Detecting People Interested in Non-Suicidal Self-Injury on Social Media

Zaihan Yang, Dmitry Zinoviev

Abstract: We propose a supervised learning approach to detect people interested in Non-Suicidal Self-Injury (NSSI). We treat the task as a binary classification problem, and build classifiers based upon features extracted from people self-declared interests. Experimental evaluation on a real-world dataset, the LiveJournal social blogging networking platform, demonstrates the effectiveness of our proposed model.