Abstract
Substances involved in overdose deaths have shifted over time and continue to undergo transition. Early detection of emerging drugs involved in overdose is a major challenge for traditional public health data systems. While novel social media data have shown promise, there is a continued need for robust natural language processing approaches that can identify emerging substances. Consequently, we developed a new metric, the relative similarity ratio, based on diachronic word embeddings to measure movement in the semantic proximity of individual substance words to 'overdose' over time. Our analysis of 64,420,376 drug-related posts made between January 2011 and December 2018 on Reddit, the largest online forum site, reveals that this approach successfully identified fentanyl, the most significant emerging substance in the overdose epidemic, >1 year earlier than traditional public health data systems. Use of diachronic word embeddings may enable improved identification of emerging substances involved in drug overdose, thereby improving the timeliness of prevention and treatment activities.