Opioid Discussion in the Twittersphere

Graves, Rachel; Tufts, Christopher; Meisel, Zachary F.; Polsky, Dan; Ungar, Lyle H.; Merchant, Raina M.

doi:10.1080/10826084.2018.1458319

Cited by 31 publications

(37 citation statements)

References 23 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, results from the Granger tests showed that heroin tweets in a given year predicted subsequent heroin deaths better than lagged heroin OODs alone. These predictive results extend recent reports of correlations between opioid-related tweets and opioid overdose rates at the state and county levels [ 28 , 29 ].…”

Section: Discussionsupporting

confidence: 87%

“…Researchers have used other data streams, including Google Trends to forecast premature death from alcohol, drugs, and suicides [ 24 ]; a cryptomarket forum on the Dark Web to assess the emergence of new psychoactive substances [ 25 ]; and WebMD to explore motivations to use buprenorphine [ 26 , 27 ]. Recently, Graves et al [ 28 ] reported that thematic patterns of opioid-related tweets correlated with opioid overdose rates at the state and county levels. Sarker et al [ 29 ] reported that opioid-related tweets in Pennsylvania correlated with county-level OODs over 3 years.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Twitter to Surveil the Opioid Epidemic in North Carolina: An Exploratory Study

Anwar¹,

Khoury²,

Aldridge³

et al. 2020

JMIR Public Health Surveill

View full text Add to dashboard Cite

Background Over the last two decades, deaths associated with opioids have escalated in number and geographic spread, impacting more and more individuals, families, and communities. Reflecting on the shifting nature of the opioid overdose crisis, Dasgupta, Beletsky, and Ciccarone offer a triphasic framework to explain that opioid overdose deaths (OODs) shifted from prescription opioids for pain (beginning in 2000), to heroin (2010 to 2015), and then to synthetic opioids (beginning in 2013). Given the rapidly shifting nature of OODs, timelier surveillance data are critical to inform strategies that combat the opioid crisis. Using easily accessible and near real-time social media data to improve public health surveillance efforts related to the opioid crisis is a promising area of research. Objective This study explored the potential of using Twitter data to monitor the opioid epidemic. Specifically, this study investigated the extent to which the content of opioid-related tweets corresponds with the triphasic nature of the opioid crisis and correlates with OODs in North Carolina between 2009 and 2017. Methods Opioid-related Twitter posts were obtained using Crimson Hexagon, and were classified as relating to prescription opioids, heroin, and synthetic opioids using natural language processing. This process resulted in a corpus of 100,777 posts consisting of tweets, retweets, mentions, and replies. Using a random sample of 10,000 posts from the corpus, we identified opioid-related terms by analyzing word frequency for each year. OODs were obtained from the Multiple Cause of Death database from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER). Least squares regression and Granger tests compared patterns of opioid-related posts with OODs. Results The pattern of tweets related to prescription opioids, heroin, and synthetic opioids resembled the triphasic nature of OODs. For prescription opioids, tweet counts and OODs were statistically unrelated. Tweets mentioning heroin and synthetic opioids were significantly associated with heroin OODs and synthetic OODs in the same year (P=.01 and P<.001, respectively), as well as in the following year (P=.03 and P=.01, respectively). Moreover, heroin tweets in a given year predicted heroin deaths better than lagged heroin OODs alone (P=.03). Conclusions Findings support using Twitter data as a timely indicator of opioid overdose mortality, especially for heroin.

show abstract

Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Using Twitter to Surveil the Opioid Epidemic in North Carolina: An Exploratory Study

Anwar¹,

Khoury²,

Aldridge³

et al. 2020

JMIR Public Health Surveill

View full text Add to dashboard Cite

show abstract

“…Our goal was to study the distributions of abuse- and information-related social media chatter over time and geolocations, as past research has suggested that such analyses may reveal interesting trends. 5,21,37…”

Section: Methodsmentioning

confidence: 99%

Machine Learning and Natural Language Processing for Geolocation-Centric Monitoring and Characterization of Opioid-Related Social Media Chatter

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Graves at al. (29) and Chary et al (30) used unsupervised machine learning methods (i.e., without manually annotated training data) to derive meanings from large, unlabeled sets of tweets mentioning opioids and found correlations between numbers of tweets and state-level information from the National Surveys on Drug Usage and Health (NSDUH) and county-level opioid death rates. In our past work on the topic, we showed that supervised machine learning is capable of automatically characterizing abuse-related discussion on Twitter and that the temporal patterns of abuse detected from social media convey information identical to that obtained from prior manual analyses (23).…”

Section: Discussionmentioning

confidence: 99%

Effective buprenorphine use and tapering strategies: Endorsements and insights by people in recovery from opioid use disorder on a Reddit forum

Graves

Sarker

Al-Garadi

et al. 2019

Preprint

View full text Add to dashboard Cite

Opioid use disorder (OUD) is a national crisis in the United States and buprenorphine is one of the most effective evidence-based treatments. However, few studies have explored successful strategies for using and tapering buprenorphine to achieve stable recovery, particularly due to the difficulties of conducting long-term studies involving patients with OUD. In this study, we show that discussions on the anonymous social network Reddit may be leveraged, via automatic text mining methods, to discover successful buprenorphine use and tapering strategies. We discovered that longer tapering schedules, compared to those investigated in past clinical trials, may lead to (self-reported) sustained recovery. Furthermore, Reddit posts also provide key information regarding buprenorphine withdrawal, cravings, adjunct medications for withdrawal symptoms and relapse prevention strategies. AbstractOpioid use disorder (OUD) is a public health emergency in the United States. Over 47,000 overdose-related deaths in 2017 involved opioids. Medication-assisted treatment (MAT), in particular, buprenorphine and buprenorphine combination products such as Suboxone®, is the most effective, evidence-based treatment for OUD. However, there are a limited number of conclusive scientific studies that provide guidance to medical professionals about strategies for using buprenorphine to achieve stable recovery. In this study, we used data-driven natural language processing methods to mine a total of 16,146 posts about buprenorphine from 1933 unique users on the anonymous social network Reddit. Analysis of a sample of these posts showed that 74% of the posts described users' personal experiences and that the top three topics included advice on using Suboxone® (55.0%), Suboxone® dosage information (35.5%) and information about Suboxone® tapering (32.0%). Based on two models, one that incorporated 'upvoting' by other members and one that did not, we found that Reddit users reported more successful recovery with longer tapering schedules, particularly from 2.0 mg to 0.0 mg (median: 93 days; mean: 95 days), as compared to shorter tapering schedules investigated in past clinical trials. Diarrhea, insomnia, restlessness, and fatigue were commonly reported adverse events. Physical exercise, clonidine, and Imodium® were frequently reported to help during the recovery process. Due to the difficulties of conducting longer-term clinical trials involving patients with OUD, clinicians should consider other information sources including peer discussions from the abundant, real-time information available on Reddit.

show abstract

Opioid Discussion in the Twittersphere

Cited by 31 publications

References 23 publications

Using Twitter to Surveil the Opioid Epidemic in North Carolina: An Exploratory Study

Using Twitter to Surveil the Opioid Epidemic in North Carolina: An Exploratory Study

Machine Learning and Natural Language Processing for Geolocation-Centric Monitoring and Characterization of Opioid-Related Social Media Chatter

Effective buprenorphine use and tapering strategies: Endorsements and insights by people in recovery from opioid use disorder on a Reddit forum

Contact Info

Product

Resources

About