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AbstractCannabis-related tweets were collected between January and April 2022 to estimate the availability and characteristics of cannabis products advertised on Twitter amid the legalization of recreational cannabis in Thailand. The Twitter API was called using the tweepy Python library to collect cannabis-related tweets in the Thai language. A total of 185,558 unique tweets were collected over the duration of the data collection period based on 83 search terms. Twenty thousand random tweets were manually coded by four Thai native speakers to assess the volume and characteristics of tweets proposing cannabis. 72.6% of collected tweets from the 20,000 random samples were coded as relevant to the study. 54.6% of relevant tweets were advertising cannabis products, 29.8% were personal communications, and 15.6% were related to news or media content. Among the tweets that advertised cannabis products, 94.4% proposed cannabis flower, 2.4% cannabis edibles and 1.8% cannabis concentrates. Consumption of potent forms of cannabis such as cannabis edibles and concentrates increase the risk of harmful side-effects, especially in a population with limited knowledge about these products. Our findings call for additional monitoring efforts and for increasing the public awareness on potent cannabis products emerging in Thailand. [ABSTRACT FROM AUTHOR]

From 2018 to 2021, seizures of counterfeit oxycodone pills containing non-pharmaceutical fentanyl or other novel synthetic opioids increased significantly contributing to continuing increases in overdose mortality in Northern America. Evidence suggests that counterfeit pills are distributed through cryptomarkets. This article presents data regarding the availability and characteristics of oxycodone pills advertised on one major cryptomarket between January and March 2022. Collected data were processed using a dedicated Named Entity Recognition algorithm to identify oxycodone listings and categorized them as either counterfeit or pharmaceutical. Frequency of listings, average number of pills advertised, average prices per milligram, number of sales, and geographic indicators of shipment origin and destination were analyzed. In total, 2,665 listings were identified as oxycodone. 48.2% (1,285/2,665) of these listings were categorized as counterfeit oxycodone, advertising a total of 652,699 pills (93,242.7 pills per datapoint) offered at a lower price than pharmaceutical pills. Our data indicate the presence of a large volume of counterfeit oxycodone pills both in retail- and wholesale-level amounts mostly targeting US and Canadian customers. These exploratory findings call for more research to develop epidemiological surveillance systems to track counterfeit pill and other drug availability on the Dark web environment. [ABSTRACT FROM AUTHOR]

Domestic secondhand smoke endangers the health of children. Nevertheless, this behavior persists in Indonesia, where up to 72% of men smoke in homes despite national intervention efforts to reduce such behavior. This qualitative exploratory research focused on why young Indonesian fathers continue to smoke cigarettes at home despite knowing about the dangers of secondhand smoke by investigating the influence of masculinity on their choices. Thirty young fathers, all active smokers, were recruited for semi-structured in-depth qualitative interviews. The process of coding data from grounded theory was used to interpret collected data, although grounded theory itself was not the theoretical framework used by this research. Findings indicate that smoking is essential to Indonesia's socially constructed image of masculinity. Toxic masculinity was observed throughout the participants' smoking trajectory, expressing itself through the stigmatization of non-smokers, display of financial success through expensive cigarettes, and health risk-taking. Low trust in official health messages and the use of neutralization techniques to justify their smoking behavior drive young fathers to continue smoking. Participants appear to have adopted a variation of toxic masculinity by creating strategies to conciliate the role of a caring father and the part of a man who smokes cigarettes. By being able to legitimize their smoking continuation, participants unintentionally maintain the cycle of male smoking in which children witness older men smoking and link tobacco smoking with masculinity. [ABSTRACT FROM AUTHOR]

Objective: Twitter data offer new possibilities for tracking health-related communications. This study is among the first to apply advanced information processing to identify geographic and content features of cannabis-related tweeting in the United States.Method: Tweets were collected using streaming Application Programming Interface (March-May 2016) and were processed by eDrugTrends to identify geolocation and classify content by source (personal communication, media, retail) and sentiment (positive, negative, neutral). States were grouped by cannabis legalization policies into "recreational," "medical, less restrictive," "medical, more restrictive," and "illegal." Permutation tests were performed to analyze differences among four groups in adjusted percentages of all tweets, unique users, personal communications only, and positive-to-negative sentiment ratios.Results: About 30% of all 13,233,837 cannabis-related tweets had identifiable state-level geo-information. Among geolocated tweets, 76.2% were personal communications, 21.1% media, and 2.7% retail. About 71% of personal communication tweets expressed positive sentiment toward cannabis; 16% expressed negative sentiment. States in the recreational group had significantly greater average adjusted percentage of cannabis tweets (3.01%) compared with other groups. For personal communication tweets only, the recreational group (2.47%) was significantly greater than the medical, more restrictive (1.84%) and illegal (1.85%) groups. Similarly, the recreational group had significantly greater average positive-to-negative sentiment ratio (4.64) compared with the medical, more restrictive (4.15) and illegal (4.19) groups. Average adjusted percentages of unique users showed similar differences between recreational and other groups.Conclusions: States with less restrictive policies displayed greater cannabis-related tweeting and conveyed more positive sentiment. The study demonstrates the potential of Twitter data to become a valuable indicator of drug-related communications in the context of varying policy environments. [ABSTRACT FROM AUTHOR]

Background: To 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., Objectives: The 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., Methods: Tweets 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., Results: In 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, compared with Approach 1, for LR (0.8752 vs 0.8516, P=.04) and SVM (0.8800 vs 0.8557, P=.045)., Conclusions: The study provides an example of the use of supervised machine learning methods to categorize cannabis- and synthetic cannabinoid-related tweets with fairly high accuracy. Use of these content analysis tools along with geographic identification capabilities developed by the eDrugTrends platform will provide powerful methods for tracking regional changes in user opinions related to cannabis and synthetic cannabinoids use over time and across different regions., Competing Interests: Conflicts of Interest: None declared.