Title: 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 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.