ArticlePDF Available

Assessing perceptions about medications for opioid use disorder and Naloxone on Twitter

Authors:

Abstract

Introduction Qualitative analysis of Twitter posts reveals key insights about user norms, informedness, perceptions, and experiences related to opioid use disorder (OUD). This paper characterizes Twitter message content pertaining to medications for opioid use disorder (MOUD) and Naloxone. Methods In-depth thematic analysis was conducted of 1,010 Twitter messages collected in June 2019. Our primary aim was to identify user perceptions and experiences related to harm reduction (e.g., Naloxone) and MOUD (e.g., sublingual and Extended-release buprenorphine, Extended-release naltrexone, Methadone). Results Tweets relating to OUD were most commonly authored by general Twitter users (43.8%), private residential or detoxification programs (24.6%), healthcare providers (e.g., physicians, first responders; 4.3%), PWUOs (4.7%) and their caregivers (2.9%). Naloxone was mentioned in 23.8% of posts and authored most commonly by general users (52.9%), public health experts (7.4%), and nonprofit/advocacy organizations (6.6%). Sentiment was mostly positive about Naloxone (73.6%). Commonly mentioned MOUDs in our search consisted of Buprenorphine-naloxone (13.8%), Methadone (5.7%), Extended-release naltrexone (4.1%), and Extended-release buprenorphine (0.01%). Tweets authored by PWUOs (4.7%) most commonly related to factors influencing access to MOUD or adverse events related to MOUD (70.8%), negative or positive experiences with illicit substance use (25%), policies related to expanding access to treatments for OUD (8.3%), and stigma experienced by healthcare providers (8.3%). Conclusion Twitter is utilized by a diverse array of individuals, including PWUOs, and offers an innovative approach to evaluate experiences and themes related to illicit opioid use, MOUD, and harm reduction.
Assessing perceptions about medications for opioid use
disorder and Naloxone on Twitter
Babak Tofighi, MD, MSca, Omar El Shahawy, MD, PhD, MPHa, Andrew Segoshi, MDa,
Katerine P. Moreno, BSb, Beita Badiei, BSa, Abeed Sarker, PhDc, Noa Krawczyk, PhDa
aDepartment of Population Health, NYUMC, New York, NY, USA;
bUniversidad Surcolombiana, Neiva, Colombia;
cDepartment of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
Abstract
Introduction: Qualitative analysis of Twitter posts reveals key insights about user norms,
informedness, perceptions, and experiences related to opioid use disorder (OUD). This paper
characterizes Twitter message content pertaining to medications for opioid use disorder (MOUD)
and Naloxone.
Methods: In-depth thematic analysis was conducted of 1,010 Twitter messages collected in June
2019. Our primary aim was to identify user perceptions and experiences related to harm reduction
(e.g., Naloxone) and MOUD (e.g., sublingual and Extended-release buprenorphine, Extended-
release naltrexone, Methadone).
Results: Tweets relating to OUD were most commonly authored by general Twitter users
(43.8%), private residential or detoxification programs (24.6%), healthcare providers (e.g.,
physicians, first responders; 4.3%), PWUOs (4.7%) and their caregivers (2.9%). Naloxone was
mentioned in 23.8% of posts and authored most commonly by general users (52.9%), public health
experts (7.4%), and nonprofit/advocacy organizations (6.6%). Sentiment was mostly positive about
Naloxone (73.6%). Commonly mentioned MOUDs in our search consisted of Buprenorphine-
naloxone (13.8%), Methadone (5.7%), Extended-release naltrexone (4.1%), and Extended-release
buprenorphine (0.01%). Tweets authored by PWUOs (4.7%) most commonly related to factors
influencing access to MOUD or adverse events related to MOUD (70.8%), negative or positive
experiences with illicit substance use (25%), policies related to expanding access to treatments for
OUD (8.3%), and stigma experienced by healthcare providers (8.3%).
Conclusion: Twitter is utilized by a diverse array of individuals, including PWUOs, and offers
an innovative approach to evaluate experiences and themes related to illicit opioid use, MOUD,
and harm reduction.
Keywords
Opioid use disorder; social media; twitter; medications for opioid use disorder; naloxone;
buprenorphine-naloxone; extended-release naltrexone
CONTACT Babak Tofighi, MD, MSc Babak.tofighi@nyumc.org 180 Madison, New York, NY 10016, USA.
HHS Public Access
Author manuscript
J Addict Dis
. Author manuscript; available in PMC 2021 July 16.
Published in final edited form as:
J Addict Dis
. 2021 ; 39(1): 37–45. doi:10.1080/10550887.2020.1811456.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Introduction
Nearly all Americans regularly use the Internet (90%), and the majority are connected to
social media (72%).1,2 As a result, social media has become an important tool by which
health researchers can study perceptions and patterns of health information sharing across
multiple communities. Social media research is an especially important way to reach special
interest groups that may be less represented in traditional health research including young
adults (18–29 years old, 90%), Hispanic/Latinos (70%), African-Americans (68%), and
women (70%).2 Twitter, which is used by approximately 22% of US adults and attracts a
broad array of conversations related to health, has become a useful tool to assess current
events and experiences related to a range of health topics.3 Twitter is especially popular
because it is essentially a micro-blogging platform and user posts on it are typically publicly
available.
Over recent years, health researchers have utilized Twitter to study substance use disorders,
including alcohol, tobacco, cannabis, psychostimulants, and opioids. Given the high stigma
associated with substance use, social media allows many users and their networks to openly
share information with reduced concern for judgment and retaliation. Opioid use disorder
(OUD), which affects over two million Americans and is the primary driver of ongoing
overdose deaths across the U.S., is of particularly high interest to health researchers,
especially given incessant gaps in access to effective treatment and services to reduce
opioid-related harms.4
Preliminary research pertaining to OUD on Twitter have yielded some important findings:
Mackey and colleagues reported that Twitter can be used to facilitate illicit sales of
prescription opioids.5 Sarker et al., utilized supervised classification and natural language
processing for monitoring and classifying posts with prescription opioid misuse content.6
Natural language processing has also been harnessed to identify prescription opioid misuse
(i.e., Oxycontin®) on Twitter and assess the location of prescription opioid misuse tweets
relative to state-level OUD prevalence estimates from nationally representative data.7
Finally, Tofighi et al. identified how peer-to-peer exchanges on Twitter may facilitate: 1)
access to heroin and prescription opioids; 2) sharing opioid withdrawal experiences; and 3)
exchanges of emotional support and recovery resources among family and friends of people
who use opioids (PWUO).
Despite the promise of Twitter as a scalable resource for OUD-related information from a
large population, there is a paucity of studies that have investigated the perceptions,
experiences, and information posted about medications for opioid use disorder (MOUD) and
harm reduction (e.g., Naloxone) to reduce overdose and other health harms. Two effective
approaches to reducing opioid overdose fatalities include improving access to Naloxone,
which effectively reverses opioid overdose, and improving entry and retention on MOUD,
including Methadone, Buprenorphine-naloxone, and Extended release naltrexone.8,9 Prior
work suggests the limited frequency of credible posts by clinicians and public health experts
relating to OUD relative to marketing and stigmatizing content related to PWUOs.10 Still,
more is needed to understand the nature of the content that is being circulated related to
these services as well as the different driving sources of this content.
