Predicting Opioid Relapse Using Social Media Data
This work addresses opioid addiction prevention for recovering patients by providing a novel prediction method, though it is an early attempt in this domain.
The paper tackles the problem of predicting opioid relapse by using a Generative Adversarial Networks (GAN) model on social media data from Reddit, achieving better performance than alternative techniques and identifying connections between relapse and emotions like 'joy' and 'negative'.
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.