Institute of Information Science, Academia Sinica

While online social networks have become a part of many people’s daily lives, an increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed passively today, resulting in delayed clinical intervention. In this talk, we first point out that mining online social behavior provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mentalfactors considered in standard diagnostic criteria (questionnaire) cannot be observed from online social activity logs. Our approach, new and innovative to the practice of SNMD detection, does not rely on self-revealing of those mentalfactors via questionnaires. Instead, we propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD), that exploits features extracted from social network data to accurately identify potential cases of SNMDs. Moreover, with increasing patients in addictive Internet use, clinicians often form support groups to help patients. With the emergence of online social network services, there is a trend to form support groups online with the aid of psychiatrists. Nevertheless, it becomes impractical for a psychiatrist to manually select the group members because the enormous number of candidates and the complicated selection criteria. To address the need of psychiatrists, we then a new problem, namely Member Selection for Online Support Group (MSSG). We prove MSSG NP-Hard and inapproximable within any ratio, and design a 3-approximation with a guaranteed error bound. We evaluate MSSG via a user study with 11 mental health professionals and via extensive experiments on real datasets.