Tofighi et al. Page 2
J Addict Dis
. Author manuscript; available in PMC 2021 July 16.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
In light of ongoing opioid overdose fatalities and underutilization of effective harm
reduction and treatment strategies for PWUOs, this study sought to assess perceptions and
experiences relating to Naloxone and MOUDs (e.g., Methadone, Buprenorphine-naloxone,
Extended-release naltrexone, and Extended-release buprenorphine) and how these vary
based on user type (e.g., PWUOs, family/friends of PWUO, and healthcare providers).9
These findings may inform public health interventions that leverage social media platforms
to rapidly increase access to evidence-based harm reduction and treatment resources for
hard-to-reach populations with OUD whom experience disparities in OUD outcomes.
Methods
Data collection relied on the Twitter Application Programing Interface (API), which enables
the collection of a sample of public posts on Twitter using keywords. The study team used
an open source Python module,
langdetect
, to acquire tweets and retweets that were not
geolocated. Posts archived between May 26 and June 6, 2019 were collected in August
2019. Tweets (n = 5,780) mentioning opioids were collected using opioid keywords (i.e.,
opioids, heroin, opiates, dope, oxy*
,
oxycontin, pills, percocet
) and relevant medications for
OUD (i.e.,
narcan, naloxone, bup*
,
suboxone, zubsolv, sublocade, vivitrol, naltrexone,
Methadone
). These keywords were based on prior studies evaluating Twitter and technology
use patterns among PWUO and modified after a preliminary review of our Twitter sample.
5,7,10 Non-relevant tweets (n = 4,771) were manually excluded if they were non-English
language posts, “retweets” that lacked any additional content, tweets that were related to a
thread and necessitated further contextualization to be fully understood, consisted of links or
hashtags not related to OUD, MOUD, or Naloxone. The study team then manually analyzed
1,009 posts and removed tweets that were duplicates (n = 400), metaphors or sarcastic
comments not related to OUD (n = 69), pertaining to alcohol use disorder only (n = 29), or
referring to cannabis use only (n = 2) (Figure 1).
The coding schema was derived manually by content experts in OUD (BT, OE, AS) and a
trained medical student (AS) using a subset of Tweets based on the grounded theory
approach. The lead author, who is an expert in OUD (BT) identified the structured coding
categories and reviewed the schema with the two other coders (OE, AS) on the scope of each
category. The study team conducted three meetings to iteratively refine the coding schemes
after a review of 210 randomly selected tweets (n = 70/meeting). Each tweet was then
independently coded into its respective categories by one of the coders (AS, OE, BT) using a
structured coding excel workbook. The coding categories focused on differentiating the
author of the post, intended audience, overall themes, and issues or experiences related to
MOUD. The coding categories were not mutually exclusive, that is each tweet could reflect
more than one of the following categories: 1) source (PWUOs, family/friends of PWUOs,
healthcare providers, addiction treatment program); 2) intended audience per post @replies,
hashtags, and Tweet content; 3) sentiment (positive, negative, neutral); 4) genre (i.e.,
personal experience, joke/sarcasm, news, policy, education, recovery services, emotional or
concrete support for recovery, encouraging illicit opioid use); and 5) theme relating to the
content conveyed by authors (e.g., overdose, MOUD, illicit opioid use, PWUOs). Additional
attention was given to user claims requiring evidence (i.e., medical research, news). The
source of the tweet was categorized as a PWUO if they presented content meeting criteria
Tofighi et al. Page 3
J Addict Dis
. Author manuscript; available in PMC 2021 July 16.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
for OUD as outlined by the Diagnostic and Statistical Manual for Mental Disorders, elicited
an overdose episode, opioid use for non-medical purposes, or OUD treatment experience, or
requested access to illicit opioids or resources to address their OUD.11 The interrater
agreement of the coded variables was assessed via a random sample of n = 150 tweets
(29.5%) coded independently by the three coders. Mean Cohen’s
κ
for Tweet coding
categories was 0.95 (range 0.81–1.00).12,13
Results
Content analysis of twitter author categories
Tweets related to OUD were most often authored by general Twitter users (43.8%), private
residential or detoxification programs (24.6%), healthcare providers (e.g., physicians, first
responders; 4.3%), PWUOs (4.7%) and their caregivers (2.9%). Other authors included
politicians (n = 3), blog writers (n = 3), law enforcement (n = 2), magazines related to OUD
(n = 2), pharmaceutical company (n = 1), and a foundation (n = 1; see Table 1). The study
team was unable to categorize authors for 3.3% of posts (n = 17) due to the limited
information available in the tweet or the profile associated with the tweet (see Table 1).
Private residential and inpatient detoxification programs (24.6%, n = 125) infrequently cited
MOUD as a part of their treatment protocols (6.4%, n = 8/125) and some programs
encouraged “detoxification” off of Methadone or Buprenorphine-naloxone (4%, n = 5/125).
Treatment programs sought to garner legitimacy by posting “proven home detox kits,” links
to popular press coverage about their program, hashtags of popular press despite no articles
from these sources about the programs (e.g., “#reuters #foxnews), updating readers with
podcast interviews and magazine articles about the program’s CEO that was actually
published by the program’s own website, and quoting celebrities’ positive treatment
experiences after entering their program.
Tweets authored by PWUOs (4.7%) related to: 1) treatment (70.8%, 17/24), including
barriers to accessing MOUD due to cost or lack of providers prescribing MOUD,
motivations for seeking MOUD treatment versus inpatient detoxification treatment and an
abstinence based approach, positive and negative experiences utilizing addiction treatment
services (e.g., withdrawal symptoms persistent cravings), perceptions regarding MOUD,
challenges with self-tapering off of Methadone or Buprenorphine-naloxone, experiences
with worsening withdrawal symptoms following admission to inpatient detoxification
treatment, and adverse events related to MOUD; 2) negative or positive active use (25%,
6/24) experiences with heroin, prescription opioids, or poly-substance use; 3) politics/policy
(8.3%, 2/24) related to expanding access to MOUD or inpatient detoxification treatment; 4)
stigma experienced by healthcare providers; and/or 5) surviving an overdose event (4.2%,
1/24). Posts by family or friends of PWUO often recounted harrowing experiences with
acquaintances diagnosed with OUD or passing away from an opioid overdose, and the
importance of increasing access to MOUD and Naloxone (2.9%).
Healthcare providers (e.g., physicians, nurses; 4.3%) emphasized the importance of
expanding access to MOUD and Naloxone citing personal experiences, peer-reviewed
literature, or state and federal guidelines. Providers also highlighted barriers to accessing
Tofighi et al. Page 4
J Addict Dis
. Author manuscript; available in PMC 2021 July 16.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
MOUD and Naloxone, including limited information among providers and patients, out-of-
pocket costs, need for contact information to allow patients to enter programs offering
MOUD, and addressing stigma related to MOUD. Although general users frequently
tweeted about findings disseminated in peer-reviewed manuscripts (e.g., barriers to OUD
treatment, effectiveness of MOUD), public health experts infrequently posted informational,
treatment, or policy content pertaining to opioids, OUD, and treatments for OUD (2.4%).
