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Opioid addiction is a severe public health threat in the U.S, causing massivedeaths and many social problems. Accurate relapse prediction is of practicalimportance for recovering patients since relapse prediction promotes timelyrelapse preventions that help patients stay clean. In this paper, we introducea Generative Adversarial Networks (GAN) model to predict the addiction relapsesbased on sentiment images and social influences. Experimental results on realsocial media data from Reddit.com demonstrate that the GAN model delivers abetter performance than comparable alternative techniques. The sentiment imagesgenerated by the model show that relapse is closely connected with two emotions`joy' and `negative'. This work is one of the first attempts to predictrelapses using massive social media data and generative adversarial nets. Theproposed method, combined with knowledge of social media mining, has thepotential to revolutionize the practice of opioid addiction prevention andtreatment.

Predicting Opioid Relapse Using Social Media Data