LexExp: a system for automatically expanding concept lexicons for noisy biomedical texts

Al-Garadi

et al. 2022

Preprint

Self Cite

BackgroundDespite recent increasing focus on fatal overdoses involving multiple substances, there is a paucity of knowledge about stimulant co-use patterns among people who use opioids (PWUO) or people being treated with medication for opioid use disorder (PTMOUD). This study examines stimulant co-mention trends among PWUO/PTMOUD on social media.MethodsWe collected publicly-available data from 14 prescription and illicit opioid and MOUD-related forums on Reddit (subreddits) between 2011-2020 and timelines comprising past posts from a sample of Reddit users (Redditors) on these forums. We applied natural language processing to detect mentions of opioids, opioid-related medications, and stimulants and described trends and patterns in co-mentions. ResultsPosts collected for 13,812 Redditors indicated 12,306 (89.1%) mentioned ≥1 opioid, opioid-related medication or stimulant. Analyses showed the number and proportion of Redditors mentioning both opioids and/or opioid-related medications and stimulants steadily increased over time. Relative rates of co-mentions of heroin and methamphetamine, substances most commonly co-mentioned, decreased in recent years while those of fentanyl and MOUD with methamphetamine increased. ConclusionData from Reddit reflect increasing mentions of stimulants, particularly methamphetamine, among PWUO/PTMOUD and closely resemble the growth in overdose deaths involving both opioids and stimulants. These findings are consistent with recent reports suggesting increasing stimulant use among people receiving treatment for opioid use disorder. These data offer insights on emerging trends in the overdose epidemic and underscore the importance of scaling efforts to address co-occurring opioid and stimulant use including harm reduction and comprehensive healthcare access spanning mental-health services and substance use disorder treatment.

Section: Identifying Substance Mentionsmentioning

confidence: 81%

Trends in Co-mention of Stimulants and Opioids: A Natural Language Processing Driven Analysis of Reddit Forums

Al-Garadi

et al. 2022

Preprint

Self Cite

“…The first application of NLP was to generate lexical variants (e.g., misspellings) of the substances. Since drug names and related expressions are often misspelled on social media, we generated commonly used lexical variants of the terms using the LexExp tool [ 27 ]. We found that some non-standard terms and lexical variants tend to have high noise associated with them (i.e., expressions not actually referring to a stimulant or opioid, e.g., oxy clean) .…”

Section: Methodsmentioning

confidence: 99%

Signals of increasing co-use of stimulants and opioids from online drug forum data

Al-Garadi

et al. 2022

Harm Reduct J

Self Cite

Background Despite recent rises in fatal overdoses involving multiple substances, there is a paucity of knowledge about stimulant co-use patterns among people who use opioids (PWUO) or people being treated with medications for opioid use disorder (PTMOUD). A better understanding of the timing and patterns in stimulant co-use among PWUO based on mentions of these substances on social media can help inform prevention programs, policy, and future research directions. This study examines stimulant co-mention trends among PWUO/PTMOUD on social media over multiple years. Methods We collected publicly available data from 14 forums on Reddit (subreddits) that focused on prescription and illicit opioids, and medications for opioid use disorder (MOUD). Collected data ranged from 2011 to 2020, and we also collected timelines comprising past posts from a sample of Reddit users (Redditors) on these forums. We applied natural language processing to generate lexical variants of all included prescription and illicit opioids and stimulants and detect mentions of them on the chosen subreddits. Finally, we analyzed and described trends and patterns in co-mentions. Results Posts collected for 13,812 Redditors showed that 12,306 (89.1%) mentioned at least 1 opioid, opioid-related medication, or stimulant. Analyses revealed that the number and proportion of Redditors mentioning both opioids and/or opioid-related medications and stimulants steadily increased over time. Relative rates of co-mentions by the same Redditor of heroin and methamphetamine, the substances most commonly co-mentioned, decreased in recent years, while co-mentions of both fentanyl and MOUD with methamphetamine increased. Conclusion Our analyses reflect increasing mentions of stimulants, particularly methamphetamine, among PWUO/PTMOUD, which closely resembles the growth in overdose deaths involving both opioids and stimulants. These findings are consistent with recent reports suggesting increasing stimulant use among people receiving treatment for opioid use disorder. These data offer insights on emerging trends in the overdose epidemic and underscore the importance of scaling efforts to address co-occurring opioid and stimulant use including harm reduction and comprehensive healthcare access spanning mental-health services and substance use disorder treatment.

“…Key phrases included in this study along with their types and the date of the first letter mentioning each. Since product and entity names are often misspelled by social media subscribers, we generated potential spelling variants or misspellings of the products and entities using a datacentric tool [9]. The variant generation tool uses a combination of semantic and lexical similarity measures to automatically identify common misspellings and spelling variants of terms/phrases, including multi-word expressions.…”

Section: Product Detectionmentioning

confidence: 99%

Early detection of fraudulent COVID-19 products from Twitter chatter

Lakamana

Liao

et al. 2022

Preprint

Self Cite

Social media have served as lucrative platforms for misinformation and for promoting fraudulent products for the treatment, testing and prevention of COVID-19. This has resulted in the issuance of many warning letters by the United States Food and Drug Administration (FDA). While social media continue to serve as the primary platform for the promotion of such fraudulent products, they also present the opportunity to identify these products early by employing effective social media mining methods. In this study, we employ natural language processing and time series anomaly detection methods for automatically detecting fraudulent COVID-19 products early from Twitter. Our approach is based on the intuition that increases in the popularity of fraudulent products lead to corresponding anomalous increases in the volume of chatter regarding them. We utilized an anomaly detection method on streaming COVID-19-related Twitter data to detect potentially anomalous increases in mentions of fraudulent products. Our unsupervised approach detected 34/44 (77.3%) signals about fraudulent products earlier than the FDA letter issuance dates, and an additional 6/44 (13.6%) within a week following the corresponding FDA letters. Our proposed method is simple, effective and easy to deploy, and do not require high performance computing machinery unlike deep neural network-based methods.