Mining online social data for detecting social network mental disorders
Journal
25th International World Wide Web Conference, WWW 2016
Pages
275-285
Date Issued
2016
Author(s)
Abstract
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 paper, we argue that mining online social behavior provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mental factors 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 mental factors 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. We also exploit multi-source learning in SNMDD and propose a new SNMDbased Tensor Model (STM) to improve the performance. Our framework is evaluated via a user study with 3126 online social network users. We conduct a feature analysis, and also apply SNMDD on large-scale datasets and analyze the characteristics of the three SNMD types. The results show that SNMDD is promising for identifying online social network users with potential SNMDs.
Subjects
Feature Extraction; Mental Disorder Detection; Online Social Network; Tensor Factorization
SDGs
Other Subjects
Feature extraction; Learning systems; Surveys; Tensors; World Wide Web; Clinical interventions; Information overloads; Large-scale datasets; Mental disorders; On-line social networks; Online social behaviors; Social activities; Tensor factorization; Social networking (online)
Type
conference paper