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

arXiv: https://arxiv.org/abs/2211.10443 · HTML: https://mlsanity.com/paper/2211.10443/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 18 Nov 2022.

## Builds on (most cited first, 2 of 2)

- [An unsupervised and customizable misspelling generator for mining noisy health-related text sources](https://mlsanity.com/paper/1806.00910/) (2018)
- [Towards Automatic Bot Detection in Twitter for Health-related Tasks](https://mlsanity.com/paper/1909.13184/) (2019)

## Built on by (most cited first, 1 of 1)

- [Reddit-Impacts: A Named Entity Recognition Dataset for Analyzing Clinical and Social Effects of Substance Use Derived from Social Media](https://mlsanity.com/paper/2405.06145/) (2024)

## Abstract (arXiv)

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.