Claims shared by all users were primarily based on personal experiences (33.8%),
information posted by a private detoxification and/or residential treatment program (27.5%),
a public health expert (e.g., departments of health, academic or peer-reviewed research;
15.1%), and news (e.g., television, newspaper; 10.8%).
Content analysis of twitter post categories
Tweets were coded into non-mutually exclusive categories (see Table 2). The most common
content categories related to OUD posted in our sample referred to treatment (70.7%), policy
or political comments (27.7%), and harm reduction (24.8%).
Naloxone was mentioned in 23.8% of posts and authored most commonly by general users
(n = 65, 52.9%), public health experts (n = 9, 7.4%), nonprofit/advocacy organizations (n =
8, 6.6%), and news (n = 8, 6.6%). Sentiment was mostly positive about Naloxone (73.6%).
Negative posts about Naloxone (n = 19, 15.7%) were associated with stigmatizing comments
about PWUOs who would be “enabled” to use more illicit opioids due to Naloxone, claims
that Naloxone does not reduce overdose, and criticisms of government policies misallocating
resources for PWUOs rather than more “legitimate” health needs such as opioid analgesics
for chronic pain patients or needles for diabetic patients.
Commonly mentioned MOUDs in our search consisted of Buprenorphine-naloxone (13.8%),
Methadone (5.7%), Extended-release naltrexone (4.1%), and extended-release
buprenorphine (0.01%). Users frequently posted the importance of expanding access to
MOUD [e.g., general users (n = 40), family or friends of PWUO (n = 4), PWUO (n = 2),
healthcare providers (n = 2), and public health experts (n = 1)].
Buprenorphine-naloxone (13.8%) was most commonly posted by general users (n = 34,
48.6%), addiction treatment programs (n = 8, 11.4%), and healthcare providers (n = 8,
11.4%). Approximately half of posts pertaining to Buprenorphine-naloxone were positive (n
= 35, 50%). Negative comments pertaining to the medication (n = 23, 32.9%), were
generated by general users (n = 11), addiction treatment programs (n = 7) and PWUOs (n =
4) emphasizing an abstinence-based approach to treatment requiring “faith” and
“willpower.” Additional posts critical of Buprenorphine-naloxone targeted policies
expanding access to MOUD for PWUOs while chronic pain patients were unfairly
discontinued off of opioid analgesics. Several posts that were supportive of Buprenorphine
treatment highlighted ongoing barriers to accessing such treatment and attributed to
increased medication costs, limited access to prescribers, residential treatment or inpatient
detoxification programs not inducting patients to buprenorphine, stigma attributed to opioid
antagonist therapies by the general public and criminal justice system, and rigid clinic
protocols terminating care for patients suspected of illicit substance use.
Tofighi et al. Page 5
J Addict Dis
. Author manuscript; available in PMC 2021 July 16.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Posts regarding Methadone (5.7%) were mostly authored by general users (n = 10, 34.5%)
and PWUOs (n = 8, 27.6%). Positive sentiment relating to Methadone (n = 14, 48.3%)
included its beneficial treatment outcomes shared by some PWUOs and healthcare
providers, and importance of offering office-based treatment with Methadone. Negative
perceptions of Methadone (n = 11, 37.9%) highlighted challenges in tapering off of
Methadone compared to heroin or Buprenorphine-naloxone, the benefits of antagonist
treatment with Extended-release naltrexone versus “trading one addiction for another” in the
form of Methadone, and risks of overdose with illicit Methadone use shared by two PWUOs.
Several detoxification programs encouraged readers to utilize their services to “detox” off of
Methadone.
Fewer posts cited Extended-release naltrexone (4.1%) and were authored by general users (n
= 7, 33.3%), addiction treatment programs (n = 6, 28.6%), and healthcare providers (n = 3,
14.3%). Sentiment regarding Extended-release naltrexone was mostly positive (n = 14,
66.7%) and described as a “godsend,” with one user claiming that “its probably more
effective than Methadone and suboxone.” One Twitter user self-reported the benefits of the
treatment for their recovery openly: “I took Vivitrol for 3 months after I left treatment. I
know it was a huge factor in maintaining my sobriety especially in the beginning. I try to tell
everyone about it.” Posts critical of Extended-release naltrexone centered on its mandated
use in criminal justice settings versus expanding access to opioid agonist therapies (n = 2),
and higher cost (n = 1): “We need our courts to stop pushing Vivitrol and abstinence, and to
stop violating people for #Methadone and #suboxone.” Some Twitter users also shared
adverse experienced related to the injection, including nausea, gluteal pain, and swelling (n
= 3).
All four posts mentioning Buprenorphine extended-release were positive and authored by a
general user, physician, PWUO, and an addiction treatment program. Additional posts
mentioned heroin-assisted treatment (n = 9), all of which were posted by general users and
were positive. Seven posts pertaining to cannabis or cannabidiol products were published
with positive sentiment and published by general users (n = 6, 85.7%). Additional posts
recommended “home detox kits” that lacked information on active ingredients (n = 6),
Ibogaine (n = 4), “natural remedies” (n = 2), and ketamine (n = 1) to support detoxification
from illicit opioids, Buprenorphine-nalox-one, and/or Methadone without any references to
support such claims.
Discussion
The current study demonstrates the feasibility of leveraging Twitter to identify informational
content, experiences, and perceptions related to illicit opioid use, opioid overdose, and
treatments for OUD. This work adds to a growing body of research about the unique
opportunity that social media research provides to explore health topics that are sensitive or
stigmatized across multiple sectors of society.5,6,7,10
Analysis of posts reveals new understandings of sentiment and informedness relating to
OUD and the types of sources through which this information is being shared. Importantly,
there was a paucity of Tweets authored by public health experts, healthcare providers, and
Tofighi et al. Page 6
J Addict Dis
. Author manuscript; available in PMC 2021 July 16.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
nonprofit/advocacy organizations pertaining to Naloxone and MOUD. Most posts relating to
Naloxone and MOUD were authored by general users and private treatment programs that
shared opinions or advertisements rather than any evidence-based content. Negative
sentiment targeting Naloxone, MOUD, and PWUOs were less frequent but highlighted a
concerning proportion of posts authored primarily by general users and private treatment
programs that mislead readers and stigmatized PWUOs and MOUD. Lastly, posts informing
readers about emerging pharmacotherapies for OUD, including Extended-release naltrexone
(4.1%) and Buprenorphine extended-release (0.01%) were uncommon.
Twitter content revealed multiple ways by which PWUOs and caregivers use the platform to
reveal circumstances related to stigma, illicit opioid use, overdose events, and experiences
and perceptions regarding accessing MOUD. One strategy to enhance the availability of
evidence-based content among PWUOs and their caregivers is to identify novel strategies
that enhance the reach of “peer experts” with lived experiences relating to MOUD and
Naloxone in social media.14 A concerning finding was the frequent use of Twitter by
commercial treatment programs to promote remedies (e.g., “home detox kits”) and services
(e.g., rapid detoxification, residential treatment) that were not verifiable or evidence based.
