A Hybrid Approach for Drug Abuse Events Extraction from Twitter

Since their emergence, social media have become a reliable source of social events which attracted the interest of research community to extract them for many business requirements. However, unlike formal sources like news articles, social data exploitation for events extraction is much harder regarding the complex character of social text. Many approaches, ranging from linguistic techniques to learning algorithms, were proposed to succeed this task. Nevertheless, achieved results are weak regarding the complexity and completeness of the task.In this paper, we focus on private events extraction from Twitter by tracking digital drug abusers. We propose a hybrid approach in which we combine strengths of linguistic rules and learning techniques looking for better performance. In fact, we use linguistic rules to build an automatically annotated training set and extract a set of features as well, to be used in a learning process in order to improve obtained results. The proposed approach outperforms the baseline by 24,8% thanks to combination of techniques.