Quantitative Analysis of Narrative Reports of Psychedelic Drugs
arXiv Preprint Archive June 1, 2012 Jeremy R. Coyle, David E. Presti, Matthew J. Baggott
Machine learning applied to 1000 written reports of 10 different drugs from the Erowid website identified distinct patterns in how people describe their experiences with each drug. A random-forest classifier using just 110 key words achieved 51.1% accuracy in identifying which drug a report described, far above the 10% expected by chance. Reports of MDMA were most distinctive (86.9% accuracy), while those for DPT were hardest to classify (20.1%). Hierarchical clustering revealed similarities between certain drugs, such as DMT and Salvia divinorum. The findings suggest that automated text analysis can uncover consistent, drug-specific features in subjective experience reports, potentially aiding hypothesis generation about new or poorly understood compounds.