In some instances, commercial vendors and inpatient treatment programs disparaged MOUD
and encouraged PWUOs to procure their services (e.g., Ibogaine, various formulations of
Cannabidiols, “herbal remedies” consisting of unknown active ingredients, and creams).
Although Twitter is uniquely positioned to counter misleading claims or negative sentiment
pertaining to MOUD and harm reduction, few public health experts, harm reduction
programs, and healthcare providers were actively identified in our search, and therefore
likely make up a minority of active users commenting on these topics.
These findings therefore call for an urgent need for public health agencies to fully harness
social media platforms to scale-up information and access to evidence-based content, harm
reduction, and treatment resources. Expanding reach of valuable information to hard-to-
reach populations with OUD whom commonly utilize Twitter has the potential to reduce
disparities in OUD outcomes with minimal burden on caregivers, health systems, and state
agencies1. Distinct opportunities for public health interventions include: 1) leveraging
advances in natural language processing to offer “just-in-time” prompts linking PWUOs and
their caregivers to treatment and harm reduction services in response to posts consisting of
requests for help securing such resources, adverse experiences related to illicit opioid use,
and opioid overdose events; 2) incorporating geographical information systems in social
media to enhance linkages to nearby harm reduction and treatment services; 3) promoting
support networks among caregivers and peers to sustain protective behavior change,
adherence with MOUD, harm reduction, and treatment services; 4) confronting stigma
among general Twitter users posting jokes or sarcastic comments about PWUO or policies
addressing OUD; and 4) refine a Twitter-based surveillance system using natural language
processing to identify OUD-related content, public attitudes, and allocate harm reduction
and treatment services over time.
Limitations to this study include potential interrater variability and misinterpretation of
posts, lack of generalizability of our initial corpus of Tweets based on our limited number of
search terms, and harvesting only a fraction of posts available in the Twitter firehose.
Tofighi et al. Page 7
J Addict Dis
. Author manuscript; available in PMC 2021 July 16.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Additional qualitative research using Twitter is needed to confirm our findings, including
interviews with Twitter users with OUD and textual analysis of post content. Lastly, we
included tweets within a rather brief period of time period that may not reflect emerging
perceptions and experiences related to OUD.
Funding
This study was supported by the National Institute on Drug Abuse (K23DA042140-01A1).
References
1. Demographics of internet and home broadband usage in the United States. Pew Research Center:
Internet, Science & Tech; 2019 6 12 [accessed 2020 May 17]. https://www.pewresearch.org/
internet/fact-sheet/internet-broadband.
2. Demographics of social media users and adoption in the United States. Pew Research Center:
Internet, Science & Tech; 2019 6 12 [accessed 2020 May 17]. https://www.pewinternet.org/fact-
sheet/social-media/.
3. Sinnenberg L, Buttenheim AM, Padrez K, Mancheno C, Ungar L, Merchant RM. Twitter as a tool
for health research: a systematic review. Am J Public Health. 2017;107(1):e1–e8. doi:10.2105/
AJPH.2016.303512.
4. Center for Behavioral Health Statistics and Quality. 2016 national survey on drug use and health:
detailed tables. Rockville, MD: Substance Abuse and Mental Health Services Administration; 2017.
5. Mackey TK, Kalyanam J, Katsuki T, Lanckriet G. Twitter-based detection of illegal online sale of
prescription opioid. Am J Public Health. 2017;107(12): 1910–5. doi:10.2105/AJPH.2017.303994.
[PubMed: 29048960]
6. Chary M, Genes N, Giraud-Carrier C, Hanson C, Nelson LS, Manini AF. Epidemiology from
tweets: estimating misuse of prescription opioids in the USA from social media. J Med Toxicol.
2017;13(4):278–86. doi:10.1007/s13181-017-0625-5. [PubMed: 28831738]
7. Sarker A, O’Connor K, Ginn R, Scotch M, Smith K, Malone D, Gonzalez G. Social media mining
for toxi-covigilance: automatic monitoring of prescription medication abuse from Twitter. Drug Saf.
2016;39(3): 231–40. doi:10.1007/s40264-015-0379-4. [PubMed: 26748505]
8. Lee JD, Nunes EV, Novo P, Bachrach K, Bailey GL, Bhatt S, Farkas S, Fishman M, Gauthier P,
Hodgkins CC, et al. Comparative effectiveness of Extended-release naltrexone versus
Buprenorphine-naloxone for opioid relapse prevention (X:BOT): a multicentre, open-label,
randomised controlled trial. The Lancet. 2018;391(10118):309–18. doi:10.1016/
S0140-6736(17)32812-X.
9. Blanco C, Volkow ND. Management of opioid use disorder in the USA: present status and future
directions. The Lancet. 2019;393(10182):1760–72. doi:10.1016/S0140-6736(18)33078-2.
10. Tofighi B, Aphinyanaphongs Y, Marini C, Ghassemlou S, Nayebvali P, Metzger I, Raghunath A,
Thomas S. Detecting illicit opioid content on Twitter. Drug Alcohol Rev. 2020;39(3):205–8.
doi:10.1111/dar.13048. [PubMed: 32202005]
11. Association AP. Diagnostic and statistical manual of mental disorders (DSM-5®). Washington,
DC: American Psychiatric Pub; 2013.
12. Viera AJ, Garrett JM. Understanding interobserver agreement: the kappa statistic. Fam Med.
2005;37(5): 360–3. [PubMed: 15883903]
13. Cohen J A coefficient of agreement for nominal scales. Educational and Psychological
Measurement. 1960;20(1):37–46. doi:10.1177/001316446002000104.
14. Vydiswaran VGV, Reddy M. Identifying peer experts in online health forums. BMC Med Inform
Decis Mak. 2019;19(S3). doi:10.1186/s12911-019-0782-3.
Tofighi et al. Page 8
J Addict Dis
. Author manuscript; available in PMC 2021 July 16.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Figure 1.
Flow chart of search and exclusion of tweets.
Tofighi et al. Page 9
J Addict Dis
. Author manuscript; available in PMC 2021 July 16.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Tofighi et al. Page 10
Table 1.
Author categories for tweets related to OUD.
User type definition Users (n =
509) % (n) Tweet examples
General 43.8 (223) Most of the time it doesn’t start with heroin. It’s trying someone’s moms norco or percocets at a party and THAT leads to heroin. Please let’s educate and take
care of each other. 5 people have OD’d at the bus stop outside my apt in the last 8 months. I carry Narcan in my purse. [link]
Private residential
and/or inpatient
detoxification
programs
24.6 (125) Are you or someone you know suffering from a prescription drug or heroin problem? Help is available. Call ###-###-#### for treatment resources.
PWUO 4.7 (24) @username I feel like this too. I think it’s gotten worse in my late 20’s for sure. I’d like to think it’s clinical depression or some lingering affect of heroin
addiction/recent sobriety. Ketamine treatment legitimately worked but the physician in my town stopped taking my insurance.
