# Mining Online Social Data for Detecting Social Network Mental Disorders

## TL;DR

This paper introduces a machine learning framework called SNMDD that uses social network data to actively detect social network mental disorders early, without relying on questionnaires, and demonstrates its effectiveness through large-scale user studies.

## Contribution

The paper presents a novel machine learning approach and a tensor model for detecting SNMDs from social data, avoiding reliance on self-reported mental factors.

## Key findings

- SNMDD achieves promising detection accuracy.
- Multi-source learning improves model performance.
- Analysis reveals characteristics of different SNMD types.

## 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 SNMD-based 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.