Utilizing Social Media to Combat Opioid Addiction Epidemic: Automatic Detection of Opioid Users from Twitter

Opioid (e.g., heroin and morphine) addiction has become one of the largest and deadliest epidemics in the United States. To combat such deadly epidemic, in this paper, we propose a novel framework named AutoOPU to automatically detect the opioid users from Twitter, which will assist in sharpen- ing our understanding toward the behavioral process of opioid addiction and treatment. In AutoOPU , to model the users and posted tweets as well as their rich relationships, we first introduce a heterogeneous information network (HIN) for representation. Then we use meta-structure based approach to characterize the semantic relatedness over users. After- wards, we integrate content-based similarity and relatedness depicted by each meta-structure to formulate a similarity measure over users. Further, we aggregate different similarities using multi-kernel learning, each of which is automati- cally weighted by the learning algorithm to make predictions. To the best of our knowledge, this is the first work to use multi-kernel learning based on meta-structures over HIN for biomedical knowledge mining, especially in drug-addiction domain. Comprehensive experiments on real sample collections from Twitter are conducted to validate the effectiveness of our developed system AutoOPU in opioid user detection by comparisons with other alternative methods.