Healthcare Provider 4.3 (22) @username Why? It’s not 2002–2004 anymore. 2004 in NY you were paying $1500+ just to SEE the doctor to get on Suboxone. I have the easiest time
getting clients on Suboxone/Subutex in CA, Vivitrol is another story.
News 3.1 (16) ‘I’m trying not to die right now’: Why opioid-addicted patients are still searching for help [link]
Caregivers of PWUO 2.9 (15) @username I appreciate his tolerance levels will have dropped but I can’t understand why his treatment would have to be cut for a month - how does this help
the situation? he was telling me how proud he was he had been off heroin for a week before all this happened
Nonprofit/Advocacy 2.9 (15) Interested in accessing resources during PRIDE? Here is a map of the safer sex spots we have set up along the route! Want more info on naloxone and
treatment services? Give the Revive. Survive. OverDose Prevention Team a call at ###-###-#### # iprevent #pride #safersex #naloxone [link]
Podcast 2.6 (13) Are you talking with Colin Morrison [link] ###-###-#### #sober #treatement #intervention #addiction #dependency #detox #relapse #Malibu #recovery #wsj
#nytimes #reuters #forbes #nasdaq #chicago #newyork #business #cnn #bet #foxnews #CBD #MAGA #sports [link]
Public Health Expert 2.4 (12) OD ALERT: Saint Paul has had five suspected heroin overdoses in the last 36 hours.
If you suspect an overdose:
Call 911 immediately
Administer Narcan
Share this information
Seek help for addiction.
Read more [link]
Chronic pain patient 2.2 (11) @username Yeah, I have a benzo, but also am on opioids for chronic pain, and my doctor is quite concerned about the two. He makes me carry Narcan with
me due to the potential for overdose even at prescribed doses. So it’s an option! And I use it when it’s bad.
Website/Blog 1.0 (5) Hello @username, are you looking for #Alcohol & #Drug #Addiction #Rehab #Treatment #Tips Alcohol and drug addiction represent a growing #public
#health crisis. This blog can give you the help you need to #detox, rehab, and get freedom from addiction. [link]
J Addict Dis
. Author manuscript; available in PMC 2021 July 16.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Tofighi et al. Page 11
Table 2.
Categories of tweets related to OUD identified via content analysis (n = 509).
Category Example
Post frequency
related to OUD,
%1 (n)
Treatment Anyone know if PCP in CA can write for Methadone? If no, who has the authority? Some counties are Suboxone only. PCP trying 2 help by sending me 2
MAT afraid of health consequences w/ Methadone detox after 20 years. #notanaddict just need my Methadone Does MAT take #CPP? 70.7 (360)
Politics/Policy Pathetic time-wasting “efforts” of @username inventing an “ecig” epidemic while ignoring #opioids during his tenure - almost kills 2-year-old. [link] 27.7 (141)
Harm Reduction #Narcan (naloxone) is designated as an opiate antidote, meaning it can reverse the effects of opioids after someone has taken them. @username is a
nonprofit group formed to facilitate prevention efforts in Shelby County to combat substance abuse and its consequences. 24.8 (126)
Active Use @username It is the catalyst for many users. Starts with pain / injury and becomes addictive. Nearly 80 percent of Americans using heroin (including those
in treatment) reported misusing prescription opioids prior to using heroin. 17.7 (90)
Overdose News wish Canada would get a grip on the addiction crisis..my son is in dire need..he’s been calling detox for 3 weeks … no beds..in the meantime he’s
overdosed 2x..#heartbroken Paramedics revive Beechview girl, 11, who overdosed on heroin [link] 13.8 (70)
11.0 (56)
Peer Support @username And this is NOT to downplay/dismiss the ‘addiction’. We KNOW it can be so so strong. It is however, encouragement to adjust your focus onto
yourself. Once your reasons/motives/drives are uncovered, they can be tackled. Hugs. Detox is painful, but important. 2.7 (36)
Pain Management @username The narrative for persons having legitimate, Physician-treated chronic pain-management issues requiring opioids is a valid one. Unfortunately,
CNS depressants/opioids have hundreds of narratives; many which don’t involve a physician but involve Narcan, some involving OD deaths. 5.9 (30)
PWUO @username We have no idea the duration or depth of her addiction- each withdrawl is different in time & degree dep on abuse. When she was in the hosp
she started detox, had taken pills only after fight with username. Keeping a calendar on her sobriety wont match unless your with her 24/7. 5.1 (26)
Education We just finished a dynamic #webinar with local health departments in #newyork on using #academicdetailing to improve access to #suboxone for treatment
of #opioidusedisorder! #publichealth #OpioidCrisis #opioidepidemic #buprenorphine #mat #behaviorchange #stigma [link] 2.2 (11)
Jokes/Sarcasm Well you gave free condoms for the AIDS crisis, how about some free Narcan and Fentanyl test strips for the addicts that illegally abuse opioids. Come On
Now !! #fakeopioidcrisis #opioidcrisis #personalresponsibility 1.2 (6)
Stigma Does It Really Matter How We Talk About Addiction? Words can hurt when they reinforce misconceptions. Read more:
#detox #healthylifestyle #wellness #rehab #drug #addiction #alcohol #therapist #abuse #program #recovery [link] >1.0 (4)
News *Cops Saves Overdose Victim *A #RedHook police officer saved a man overdosing on opioids by administering Narcan. #DailyVoice #Dutchess [link] >1.0 (3)
1
Content frequency may be greater than 100% because categories are not mutually exclusive.
J Addict Dis
. Author manuscript; available in PMC 2021 July 16.
... However, participants who had been in OMT prior to XR-NTX treatment described how they faced stigma and ignorance from the wider society, similar to a recent systematic review of qualitative studies of OMT patient experiences (Steiro et al., 2020). A public perception might indeed be that people with opioid dependence need to leave OMT eventually, for the treatment to be judged successful, or recovery to be considered complete (Randall-Kosich et al., 2020;Tofighi et al., 2020). The association between motivation for XR-NTX and stigma regarding OMT was also discussed by Gauthier et al. (2021), who suggested improving patient education to mitigate the impact of stigma. ...
Article
Full-text available
Background Extended-release naltrexone (XR-NTX), an opioid antagonist, has demonstrated equal treatment outcomes, in terms of safety, opioid use, and retention, to the recommended OMT medication buprenorphine. However, premature discontinuation of XR-NTX treatment is still common and poorly understood. Research on patient experiences of XR-NTX treatment is limited. We sought to explore participants' experiences with discontinuation of treatment with XR-NTX, particularly motivation for XR-NTX, experiences of initiation and treatment, and rationale for leaving treatment. Methods We conducted qualitative, semi-structured interviews with participants from a clinical trial of XR-NTX. The study participants (N = 13) included seven women and six men with opioid dependence, who had received a minimum of one and maximum of four injections of XR-NTX. The study team analyzed transcribed interviews, employing thematic analysis with a critical realist approach. Findings The research team identified three themes, and we present them as a chronological narrative: theme 1: Entering treatment – I thought I knew what I was going into; theme 2: Life with XR-NTX – I had something in me that I didn't want; and theme 3: Leaving treatment – I want to go somewhere in life. Patients' unfulfilled expectations of how XR-NTX would lead to a better life were central to decisions about discontinuation, including unexpected physical, emotional, or mental reactions as well as a lack of expected effects, notably some described an opioid effect from buprenorphine. A few participants ended treatment because they had reached their treatment goal, but most expressed disappointment about not achieving this goal. Some also expressed renewed acceptance of OMT. The participants' motivation for abstinence from illegal substances generally remained. Conclusion Our findings emphasize that a dynamic understanding of discontinuation of treatment is necessary to achieve a long-term approach to recovery: the field should understand discontinuation as a feature of typical treatment trajectories, and discontinuation can be followed by re-initiation of treatment.
