Social media mining for toxicovigilance of prescription medications: End-to-end pipeline, challenges and future work

TL;DR

This paper presents an end-to-end AI pipeline for mining social media data to improve surveillance of prescription medication misuse, addressing current challenges and outlining future research directions.

Contribution

Development of a comprehensive social media mining pipeline for toxicovigilance of prescription drugs, integrating NLP and machine learning over four years.

Findings

Effective filtering of noise from social media data

Characterization of social media chatter about prescription drugs

Discussion of challenges and future research avenues

Abstract

Substance use, substance use disorder, and overdoses related to substance use are major public health problems globally and in the United States. A key aspect of addressing these problems from a public health standpoint is improved surveillance. Traditional surveillance systems are laggy, and social media are potentially useful sources of timely data. However, mining knowledge from social media is challenging, and requires the development of advanced artificial intelligence, specifically natural language processing (NLP) and machine learning methods. We developed a sophisticated end-to-end pipeline for mining information about nonmedical prescription medication use from social media, namely Twitter and Reddit. Our pipeline employs supervised machine learning and NLP for filtering out noise and characterizing the chatter. In this paper, we describe our end-to-end pipeline developed over four years. In addition to describing our data mining infrastructure, we discuss existing challenges in social media mining for toxicovigilance, and possible future research directions.