“Sub is a weird drug:” A web‐based study of lay attitudes about use of buprenorphine to self‐treat opioid withdrawal symptoms

Daniulaityte, Raminta; Carlson, Robert G.; Brigham, Gregory S.; Cameron, Delroy; Sheth, Amit P.

doi:10.1111/ajad.12213

Cited by 28 publications

(28 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Development of eDrugTrends platform is based on previous research and infrastructure created by our research team, including Twitris (for analysis of Twitter data) [33-36] and PREDOSE (for analysis of Web forum data) [37-39]. …”

Section: Introductionmentioning

confidence: 99%

“When ‘Bad’ is ‘Good’”: Identifying Personal Communication and Sentiment in Drug-Related Tweets

Daniulaityte¹,

Chen²,

Lamy³

et al. 2016

JMIR Public Health Surveill

Self Cite

View full text Add to dashboard Cite

BackgroundTo harness the full potential of social media for epidemiological surveillance of drug abuse trends, the field needs a greater level of automation in processing and analyzing social media content.ObjectivesThe objective of the study is to describe the development of supervised machine-learning techniques for the eDrugTrends platform to automatically classify tweets by type/source of communication (personal, official/media, retail) and sentiment (positive, negative, neutral) expressed in cannabis- and synthetic cannabinoid–related tweets.MethodsTweets were collected using Twitter streaming Application Programming Interface and filtered through the eDrugTrends platform using keywords related to cannabis, marijuana edibles, marijuana concentrates, and synthetic cannabinoids. After creating coding rules and assessing intercoder reliability, a manually labeled data set (N=4000) was developed by coding several batches of randomly selected subsets of tweets extracted from the pool of 15,623,869 collected by eDrugTrends (May-November 2015). Out of 4000 tweets, 25% (1000/4000) were used to build source classifiers and 75% (3000/4000) were used for sentiment classifiers. Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machines (SVM) were used to train the classifiers. Source classification (n=1000) tested Approach 1 that used short URLs, and Approach 2 where URLs were expanded and included into the bag-of-words analysis. For sentiment classification, Approach 1 used all tweets, regardless of their source/type (n=3000), while Approach 2 applied sentiment classification to personal communication tweets only (2633/3000, 88%). Multiclass and binary classification tasks were examined, and machine-learning sentiment classifier performance was compared with Valence Aware Dictionary for sEntiment Reasoning (VADER), a lexicon and rule-based method. The performance of each classifier was assessed using 5-fold cross validation that calculated average F-scores. One-tailed t test was used to determine if differences in F-scores were statistically significant.ResultsIn multiclass source classification, the use of expanded URLs did not contribute to significant improvement in classifier performance (0.7972 vs 0.8102 for SVM, P=.19). In binary classification, the identification of all source categories improved significantly when unshortened URLs were used, with personal communication tweets benefiting the most (0.8736 vs 0.8200, P<.001). In multiclass sentiment classification Approach 1, SVM (0.6723) performed similarly to NB (0.6683) and LR (0.6703). In Approach 2, SVM (0.7062) did not differ from NB (0.6980, P=.13) or LR (F=0.6931, P=.05), but it was over 40% more accurate than VADER (F=0.5030, P<.001). In multiclass task, improvements in sentiment classification (Approach 2 vs Approach 1) did not reach statistical significance (eg, SVM: 0.7062 vs 0.6723, P=.052). In binary sentiment classification (positive vs negative), Approach 2 (focus on personal communication tweets only) improved classification results, ...

show abstract

Section: Introductionmentioning

confidence: 99%

“When ‘Bad’ is ‘Good’”: Identifying Personal Communication and Sentiment in Drug-Related Tweets

Daniulaityte¹,

Chen²,

Lamy³

et al. 2016

JMIR Public Health Surveill

Self Cite

View full text Add to dashboard Cite

show abstract

“…The DAO is a domain-specific conceptual framework interconnecting sets (named “classes”) of drug-focused lexicons initially designed for the PREDOSE project which analyzed Web forum posts mentioning buprenorphine (Cameron, et al, 2013; Daniulaityte, Carlson, Brigham, Cameron, & Sheth, 2015). One of the key benefits of using an ontology-enhanced semantic approach is the ability to identify all variants of a concept in data.…”

Section: Methodsmentioning

confidence: 99%

“…The last few years have seen an increased interest in the use of large-scale internet-based data as a source of information on evolving public health problems and emerging trends (Butler, et al, 2007; Daniulaityte, Nahhas, et al, 2015; Eysenbach, 2011; Lamy, et al, 2016; Lazer, et al, 2009; P. G. Miller & Sonderlund, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, harnessing data on drug forums is a significant challenge due to the large amount of data displayed on Web forums. We used an advanced software platform, eDrugTrends (Daniulaityte, Nahhas, et al, 2015; eDrugTrends, 2015), to collect and analyze forum posts related to SCRA and their effects, and conducted a longitudinal analysis of drug-focused web-forum posts. The key aims of this study are to: 1) identify the most common SCRA-related effects discussed on Web forums; and 2) analyze how frequencies of SCRA and their effects mentioned in Web forum discussions changed from January 2008 through September 2015.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Increases in synthetic cannabinoids-related harms: Results from a longitudinal web-based content analysis

Lamy

Daniulaityte

Nahhas

et al. 2017

International Journal of Drug Policy

Self Cite

View full text Add to dashboard Cite

Background Synthetic Cannabinoid Receptor Agonists (SCRA), also known as “K2” or “Spice,” have drawn considerable attention due to their potential of abuse and harmful consequences. More research is needed to understand user experiences of SCRA-related effects. We use semiautomated information processing techniques through eDrugTrends platform to examine SCRA-related effects and their variations through a longitudinal content analysis of web-forum data. Method English language posts from three drug-focused web-forums were extracted and analyzed between January 1st 2008 and September 30th 2015. Search terms are based on the Drug Abuse Ontology (DAO) created for this study (189 SCRA-related and 501 effect-related terms). EDrugTrends NLP-based text processing tools were used to extract posts mentioning SCRA and their effects. Generalized linear regression was used to fit restricted cubic spline functions of time to test whether the proportion of drug-related posts that mention SCRA (and no other drug) and the proportion of these “SCRA-only” posts that mention SCRA effects have changed over time, with an adjustment for multiple testing. Results 19,052 SCRA-related posts (Bluelight (n=2,782), Forum A (n=3,882), and Forum B (n=12,388)) posted by 2,543 international users were extracted. The most frequently mentioned effects were “getting high” (44.0%), “hallucinations” (10.8%), and “anxiety” (10.2%). The frequency of SCRA-only posts declined steadily over the study period. The proportions of SCRA-only posts mentioning positive effects (e.g., “High” and “Euphoria”) steadily decreased, while the proportions of SCRA-only posts mentioning negative effects (e.g., “Anxiety,” “Nausea,” “Overdose”) increased over the same period. Conclusion This study's findings indicate that the proportion of negative effects mentioned in web forum posts and linked to SCRA has increased over time, suggesting that recent generations of SCRA generate more harms. This is also one of the first studies to conduct automated content analysis of web forum data related to illicit drug use.

show abstract

“…While we appreciate the short –term use of buprenorphine/naloxone, clinicians should continue to monitor a patients response since there have been a number of issues involving toxicity(DeVido, Connery, & Hill, 2015; Gangahar, 2015; Daniulaityte, Carlson, Brigham, Cameron, & Sheth, 2015; Ciftci et al, 2015; Edwards, McCormick-Deaton, & Hosanagar, 2014; Lavonas et al, 2013; Kim, Smiddy, Hoffman, & Nelson, 2012). One limited study suggests that buprenorphine does not affect the cingulate gyrus in early abstinent heroin addicts and as such may act as a true non-addicting pharmaceutical to prevent relapse (Mei, Zhang, & Xiao, 2010); clearly much more research is needed on this topic.…”

mentioning

confidence: 99%

Should the United States Government Repeal Restrictions on Buprenorphine/Naloxone Treatment?

Blum

Gold

Clark

et al. 2016

Substance Use & Misuse

View full text Add to dashboard Cite

Attention must be focused on needed changes to the current United States law that restricts physicians who prescribe buprenorphine for the detoxification or treatment of Opioid Use Disorder, to accepting no more than100 patients. The current system does not provide comprehensive treatment as defined by the American Society of Addiction Medicine (ASAM) criteria. Additionally, it suffers from both fragmentation and stigma and will require a significant change to comply with ASAM’s call for integrated delivery of comprehensive addiction treatment. This commentary calls for the development and implementation of “best practice,” by recommending caution in lifting the 100 patient limit until substantial achievement of this goal occurs. The authors call for an increase to 200 in the patient limit to be restricted to those physicians who are Board Certified in Addiction Medicine by the American Board of Addiction Medicine (ABAM) or in Addiction Psychiatry by the American Board of Psychiatry and Neurology (ABPN), or other responsible medical organizations. Any additional restriction lifting should follow a systemic evolution that rewards and documents competency. Such a system would involve the integration of treatment, treatment systems, and recovery with prescription medication. In addition, it should monitor emotional blunting, treatment progress and initiation of genetic addiction risk testing.

show abstract

“Sub is a weird drug:” A web‐based study of lay attitudes about use of buprenorphine to self‐treat opioid withdrawal symptoms

Cited by 28 publications

References 33 publications

“When ‘Bad’ is ‘Good’”: Identifying Personal Communication and Sentiment in Drug-Related Tweets

“When ‘Bad’ is ‘Good’”: Identifying Personal Communication and Sentiment in Drug-Related Tweets

Increases in synthetic cannabinoids-related harms: Results from a longitudinal web-based content analysis

Should the United States Government Repeal Restrictions on Buprenorphine/Naloxone Treatment?

Contact Info

Product

Resources

About