... Recent research related to our work has attempted to analyze publicly available social media chatter about MOUDs to study public perceptions [25]. However, prior to such recent efforts, research on analyzing public perceptions and discussions surrounding methadone and Suboxone V R focused primarily on conducting interviews with patients at inpatient treatment facilities. ...
Article
Full-text available
Background: According to the latest medical evidence, Methadone and buprenorphine-naloxone (Suboxone®) are effective treatments for opioid use disorder (OUD). While the evidence basis for the use of these medications is favorable, less is known about the perceptions of the general public about them. Objective: This study aimed to use Twitter to assess the public perceptions about methadone and buprenorphine-naloxone, and to compare their discussion contents based on themes/topics, subthemes, and sentiment. Methods: We conducted a descriptive analysis of a small and automatic analysis of a large volume of microposts ("tweets") that mentioned "methadone" or "suboxone". In the manual analysis, we categorized the tweets into themes and subthemes, as well as by sentiment and personal experience, and compared the information posted about these two medications. We performed automatic topic modeling and sentiment analysis over large volumes of posts and compared the outputs to those from the manual analyses. Results: We manually analyzed 900 tweets, most of which related to access (15.3% for methadone; 14.3% for buprenorphine-naloxone), stigma (17.0%; 15.5%), and OUD treatment (12.8%; 15.6%). 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 broadly, rather than comparing the two medications directly. Automatic topic modeling revealed topics from the larger dataset that corresponded closely to the manually identified themes, but sentiment analysis did not reveal any notable differences in chatter regarding the two medications. Conclusions: Twitter content about methadone and Suboxone® is 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 OUD. Perceptions of these medications may contribute to their underutilization in combatting OUDs.
... Recent research related to our work has attempted to analyze publicly available social media chatter about MOUDs to study public perceptions [25]. However, prior to such recent efforts, research on analyzing public perceptions and discussions surrounding methadone and Suboxone V R focused primarily on conducting interviews with patients at inpatient treatment facilities. ...
Preprint
Full-text available
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.
Article
Background: Digitally-mediated peer support may improve opioid use disorder (OUD) recovery. Our objective was to examine the types and sources of stigma that people seek support for in online OUD recovery communities (subreddits) on Reddit.Methods: We extracted all posts containing stigma keywords from three subreddits as well as a random sample that do not contain stigma keywords. We conducted deductive content analysis to confirm that the post self-described an experience of stigma and identify the type (condition, intervention) and source (provider-based, public, self, structural) of stigma.Results: Two-hundred and fifty-nine posts self-reported a stigmatizing experience. The majority of posts described an intervention stigma associated with medications for OUD. Posts discussing intervention stigma acknowledged the role of stigma in their treatment decision-making and quality of their treatment program. The most frequent sources of stigma were the public (including family members), provider-based (healthcare and pharmacy workers), structural (workplace, law enforcement, child protective services, and abstinence-based self-help groups), and self. No posts mentioned courtesy stigma. Posts sought assistance in navigating their experiences and participating in advocacy to counter stigmatized narratives.Conclusions: Our study indicates that people in online communities seek support to disclose and manage experiences of stigma on Reddit in similar ways to people in offline communities with the noted exception of an absence of discussions of courtesy stigma. Since each subreddit is a microcosm of varying needs, we suggest areas of future work for collaborative resources developed between stakeholders of these subreddits and public health that work within the preexisting Reddit social norms.
Article
The United States opioid epidemic is fueled by illicit opioid abuse and prescription opioid misuse and abuse. Consequently, cases of opioid use disorder (OUD, opioid addiction), opioid overdose, and related deaths have increased since the year 2000. Naloxone is an opioid antagonist that rapidly reverses opioid intoxication to prevent death from overdose. It is one of the major risk mitigation strategies recommended in the 2016 Centers for Disease Control and Prevention Guideline for Prescribing Opioids for Chronic Pain. However, despite the exponential increase in dispensing and distribution of naloxone, opioid overdose and related deaths have continued to increase; suggesting that the increased naloxone supply still lags the need. This discordance is attributed at least in part to the negative attitude toward naloxone, which is based on the belief that naloxone is only meant for “addicts” and “abusers” (OUD patients). This negative attitude or so-called naloxone stigma is therefore considered a major barrier for naloxone distribution and consequently, overdose-death prevention efforts. This article presents evidence that challenges common assertions about OUD stigma being the sole and direct driving force behind naloxone stigma, and the purported magnitude of the barrier that naloxone stigma constitutes for naloxone distribution programs among the stakeholders (patients, pharmacists, and prescribers). The case was then made to operationalize and quantify the construct among the stakeholders to determine the extent to which OUD stigma drives naloxone stigma, and the relative impact of naloxone stigma as a barrier for naloxone distribution efforts.
Article
Introduction The relationship between cannabis, tobacco, and vaping devices is both rapidly changing and poorly understood, with consumers rapidly shifting between use of all three product types. Given this dynamic and evolving landscape, there is an urgent need to monitor and better understand co-use, dual-use, and transition patterns between these products. This study describes work that utilizes social media — in this case, Reddit — in conjunction with automated Natural Language Processing (NLP) methods to better understand cannabis, tobacco, and vaping device product usage patterns. Methods We collected Reddit data from the period 2013-2018 sourced from eight popular, high-volume Reddit communities (subreddits) related to the three product categories. We then manually annotated (coded) a set of 2,640 Reddit posts and trained a machine learning-based NLP algorithm to automatically identify and disambiguate between cannabis or tobacco mentions (both smoking and vaping) in Reddit posts. This classifier was then applied to all data derived from the eight subreddits, 767,788 posts in total. Results The NLP algorithm achieved an overall moderate performance (overall F-score of 0.77). When applied to our large corpus of Reddit posts, we discovered that over 10% of posts in the smoking cessation subreddit r/stopsmoking were classified as referring to vaping nicotine, and that only 2% of posts from the subreddits r/electronic_cigarette and r/vaping were classified as referring to smoking (tobacco) cessation. Conclusions This study presents the results of applying an NLP algorithm designed to identify and distinguish between cannabis and tobacco mentions (both smoking and vaping) in Reddit posts, hence contributing to our currently limited understanding of co-use, dual-use, and transition patterns between these products.
