Predicting Opioid Relapse Using Social Media Data

Abstract--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 patientssince 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. I. INTRODUCTION Opioid addiction is a severe public health threat in the U.S, causing massive deaths and many social problems [1]. According to the latest statistics of National Institute on Drug Abuse (NIDA, 2017), more than 115 Americans die after overdosing on opioids on a daily basis, and nearly 64,000 people died of drug overdoses in the US in 2016, the most lethal year of the drug overdose epidemic (NIDA, 2017).