Methadone and Suboxone® mentions on Twitter: Thematic and Sentiment Analysis (Preprint)

Abstract

BACKGROUND Methadone and buprenorphine-naloxone (Suboxone®) have been discussed and compared extensively in the medical literature as effective treatments for opioid use disorder (OUD). While the evidence basis for the use of these medications is very favorable, less is known about the perceptions of these medications within the general public. OBJECTIVE This study aimed to use social media, specifically Twitter, to assess the public perception of these medications, and to compare the discussion content between each medication based on theme, subtheme, and sentiment. METHODS We conducted a mixed methods descriptive study analyzing individual microposts (“tweets”) that mentioned “methadone” or “suboxone”. We then categorized these tweets into themes and subthemes, as well as by sentiment and personal experience, and compared the information posted about these two medications, including in tweets that mentioned both medications. RESULTS We analyzed 900 tweets, most of which related to access (13.8% for methadone; 12.9% for suboxone®), stigma (15.3%; 14.0%), and OUD treatment (11.5%; 5.4%). Only a small proportion of tweets (16.4% for suboxone® and 9.3% for methadone) expressed positive sentiments about the medications, with few tweets describing personal experiences. Tweets mentioning both medications primarily discussed MOUD in general, rather than comparing the two medications directly. CONCLUSIONS Twitter content about methadone and suboxone are similar, with the same major themes and similar sub-themes. Despite the proven effectiveness of these medications, there was little dialogue related to their benefits or efficacy in the treatment of opioid use disorder. Perceptions of these medications may contribute to their underutilization in combatting opioid use disorder.

#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 38 studies [21, 22, 25, 29, 30, 32, 34–37, 40–48, 50, 52, 53, 55–58, 66, 71, 72, 75, 76, 79, 82, 86, 88–90] , analysed the sentiment of substance‐related content ( n = 14 753 551) 1 on social media. Most substance‐related content depicted substance use positively (76.3%).…”

“…Sixty studies [20–24, 26–30, 32–36, 38–40, 42–46, 48, 50, 52–55, 58–85, 87–91] analysed themes identified in substance‐related content ( n = 2 178 739) on social media. We summarised the themes into 22 overarching categories: Health, Safety and Harms , Barriers to Treatment , Legislation and Legalisation , Use , Entertainment , Promotions/Advertisements , Popular Culture , Traditional Culture , Informative , Sensory Characteristics , Access to Substances , Polysubstance Use , Paraphernalia , Perceived Benefits , Associated Qualities , Attitudes , Drug Effects , Social Interactions , Special Populations , Other Risky Behaviours , Criminal/Legal Consequences and Other (see Supporting information Table S1 for definitions).…”

“…Twenty‐eight studies, with a total of 15 792 280 tweets, reported the results of sentiment and thematic analyses on Twitter. Of these studies, 12 assessed e‐cigarette‐related content [24, 26– 28, 33, 34, 36, 37, 39, 43, 48, 53], nine assessed cannabis‐related content [22, 23, 38, 41, 42, 46, 47, 50, 51], five assessed opiate‐related content [20, 21, 25, 40, 56], five assessed tobacco‐related content [32, 35, 44, 45, 52], one assessed alcohol‐related content [30], and a single study assessed polysubstance use‐related content [29].…”