to determine benefits.
Most participants appeared to navigate social media recovery spaces without professional guidance. Four out of five reported finding support groups on their own, consistent with earlier qualitative research indicating that people in recovery typically manage their digital environments independently (Phelan et al., 2022). This raises concerns, as social media can also expose social media users to triggering or substance use-positive content. Such content was commonly encountered even by those using social media for recovery. Notably, one in six participants who sought recovery support through social media felt it ultimately harmed their recovery.
These findings support calls for greater algorithmic accountability, such as those made by Russell et al. (Russell et al., 2023). Algorithms that prioritize engagement may inadvertently expose social media users to harmful or triggering content. Addressing this issue could involve giving social media users more control over the types of content they see, enabling them to curate their digital environments in recovery-supportive ways. It may also be that online recovery support is safest and most effective for individuals who are more stable in their recovery and less vulnerable to triggering content. Future research should examine both of these possibilities, as well as whether algorithmic interventions could improve outcomes.
This study had several limitations. First, its cross-sectional design relied on participants’ ability to recall and summarize their social media behavior, which may have introduced systematic bias. Future longitudinal studies that assess behaviors close in time to when they occur could enhance confidence in findings. Second, our sample was predominantly White, non-Hispanic, and included many individuals with long-term SUD histories (>5 years). We were only able to test racial and sexual identity differences using broad groupings and found no significant effects. Results may differ considerably in more diverse samples or those with more representation among those earlier in their recovery. Third, the survey was conducted before the rapid growth of Bluesky, so usage of that platform was not captured. Finally, many survey items were developed specifically for this study, and some may have had limited validity or incomplete response options, as this area of research is relatively new and the validity of some of the concepts we assessed is not yet well-established.
In summary, nearly half of individuals with past-year SUD treatment reported using social media for recovery support, primarily to exchange support with others in recovery. Women were more likely to engage in this behavior, and older (50 + years) adults were less likely to engage. Most social media users perceived benefits, though some experienced harm. Future research should determine whether online recovery support leads to improved outcomes, identify who benefits most, and explore ways to optimize digital recovery environments to maximize those benefits.
CRediT authorship contribution statement
Wray Tyler B.: Writing – review & editing, Writing – original draft, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization. Jackson Kristina M.: Writing – review & editing, Validation, Supervision. Phelan Chanda: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Merrill Jennifer E.: Writing – review & editing, Validation, Supervision. Katz Abby P. M.: Writing – review & editing, Writing – original draft.
Disclosures
Tyler B. Wray is a paid scientific consultant for IAmSober, LLC. IAmSober had no role in the design, conduct, analysis, or reporting of this study
Declaration of Competing Interest
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Tyler B Wray reports a relationship with IAmSober, LLC that includes: consulting or advisory. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This work was supported by the National Institute on Drug Abuse (NIDA) of the National Institutes of Health (NIH) through a T32 postdoctoral training fellowship (5T32DA016184–18) awarded to the first author. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Footnotes
Supplementary data associated with this article can be found in the online version at doi:10.1016/j.dadr.2025.100331.
Appendix A. Supplementary material
Supplementary material
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