The Centers for Disease Control and Prevention (CDC) reported that the opioid crisis caused more than 80,000 Americans to die in 2021[1], and in 2019, the number of deaths worldwide surpassed 600,000 [2]. Since 2013, significant increases in opioid-related deaths have been attributed to the use of synthetic opioids, especially fentanyl. Current methods of monitoring and understanding the opioid crisis rely heavily on traditional data sources such as state-level statistical aggregation and reporting through agencies such as the CDC. National statistics on opioid overdose deaths are released annually by the National Center for Health Statistics (NCHS), a division of the CDC, which compiles these data from the National Death Index [3]. These traditional sources, though comprehensive, take significant time to prepare and disseminate. The rapid emergence of traditional and synthetic drug production and distribution is a constant threat that law enforcement agencies and public health agencies face. The vernacular used by those who produce and distribute illegal drugs also continues to evolve, posing potential, time-sensitive risks. Technical and regulatory issues may further delay data availability, significantly hindering public health responses in several ways. This can result in missed opportunities for early intervention, allowing epidemics to become unchecked. There is also the risk of misallocating resources due to outdated or significantly lagging information. Additionally, it can delay the identification of new drug trends or emerging health crises, which contributes to the effectiveness of educational and prevention programs. Ultimately, these delays can increase morbidity, mortality, and economic losses associated with health crises.
With the ubiquity of social media among Americans, online forums have emerged as vital sources for real-time health data, particularly in substance abuse research [4]. While X, previously known as Twitter, has been a focal point of this research, the broader digital ecosystem presents a candid window into drug use discussions [5–8]. Social media's real-time data and anonymous nature facilitate a deeper understanding of drug use patterns, impacts, and trends, surpassing traditional surveillance methods in speed and insight, thus enhancing our ability to monitor and intervene in drug-related public health issues [9]. Although they do not exceed traditional methods in terms of specificity and reliability, social media involves a vast volume of drug-related conversations. Among social media platforms, Reddit.com has emerged as a high-volume platform, especially for discussions surrounding opioid use [10, 11]. Home to many subcommunities or 'subreddits,' Reddit fosters an enriched discussion of specific interests, including opioids, through its largest opioid-related subreddit, r/Opiates, which boasts over 200,000 members. This subreddit thrives on active participation, with thousands of posts and comments daily. Reddit candid engagement and a wide range of conversations provide an invaluable, data-rich environment for a comprehensive overview of opioid trends and behaviors [11].
Existing computational methods for identifying drugs of abuse often rely on static keyword lists or manual dataset curation. This results in limitations due to keyword list comprehensiveness or the effort required for manual selection. Online dynamic and slang-heavy language drug discussions present additional challenges, necessitating accurate slang identification for precise research results. While recent advancements have improved slang term identification, they largely depend on labor-intensive manual example gathering [12]. This highlights a crucial gap in research: the need for an efficient, automated approach to identifying slang from minimal initial data [13]. Current approaches using pretrained machine learning models rely on significant dataset reviews and subject matter expertise [14]. An updated method is needed to identify key slang terminology with minimal human input rapidly. It can either stand alone or supplement existing utilities, enhancing functionality with reduced complexity. Moreover, understanding the interplay between opioids and other substances or usage methods is vital for comprehensively assessing public health implications.
This study advances the field of big data linguistic analysis of the opioid crisis by using Reddit data to automate the discovery of drug-related slang from a few starting terms. Through the development of an autoencoding neural network, a specialized vector space was crafted from Reddit discourse, facilitating the detection of new slang terms closely tied to initial keywords. Our linguistic investigation further leveraged this technology to discover previously undetected drug references, which are provided as a supplementary file, offering key insights into the community’s nuanced and constantly changing terminology of drug use. Moreover, by analyzing the semantic relationships among drug categories and their slang, this study provides novel steps in understanding the discourse surrounding opioids and related drugs of abuse. The implications of this work extend far beyond opioid slang, promising applications in broader substance use research and setting a precedent for rapid, data-driven public health responses.