Faculty Bibliography

Study Objectives: Previously, we built machine-learned models to automatically identify Tweets indicating alcohol use from 34,563 labeled Tweets collected over 24 hours during New Year's Day. The models demonstrated an estimated area under the receiver operating curve (AUROC) of 0.94 for identifying alcohol use Tweets. In this study, we validated our alcohol use model in an independently collected dataset - the Electric Zoo music festival on New York City's Randall's Island. This event attracted over 130,000 people in 2013 and resulted in two substance-associated deaths. Methods: The initial dataset contained all Tweets and Instagrams geo-tagged within 5 miles of Randall's Island, covering all event days from August 29-31, 2014. Two authors independently reviewed Tweets for drug- or alcohol-related content. 10% of the Tweets were randomly selected for dual independent review to determine agreement using a weighted Cohen's kappa. Identified Tweets were then jointly reviewed to determine those indicative of alcohol use according to previous definitions. Tweets and Instagrams were considered indicators of alcohol use if they referred to: intention to drink, the act of drinking, location at a bar or liquor store, mention of a specific brand, drinking paraphernalia (eg, flask), consequences from drinking (eg, drunk, wasted, tipsy), or alcohol-related hashtags. Our Bayesian logistic regression machine learned model, which had been derived only from Tweets, was applied to a restricted dataset excluding Instagrams. Results: The complete geo-located collection included 11,071 Tweets and Instagrams. The restricted dataset containing only Tweets consisted of 2,928 elements, of which 82 Tweets were classified as drug- or alcohol-related (weighted kappa = 0.92). Of these, 23 Tweets explicitly referenced alcohol use (eg, "Wine at Zoo is the right play. Instadrunk;" "Wow. I am not sober;" "#clskipfridays #livesummer #Ezoo #were dumb #and drunk"). The model achieved an AUROC of 0.87 when applied to this independent Tweet validation set. Conclusion: Our machine-learned model automatically identified alcohol use at Electric Zoo with high discriminatory power. Differences between the previous estimated AUROC performance and the validated AUROC performance are likely due to language variations between the two groups. An in-depth error analysis may identify approaches to improve model performance. The ability to automate social media geosurveillance of substance behavior at events could be coupled with real-time data feeds. Model automation would allow these real-time data feeds to be analyzed for potential public health interventions (including messaging, Tweet geodensity dependent medical presence, or other measures) to further reduce harm

Although the putative pharmacological targets of synthetic cannabinoids (SCBs) abused in "K2" and "Spice" are similar to Delta(9) -tetrahydrocannabinol (Delta(9) -THC), it remains unclear why SCB toxicity is similar yet different from marijuana. There are obvious potency and efficacy differences, but also important metabolic differences that help explain the unique adverse reactions associated with SCBs. This brief review discusses the limited research on the metabolism of the SCB JWH-018 and contrasts that with the metabolism of Delta(9) -THC.