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BackgroundData from the social media platform X (formerly Twitter) can provide insights into the types of language that are used when discussing drug use. In past research using latent Dirichlet allocation (LDA), we found that tweets containing “street names” of prescription drugs were difficult to classify due to the similarity to other colloquialisms and lack of clarity over how the terms were used. Conversely, “brand name” references were more amenable to machine-driven categorization. ObjectiveThis study sought to use next-generation techniques (beyond LDA) from natural language processing to reprocess X data and automatically cluster groups of tweets into topics to differentiate between street- and brand-name data sets. We also aimed to analyze the differences in emotional valence between the 2 data sets to study the relationship between engagement on social media and sentiment. MethodsWe used the Twitter application programming interface to collect tweets that contained the street and brand name of a prescription drug within the tweet. Using BERTopic in combination with Uniform Manifold Approximation and Projection and k-means, we generated topics for the street-name corpus (n=170,618) and brand-name corpus (n=245,145). Valence Aware Dictionary and Sentiment Reasoner (VADER) scores were used to classify whether tweets within the topics had positive, negative, or neutral sentiments. Two different logistic regression classifiers were used to predict the sentiment label within each corpus. The first model used a tweet’s engagement metrics and topic ID to predict the label, while the second model used those features in addition to the top 5000 tweets with the largest term-frequency–inverse document frequency score. ResultsUsing BERTopic, we identified 40 topics for the street-name data set and 5 topics for the brand-name data set, which we generalized into 8 and 5 topics of discussion, respectively. Four of the general themes of discussion in the brand-name corpus referenced drug use, while 2 themes of discussion in the street-name corpus referenced drug use. From the VADER scores, we found that both corpora were inclined toward positive sentiment. Adding the vectorized tweet text increased the accuracy of our models by around 40% compared with the models that did not incorporate the tweet text in both corpora. ConclusionsBERTopic was able to classify tweets well. As with LDA, the discussion using brand names was more similar between tweets than the discussion using street names. VADER scores could only be logically applied to the brand-name corpus because of the high prevalence of non–drug-related topics in the street-name data. Brand-name tweets either discussed drugs positively or negatively, with few posts having a neutral emotionality. From our machine learning models, engagement alone was not enough to predict the sentiment label; the added context from the tweets was needed to understand the emotionality of a tweet.

IntroductionThe prevalence of anxiety disorders, and mental chronic diseases, has increased over the last decade among adolescents. Since aerobic exercise reduces the risk of chronic diseases and stress symptoms, we aimed to examine the association between aerobic exercise in adolescence and anxiety disorders in adulthood.MethodsSelf-reported, publicly available data from 5,114 adolescents who participated in Waves I and IV of the National Longitudinal Study of Adolescent Health (Add Health) was analyzed from 1994-2009. We included US-based individuals aged 16 years on average and observed them for 15 years. Weighted Poisson regression models estimated the association between aerobic exercise in Wave I (1994, baseline) and anxiety disorders in Wave IV (2009, adulthood), adjusting for sociodemographic characteristics and substance use at baseline.ResultsOverall, 639/5,114 (weighted 12.96%) individuals experienced anxiety disorders at baseline. Age and sex differed significantly across all exercise groups (p'sConclusionAerobic Exercise in adolescence did not protect against anxiety disorders in adulthood. More evidence is needed on this association, including using homogeneous measures of exercise and repeated measures methods.