# Predicting Opioid Relapse Using Social Media Data

arXiv: https://arxiv.org/abs/1811.12169 · HTML: https://mlsanity.com/paper/1811.12169/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2018.

## Built on by (most cited first, 2 of 2)

- [Patient-independent Schizophrenia Relapse Prediction Using Mobile Sensor based Daily Behavioral Rhythm Changes](https://mlsanity.com/paper/2106.15353/) (2021)
- [Detection of Opioid Users from Reddit Posts via an Attention-based Bidirectional Recurrent Neural Network](https://mlsanity.com/paper/2403.15393/) (2024)

## Abstract (arXiv)

Opioid addiction is a severe public health threat in the U.S, causing massive deaths and many social problems. Accurate relapse prediction is of practical importance for recovering patients since relapse prediction promotes timely relapse preventions that help patients stay clean. In this paper, we introduce a Generative Adversarial Networks (GAN) model to predict the addiction relapses based on sentiment images and social influences. Experimental results on real social media data from Reddit.com demonstrate that the GAN model delivers a better performance than comparable alternative techniques. The sentiment images generated by the model show that relapse is closely connected with two emotions `joy' and `negative'. This work is one of the first attempts to predict relapses using massive social media data and generative adversarial nets. The proposed method, combined with knowledge of social media mining, has the potential to revolutionize the practice of opioid addiction prevention and treatment.