Semi-supervised knowledge extraction for detection of drugs and their effects

TL;DR

This paper presents a semi-supervised method for extracting information about drugs and their effects from online forum posts, achieving high accuracy and aiding rapid detection of new psychoactive substances.

Contribution

It introduces a contrastive, semi-supervised approach that effectively detects drugs and effects from large text corpora using minimal initial seed data.

Findings

F1 score close to 0.9 demonstrating high accuracy

Effective detection of drugs and effects from online forums

Potential to shorten detection time for new psychoactive substances

Abstract

New Psychoactive Substances (NPS) are drugs that lay in a grey area of legislation, since they are not internationally and officially banned, possibly leading to their not prosecutable trade. The exacerbation of the phenomenon is that NPS can be easily sold and bought online. Here, we consider large corpora of textual posts, published on online forums specialized on drug discussions, plus a small set of known substances and associated effects, which we call seeds. We propose a semi-supervised approach to knowledge extraction, applied to the detection of drugs (comprising NPS) and effects from the corpora under investigation. Based on the very small set of initial seeds, the work highlights how a contrastive approach and context deduction are effective in detecting substances and effects from the corpora. Our promising results, which feature a F1 score close to 0.9, pave the way for shortening the detection time of new psychoactive substances, once these are discussed and advertised on the Internet.