A survey on mental health detection in Online Social Network

Rohizah Abd Rahman, Khairuddin Omar, Shahrul Azman Mohd Noah, Mohd Shahrul Nizam Mohd Danuri

Research output: Contribution to journalArticle

Abstract

Mental health detection in Online Social Network (OSN) is widely studied in the recent years. OSN has encouraged new ways to communicate and share information, and it is used regularly by millions of people. It generates a mass amount of information that can be utilised to develop mental health detection. The rich content provided by OSN should not be overlooked as it could give more value to the data explored by the researcher. The main purpose of this study is to extract and scrutinise related works from related literature on detection of mental health using OSN. With the focus on the method used, machine learning algorithm, sources of OSN, and types of language used for the mental health detection were chosen for the study. The basic design of this study is in the form of a survey from the literature related to current research in mental health. Major findings revealed that the most frequently used method in mental health detection is machine learning techniques, with Support Vector Machine (SVM) as the most chosen algorithm. Meanwhile, Twitter is the major data source from OSN with English language used for mental health detection. The researcher found a few challenges from the previous studies and analyses, and these include limitations in language barrier, account privacy in OSN, single type of OSN, text analysis, and limited features selection. Based on the limitations, the researcher outlined a future direction of mental health detection using language based on user's geo-location and mother tongue. The use of pictorial, audio and video formats in OSN could become one of the potential areas to be explored in future research. Extracting data from multiple sources of OSNs with new features selection will probably improve mental health detection in the future. In conclusion, this research has a big potential to be explored further in the future.

Original languageEnglish
Pages (from-to)1431-1436
Number of pages6
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Volume8
Issue number4-2
Publication statusPublished - 1 Jan 2018

Fingerprint

mental health
social networks
Social Support
Mental Health
Health
Language
researchers
artificial intelligence
Information Storage and Retrieval
Research Personnel
Learning systems
Feature extraction
Communication Barriers
Surveys and Questionnaires
Privacy
tongue
Tongue
Research
Learning algorithms
Support vector machines

Keywords

  • Big data
  • Depression
  • Machine learning
  • Stress
  • Twitter

ASJC Scopus subject areas

  • Computer Science(all)
  • Agricultural and Biological Sciences(all)
  • Engineering(all)

Cite this

A survey on mental health detection in Online Social Network. / Rahman, Rohizah Abd; Omar, Khairuddin; Mohd Noah, Shahrul Azman; Danuri, Mohd Shahrul Nizam Mohd.

In: International Journal on Advanced Science, Engineering and Information Technology, Vol. 8, No. 4-2, 01.01.2018, p. 1431-1436.

Research output: Contribution to journalArticle

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