Article
Background The coronavirus disease (COVID-19) pandemic has impacted patients receiving methadone maintenance treatment (MMT) through opioid treatment programs (OTPs), especially because of the unique challenges of the care delivery model. Previously, documentation of patient experiences during emergencies often comes years after the fact, in part because there is a substantial data void in real-time. Methods: We extracted 308 posts that mention COVID-19 keywords on r/methadone, an online community for patients receiving MMT to share information, on Reddit occurring between January 31, 2020 and September 30, 2020. 215 of these posts self-report an impact to their MMT. Using qualitative content analysis, we characterized the impacts described in these posts and identified four emergent themes describing patients’ experience of impacts to MMT during COVID-19. Results: The themes included (1) 54.4% of posts reporting impediments to accessing their methadone, (2) 28.4% reporting impediments to accessing physicial OTPs, (3) 19.5% reporting having to self-manage their care, and (4) 4.7% reporting impediments to accessing OTP providers and staff. Conclusions: Patients described unanticipated consequences to one-size-fits-all policies that are unevenly applied resulting in suboptimal dosing, increased perceived risk of acquiring COVID-19 at OTPs, and reduced interaction with OTP providers and staff. While preliminary, these results are formative for follow-up surveillance metrics for patients of OTPs as well as digitally-mediated resource needs for this online community. This study serves as a model of how social media can be employed during and after emergencies to hear the lived experiences of patients for informed emergency preparedness and response.
Article
Full-text available
Background Online health forums have become increasingly popular over the past several years. They provide members with a platform to network with peers and share information, experiential advice, and support. Among the members of health forums, we define “peer experts” as a set of lay users who have gained expertise on the particular health topic through personal experience, and who demonstrate credibility in responding to questions from other members. This paper aims to motivate the need to identify peer experts in health forums and study their characteristics. Methods We analyze profiles and activity of members of a popular online health forum and characterize the interaction behavior of peer experts. We study the temporal patterns of comments posted by lay users and peer experts to uncover how peer expertise is developed. We further train a supervised classifier to identify peer experts based on their activity level, textual features, and temporal progression of posts. Result A support vector machine classifier with radial basis function kernel was found to be the most suitable model among those studied. Features capturing the key semantic word classes and higher mean user activity were found to be most significant features. Conclusion We define a new class of members of health forums called peer experts, and present preliminary, yet promising, approaches to distinguish peer experts from novice users. Identifying such peer expertise could potentially help improve the perceived reliability and trustworthiness of information in community health forums.
Article
Full-text available
Background: Extended-release naltrexone (XR-NTX), an opioid antagonist, and sublingual buprenorphine-naloxone (BUP-NX), a partial opioid agonist, are pharmacologically and conceptually distinct interventions to prevent opioid relapse. We aimed to estimate the difference in opioid relapse-free survival between XR-NTX and BUP-NX. Methods: We initiated this 24 week, open-label, randomised controlled, comparative effectiveness trial at eight US community-based inpatient services and followed up participants as outpatients. Participants were 18 years or older, had Diagnostic and Statistical Manual of Mental Disorders-5 opioid use disorder, and had used non-prescribed opioids in the past 30 days. We stratified participants by treatment site and opioid use severity and used a web-based permuted block design with random equally weighted block sizes of four and six for randomisation (1:1) to receive XR-NTX or BUP-NX. XR-NTX was monthly intramuscular injections (Vivitrol; Alkermes) and BUP-NX was daily self-administered buprenorphine-naloxone sublingual film (Suboxone; Indivior). The primary outcome was opioid relapse-free survival during 24 weeks of outpatient treatment. Relapse was 4 consecutive weeks of any non-study opioid use by urine toxicology or self-report, or 7 consecutive days of self-reported use. This trial is registered with ClinicalTrials.gov, NCT02032433. Findings: Between Jan 30, 2014, and May 25, 2016, we randomly assigned 570 participants to receive XR-NTX (n=283) or BUP-NX (n=287). The last follow-up visit was Jan 31, 2017. As expected, XR-NTX had a substantial induction hurdle: fewer participants successfully initiated XR-NTX (204 [72%] of 283) than BUP-NX (270 [94%] of 287; p<0·0001). Among all participants who were randomly assigned (intention-to-treat population, n=570) 24 week relapse events were greater for XR-NTX (185 [65%] of 283) than for BUP-NX (163 [57%] of 287; hazard ratio [HR] 1·36, 95% CI 1·10-1·68), most or all of this difference accounted for by early relapse in nearly all (70 [89%] of 79) XR-NTX induction failures. Among participants successfully inducted (per-protocol population, n=474), 24 week relapse events were similar across study groups (p=0·44). Opioid-negative urine samples (p<0·0001) and opioid-abstinent days (p<0·0001) favoured BUP-NX compared with XR-NTX among the intention-to-treat population, but were similar across study groups among the per-protocol population. Self-reported opioid craving was initially less with XR-NTX than with BUP-NX (p=0·0012), then converged by week 24 (p=0·20). With the exception of mild-to-moderate XR-NTX injection site reactions, treatment-emergent adverse events including overdose did not differ between treatment groups. Five fatal overdoses occurred (two in the XR-NTX group and three in the BUP-NX group). Interpretation: In this population it is more difficult to initiate patients to XR-NTX than BUP-NX, and this negatively affected overall relapse. However, once initiated, both medications were equally safe and effective. Future work should focus on facilitating induction to XR-NTX and on improving treatment retention for both medications. Funding: NIDA Clinical Trials Network.
Article
Full-text available
Introduction Prescription medication overdose is the fastest growing drug-related problem in the USA. The growing nature of this problem necessitates the implementation of improved monitoring strategies for investigating the prevalence and patterns of abuse of specific medications. Objectives Our primary aims were to assess the possibility of utilizing social media as a resource for automatic monitoring of prescription medication abuse and to devise an automatic classification technique that can identify potentially abuse-indicating user posts. Methods We collected Twitter user posts (tweets) associated with three commonly abused medications (Adderall®, oxycodone, and quetiapine). We manually annotated 6400 tweets mentioning these three medications and a control medication (metformin) that is not the subject of abuse due to its mechanism of action. We performed quantitative and qualitative analyses of the annotated data to determine whether posts on Twitter contain signals of prescription medication abuse. Finally, we designed an automatic supervised classification technique to distinguish posts containing signals of medication abuse from those that do not and assessed the utility of Twitter in investigating patterns of abuse over time. Results Our analyses show that clear signals of medication abuse can be drawn from Twitter posts and the percentage of tweets containing abuse signals are significantly higher for the three case medications (Adderall®: 23 %, quetiapine: 5.0 %, oxycodone: 12 %) than the proportion for the control medication (metformin: 0.3 %). Our automatic classification approach achieves 82 % accuracy overall (medication abuse class recall: 0.51, precision: 0.41, F measure: 0.46). To illustrate the utility of automatic classification, we show how the classification data can be used to analyze abuse patterns over time. Conclusion Our study indicates that social media can be a crucial resource for obtaining abuse-related information for medications, and that automatic approaches involving supervised classification and natural language processing hold promises for essential future monitoring and intervention tasks.
