Opinion Mining of Erowid's Experience Reports on LSD and Psilocybin-Containing Mushrooms

Overview

Journal Drug Saf

Specialties Pharmacology
Toxicology

Date 2025 Mar 3

PMID 40032797

Authors

Ahmed Al-Imam

Riccardo Lora

Marek A Motyka

Erica Marletta

Michele Vezzaro

Jerzy Moczko

Manal Younus

Michal Michalak

Affiliations

Soon will be listed here.

Abstract

Background: Psychedelics are gaining attention for their therapeutic potential in modern and personalized medicine. Online forums such as Erowid provide valuable user insights, but analyses of these experiences using natural language processing (NLP) remain scarce.

Objective: This study aims to utilize NLP, including sentiment and lexicon analysis, to examine user-generated experience reports on psilocybin-containing mushrooms and LSD from the Erowid forum.

Methods: Data from 2188 Erowid users (1161 psilocybin mushrooms and 1027 LSD) was collected via automated web scraping with XPath, CSS selectors, and Selenium WebDriver. The dataset included report titles, substances, and demographics. Sentiment analysis utilized BERT, RoBERTa, and VADER models. Preprocessing involved tokenization, lemmatization, part-of-speech tagging, and stop-word filtering. Lexicon analysis identified themes through recurring n-grams, visualized using Python.

Results: User demographics revealed comparable ages for psilocybin mushrooms (23.8 ± 0.9 years) and LSD users (20.0 ± 0.6 years), with a predominance of male users. The BERT model predominantly labeled experiences as negative (unfavorable), particularly for mushroom users (p = 0.001). VADER indicated more positive experiences for mushroom users (p < 0.001), while RoBERTa mainly classified experiences as negative or neutral. Significant gender differences were found only with VADER, where more male users expressed positive opinions about psilocybin mushrooms (74.09% versus 65.52%, p < 0.021). The VADER model yielded more polarized results, whereas RoBERTa's cautious classifications indicate its suitability for analyzing lengthy and complex psychedelic reports. Further, RoBERTa outperformed other transformer-based models, achieving the highest accuracy. Lexicon analysis revealed emotional, sensory, and temporal themes, with psilocybin reports emphasizing introspection and time dilation phenomenon, while LSD reports highlighted memory issues and cognitive disorientation.

Conclusions: Sentiment analysis showed that VADER produced more polarized results, while RoBERTa offered cautious classifications with the highest accuracy. Lexicon analysis revealed shared themes, with mushroom reports focusing on introspection and time dilation perception, while those of LSD emphasized cognitive disturbances. This study highlights the value of these analyses in understanding psychedelic experiences, informing harm reduction, and guiding policy-making.

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