Quantitative Natural Language Processing Markers of Psychoactive Drug Effects: A Pre-Registered Systematic Review

Abstract

Psychoactive substances used for recreational purposes have mind-altering effects, but systematic evaluation of these effects is largely limited to self-reports. Automated analysis of expressed language (speech and written text) using Natural Language Processing (NLP) tools can provide objective readouts of mental states. In this pre-registered systematic review, we investigate findings from the emerging field of computational linguistics in substance use with specific focus on identifying short-term effects of psychoactive drugs. From the literature identified to date, we note that all the studied drugs – stimulants, MDMA, cannabis, ketamine, and psychedelics – affect language production. Based on two or more studies per substance, we note some emerging patterns: stimulants increase verbosity; LSD reduces the lexicon; MDMA increases semantic proximity to emotional words; psilocybin increases positive sentiment; and cannabis affects speech stream acoustics. Ketamine and other drugs are understudied regarding NLP features (one or no studies). One study provided externally validated support for NLP and machine learning-based identification of MDMA intoxication. We could not undertake a meta-analysis due to the high degree of heterogeneity among outcome measures and the lack of sufficient number of studies. We identify a need for harmonised speech tasks to improve replicability and comparability, standardisation of methods for curating and analysing speech and text data, theory-driven inquiries, and the need for developing a shared Substance Use Language Corpus for data mining. The growing field of computational linguistics can be leveraged in the service of human behavioural pharmacology to study psychoactive substances through concerted efforts to achieve consistency in research methods.