Tweeting Stigma: An Exploration of Twitter Discourse Regarding Medications Used for Both Opioid Use Disorder and Chronic Pain

Abstract

We examined the content of tweets on the social media site Twitter to better understand the contemporary discourse about medications for opioid use disorder (MOUD), how this chat contributes to the pervasive underpinnings of drug addiction, chronic pain stigma, and the impact it has on demand and availability of treatment. A retrospective review of tweets over 3 months containing keywords buprenorphine, naltrexone, methadone, or bupe was conducted resulting in 5,068 tweets. A content analysis was carried out focusing on a subset of tweets. Themes emerged from including suspicion and conspiracy theories about MOUD, and frustration and lack of control over their treatment options. Other tweets shared stigmatizing language and attitudes related to OUD/MOUD (e.g., “Junkies”). Twitter is a rich source of data reflecting thoughts, opinions, and sentiments entities regarding MOUD. However, this information can contain malicious comments that perpetuate stigma for people with OUD and result in avoidance of treatment.

#TurntTrending: a systematic review of substance use portrayals on social media platforms

“…A total of 15 905 182 substance‐related social media posts (15 804 353 text posts, 68 023 image posts, and 32 470 video posts) were assessed across all 73 studies. Twitter data was analysed in 34 studies [20–53]; 23 studies examined YouTube [54–76]; 10 assessed Instagram content [77–85]; 4 studies used Pinterest images [85–88]; TikTok videos were analysed in 2 studies [89, 90]; and Weibo content was assessed in a single study [91]. E‐cigarettes were the most common category analysed ( n = 24 studies), followed by tobacco ( n = 20 studies), cannabis ( n = 18 studies), opiates ( n = 6 studies), alcohol ( n = 4 studies), psychostimulants ( n = 1 study), stimulants/amphetamines ( n = 1 study), inhalants ( n = 1 study), novel psychoactive substances (NPS) ( n = 1 study) and polysubstance use ( n = 1 study).…”

“…Sample size of included studies varied within social media platforms, with the coding method influencing the quantity of substance‐related content posts coded (Supporting information Table S2). Favoured coding methods included manual coding [20–26, 29, 30, 32–34, 37–39, 42, 43, 48, 50–60, 62–91], machine learning [27, 28, 31, 35, 41, 45, 47, 49, 61] or a combination of manual and machine learning [36, 40, 44, 46]. Manual coding was the most common method, and typically involved one or more human coders categorizing data thematically and sentimentally using a codebook derived from data subsets.…”

“…A total of 15 905 182 substance‐related social media posts (15 804 353 text posts, 68 023 image posts, and 32 470 video posts) were assessed across all 73 studies. Twitter data was analysed in 34 studies [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ]; 23 studies examined YouTube [ 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , ...…”

“…Machine learning involved the use of algorithms, refined by manual coding of data subsets, to automatically code data via computer program. Studies using this method predominantly focused on Twitter data and extracted a higher number of substance‐related content posts [ 27 , 31 , 35 , 41 , 45 , 47 , 49 , 61 ], the largest being 13 233 837 tweets [ 47 ]. There was no difference in findings from manually coded and machine learning samples.…”

“…Studies included in this review identified that user‐generated content was most likely to be responsible for the dissemination of substance‐related content on social media [21–23, 25, 26, 28–31, 33–37, 39–52, 54–56, 58–82, 84, 85, 87–90]. Personal accounts typically expressed positive sentiments toward substance use and shared content relating to Informative or Entertaining themes.…”