Article
Introduction and Aims This article examines the feasibility of leveraging Twitter to detect posts authored by people who use opioids (PWUO) or content related to opioid use disorder (OUD), and manually develop a multidimensional taxonomy of relevant tweets. Design and Methods Twitter messages were collected between June and October 2017 (n = 23 827) and evaluated using an inductive coding approach. Content was then manually classified into two axes (n = 17 420): (i) user experience regarding accessing, using, or recovery from illicit opioids; and (ii) content categories (e.g. policies, medical information, jokes/sarcasm). Results The most prevalent categories consisted of jokes or sarcastic comments pertaining to OUD, PWUOs or hypothetically using illicit opioids (63%), informational content about treatments for OUD, overdose prevention or accessing self‐help groups (20%), and commentary about government opioid policy or news related to opioids (17%). Posts by PWUOs centered on identifying illicit sources for procuring opioids (i.e. online, drug dealers; 49%), symptoms and/or strategies to quell opioid withdrawal symptoms (21%), and combining illicit opioid use with other substances, such as cocaine or benzodiazepines (17%). State and public health experts infrequently posted content pertaining to OUD (1%). Discussion and Conclusions Twitter offers a feasible approach to identify PWUO. Further research is needed to evaluate the efficacy of Twitter to disseminate evidence‐based content and facilitate linkage to treatment and harm reduction services.
Article
Opioid use disorder is characterised by the persistent use of opioids despite the adverse consequences of its use. The disorder is associated with a range of mental and general medical comorbid disorders, and with increased mortality. Although genetics are important in opioid use disorder, younger age, male sex, and lower educational attainment level and income, increase the risk of opioid use disorder, as do certain psychiatric disorders (eg, other substance use disorders and mood disorders). The medications for opioid use disorder, which include methadone, buprenorphine, and extended-release naltrexone, significantly improve opioid use disorder outcomes. However, the effectiveness of medications for opioid use disorder is limited by problems at all levels of the care cascade, including diagnosis, entry into treatment, and retention in treatment. There is an urgent need for expanding the use of medications for opioid use disorder, including training of health-care professionals in the treatment and prevention of opioid use disorder, and for development of alternative medications and new models of care to expand capabilities for personalised interventions.
Article
Objectives: To deploy a methodology accurately identifying tweets marketing the illegal online sale of controlled substances. Methods: We first collected tweets from the Twitter public application program interface stream filtered for prescription opioid keywords. We then used unsupervised machine learning (specifically, topic modeling) to identify topics associated with illegal online marketing and sales. Finally, we conducted Web forensic analyses to characterize different types of online vendors. We analyzed 619 937 tweets containing the keywords codeine, Percocet, fentanyl, Vicodin, Oxycontin, oxycodone, and hydrocodone over a 5-month period from June to November 2015. Results: A total of 1778 tweets (< 1%) were identified as marketing the sale of controlled substances online; 90% had imbedded hyperlinks, but only 46 were "live" at the time of the evaluation. Seven distinct URLs linked to Web sites marketing or illegally selling controlled substances online. Conclusions: Our methodology can identify illegal online sale of prescription opioids from large volumes of tweets. Our results indicate that controlled substances are trafficked online via different strategies and vendors. Public Health Implications. Our methodology can be used to identify illegal online sellers in criminal violation of the Ryan Haight Online Pharmacy Consumer Protection Act. (Am J Public Health. Published online ahead of print October 19, 2017: e1-e6. doi:10.2105/AJPH.2017.303994).
Article
Background The misuse of prescription opioids (MUPO) is a leading public health concern. Social media are playing an expanded role in public health research, but there are few methods for estimating established epidemiological metrics from social media. The purpose of this study was to demonstrate that the geographic variation of social media posts mentioning prescription opioid misuse strongly correlates with government estimates of MUPO in the last month. Methods We wrote software to acquire publicly available tweets from Twitter from 2012 to 2014 that contained at least one keyword related to prescription opioid use (n = 3,611,528). A medical toxicologist and emergency physician curated the list of keywords. We used the semantic distance (SemD) to automatically quantify the similarity of meaning between tweets and identify tweets that mentioned MUPO. We defined the SemD between two words as the shortest distance between the two corresponding word-centroids. Each word-centroid represented all recognized meanings of a word. We validated this automatic identification with manual curation. We used Twitter metadata to estimate the location of each tweet. We compared our estimated geographic distribution with the 2013–2015 National Surveys on Drug Usage and Health (NSDUH). ResultsTweets that mentioned MUPO formed a distinct cluster far away from semantically unrelated tweets. The state-by-state correlation between Twitter and NSDUH was highly significant across all NSDUH survey years. The correlation was strongest between Twitter and NSDUH data from those aged 18–25 (r = 0.94, p < 0.01 for 2012; r = 0.94, p < 0.01 for 2013; r = 0.71, p = 0.02 for 2014). The correlation was driven by discussions of opioid use, even after controlling for geographic variation in Twitter usage. Conclusions Mentions of MUPO on Twitter correlate strongly with state-by-state NSDUH estimates of MUPO. We have also demonstrated that a natural language processing can be used to analyze social media to provide insights for syndromic toxicosurveillance.
Article
Background: Researchers have used traditional databases to study public health for decades. Less is known about the use of social media data sources, such as Twitter, for this purpose. Objectives: To systematically review the use of Twitter in health research, define a taxonomy to describe Twitter use, and characterize the current state of Twitter in health research. Search methods: We performed a literature search in PubMed, Embase, Web of Science, Google Scholar, and CINAHL through September 2015. Selection criteria: We searched for peer-reviewed original research studies that primarily used Twitter for health research. Data collection and analysis: Two authors independently screened studies and abstracted data related to the approach to analysis of Twitter data, methodology used to study Twitter, and current state of Twitter research by evaluating time of publication, research topic, discussion of ethical concerns, and study funding source. Main results: Of 1110 unique health-related articles mentioning Twitter, 137 met eligibility criteria. The primary approaches for using Twitter in health research that constitute a new taxonomy were content analysis (56%; n = 77), surveillance (26%; n = 36), engagement (14%; n = 19), recruitment (7%; n = 9), intervention (7%; n = 9), and network analysis (4%; n = 5). These studies collectively analyzed more than 5 billion tweets primarily by using the Twitter application program interface. Of 38 potential data features describing tweets and Twitter users, 23 were reported in fewer than 4% of the articles. The Twitter-based studies in this review focused on a small subset of data elements including content analysis, geotags, and language. Most studies were published recently (33% in 2015). Public health (23%; n = 31) and infectious disease (20%; n = 28) were the research fields most commonly represented in the included studies. Approximately one third of the studies mentioned ethical board approval in their articles. Primary funding sources included federal (63%), university (13%), and foundation (6%). Conclusions: We identified a new taxonomy to describe Twitter use in health research with 6 categories. Many data elements discernible from a user's Twitter profile, especially demographics, have been underreported in the literature and can provide new opportunities to characterize the users whose data are analyzed in these studies. Twitter-based health research is a growing field funded by a diversity of organizations. Public health implications. Future work should develop standardized reporting guidelines for health researchers who use Twitter and policies that address privacy and ethical concerns in social media research. (Am J Public Health. Published online ahead of print November 17, 2016: e1-e8. doi:10.2105/AJPH.2016.303512).