{"type":"ScholarlyArticle","title":"Automatic Opioid User Detection from Twitter: Transductive Ensemble Built on Different Meta-graph Based Similarities over Heterogeneous Information Network","authors":[{"name":"Yujie Fan","slug":"yujie-fan","given":"Yujie","family":"Fan","affiliation":"Department of Computer Science and Electrical Engineering , West Virginia University , WV , USA"},{"name":"Yiming Zhang","slug":"yiming-zhang","given":"Yiming","family":"Zhang","affiliation":"Department of Computer Science and Electrical Engineering , West Virginia University , WV , USA"},{"name":"Yanfang Ye","slug":"yanfang-ye","given":"Yanfang","family":"Ye","affiliation":"Department of Computer Science and Electrical Engineering , West Virginia University , WV , USA"},{"name":"Xin Li","slug":"xin-li","given":"Xin","family":"Li","affiliation":"Department of Computer Science and Electrical Engineering , West Virginia University , WV , USA"}],"abstract":"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 HinOPU to automatically detect opioid users from Twitter, which will assist in sharpening our understanding toward the behavioral process of opioid addiction and treatment. In HinOPU, to model the users and the posted tweets as well as their rich relationships, we introduce structured heterogeneous information network (HIN) for representation. Afterwards, we use meta-graph based approach to characterize the semantic relatedness over users; we then formulate different similarities over users based on different meta-graphs on HIN. To reduce the\r\ncost of acquiring labeled samples for supervised learning, we propose a transductive classification method to build the base classifiers based on different similarities formulated by different meta-graphs. Then, to further improve the detection accuracy, we construct an ensemble to combine different predictions from different base classifiers for opioid user detection. Comprehensive experiments on real sample collections from Twitter are conducted to validate the effectiveness of HinOPU in opioid user detection by comparisons with other alternate methods.","doi":"10.24963/ijcai.2018/466","url":"https://ijcai.org/proceedings/2018/466","pdf":"https://ijcai.org/proceedings/2018/0466.pdf","markdown":"https://ijcai.org/proceedings/2018/466.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/466","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":466,"pages":{"first":3357,"last":3363},"keywords":["Machine Learning: Classification","Machine Learning Applications: Bio;Medicine","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Alkhateeb et al., 2011] Fadi M Alkhateeb, Kevin A Clau- son, and David A Latif. 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