Trends in Fentanyl Content on Reddit Substance Use Forums, 2013-2021.

BACKGROUND: Fentanyl is a pressing concern in the current drug supply. Social media data can provide access to near real-time understanding of drug trends that may complement official mortality data. DESIGN: The total number of fentanyl-related posts and the total number of posts for eight drug subreddit categories (alcohol, cannabis, hallucinogens, multi-drug, opioids, over the counter, sedatives, stimulants) were collected from 2013 to 2021 using the Pushshift Reddit dataset. The proportion of fentanyl-related posts as a fragment of total subreddit posts was examined. Linear regressions described the rate of change in post volume over time. RESULTS: Overall, fentanyl-related content increased across drug-related subreddits from 2013 to 2021 (1292% increase, linear trend p ≤ 0.001). Opioid subreddits (30.62 per 1000 posts, linear trend p ≤ 0.001) had the most fentanyl-related content during the examined time period. Multi-drug (5.95 per 1000; p ≤ 0.01), sedative (3.23 per 1000, p ≤ 0.01), and stimulant (1.60 per 1000, p ≤ 0.01) subreddits also had substantial increases in fentanyl-related content. The greatest increases occurred in the multi-drug (1067% 2013:2021) and stimulant (1862% 2014:2021) subreddits. CONCLUSION: Fentanyl-related posts on Reddit trended upward, with the fastest rate of change for multi-substance and stimulant subreddits. Beyond opioids, harm reduction and public health messaging should ensure inclusion of individuals who use other drugs.

Acquisition and evaluation of verb subcategorization resources for biomedicine.

BACKGROUND: Biomedical natural language processing (NLP) applications that have access to detailed resources about the linguistic characteristics of biomedical language demonstrate improved performance on tasks such as relation extraction and syntactic or semantic parsing. Such applications are important for transforming the growing unstructured information buried in the biomedical literature into structured, actionable information. In this paper, we address the creation of linguistic resources that capture how individual biomedical verbs behave. We specifically consider verb subcategorization, or the tendency of verbs to "select" co-occurrence with particular phrase types, which influences the interpretation of verbs and identification of verbal arguments in context. There are currently a limited number of biomedical resources containing information about subcategorization frames (SCFs), and these are the result of either labor-intensive manual collation, or automatic methods that use tools adapted to a single biomedical subdomain. Either method may result in resources that lack coverage. Moreover, the quality of existing verb SCF resources for biomedicine is unknown, due to a lack of available gold standards for evaluation. RESULTS: This paper presents three new resources related to verb subcategorization frames in biomedicine, and four experiments making use of the new resources. We present the first biomedical SCF gold standards, capturing two different but widely-used definitions of subcategorization, and a new SCF lexicon, BioCat, covering a large number of biomedical sub-domains. We evaluate the SCF acquisition methodologies for BioCat with respect to the gold standards, and compare the results with the accuracy of the only previously existing automatically-acquired SCF lexicon for biomedicine, the BioLexicon. Our results show that the BioLexicon has greater precision while BioCat has better coverage of SCFs. Finally, we explore the definition of subcategorization using these resources and its implications for biomedical NLP. All resources are made publicly available. CONCLUSION: The SCF resources we have evaluated still show considerably lower accuracy than that reported with general English lexicons, demonstrating the need for domain- and subdomain-specific SCF acquisition tools for biomedicine. Our new gold standards reveal major differences when annotators use the different definitions. Moreover, evaluation of BioCat yields major differences in accuracy depending on the gold standard, demonstrating that the definition of subcategorization adopted will have a direct impact on perceived system accuracy for specific tasks.

Approaches to verb subcategorization for biomedicine.

Information about verb subcategorization frames (SCFs) is important to many tasks in natural language processing (NLP) and, in turn, text mining. Biomedicine has a need for high-quality SCF lexicons to support the extraction of information from the biomedical literature, which helps biologists to take advantage of the latest biomedical knowledge despite the overwhelming growth of that literature. Unfortunately, techniques for creating such resources for biomedical text are relatively undeveloped compared to general language. This paper serves as an introduction to subcategorization and existing approaches to acquisition, and provides motivation for developing techniques that address issues particularly important to biomedical NLP. First, we give the traditional linguistic definition of subcategorization, along with several related concepts. Second, we describe approaches to learning SCF lexicons from large data sets for general and biomedical domains. Third, we consider the crucial issue of linguistic variation between biomedical fields (subdomain variation). We demonstrate significant variation among subdomains, and find the variation does not simply follow patterns of general lexical variation. Finally, we note several requirements for future research in biomedical SCF lexicon acquisition: a high-quality gold standard, investigation of different definitions of subcategorization, and minimally-supervised methods that can learn subdomain-specific lexical usage without the need for extensive manual work.

Exploring subdomain variation in biomedical language.

BACKGROUND: Applications of Natural Language Processing (NLP) technology to biomedical texts have generated significant interest in recent years. In this paper we identify and investigate the phenomenon of linguistic subdomain variation within the biomedical domain, i.e., the extent to which different subject areas of biomedicine are characterised by different linguistic behaviour. While variation at a coarser domain level such as between newswire and biomedical text is well-studied and known to affect the portability of NLP systems, we are the first to conduct an extensive investigation into more fine-grained levels of variation. RESULTS: Using the large OpenPMC text corpus, which spans the many subdomains of biomedicine, we investigate variation across a number of lexical, syntactic, semantic and discourse-related dimensions. These dimensions are chosen for their relevance to the performance of NLP systems. We use clustering techniques to analyse commonalities and distinctions among the subdomains. CONCLUSIONS: We find that while patterns of inter-subdomain variation differ somewhat from one feature set to another, robust clusters can be identified that correspond to intuitive distinctions such as that between clinical and laboratory subjects. In particular, subdomains relating to genetics and molecular biology, which are the most common sources of material for training and evaluating biomedical NLP tools, are not representative of all biomedical subdomains. We conclude that an awareness of subdomain variation is important when considering the practical use of language processing applications by biomedical researchers.