Text classification models for the automatic detection of nonmedical prescription medication use from social media.

ABSTRACT:

Background

Prescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging-requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter.

Methods

We experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the de

SUBMITTER: Al-Garadi MA 

PROVIDER: S-EPMC7835447 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature