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A Survey on Mental Health Detection in Online Social Network

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@article{IJASEIT6830,
   author = {Rohizah Abd Rahman and Khairuddin Omar and Shahrul Azman Mohd Noah and Mohd Shahrul Nizam Mohd Danuri},
   title = {A Survey on Mental Health Detection in Online Social Network},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {8},
   number = {4-2},
   year = {2018},
   pages = {1431--1436},
   keywords = {Stress; Depression; Twitter; Big Data; Machine Learning},
   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.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6830},    doi = {10.18517/ijaseit.8.4-2.6830} }

EndNote

%A Abd Rahman, Rohizah
%A Omar, Khairuddin
%A Mohd Noah, Shahrul Azman
%A Mohd Danuri, Mohd Shahrul Nizam
%D 2018
%T A Survey on Mental Health Detection in Online Social Network
%B 2018
%9 Stress; Depression; Twitter; Big Data; Machine Learning
%! A Survey on Mental Health Detection in Online Social Network
%K Stress; Depression; Twitter; Big Data; Machine Learning
%X 

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.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6830 %R doi:10.18517/ijaseit.8.4-2.6830 %J International Journal on Advanced Science, Engineering and Information Technology %V 8 %N 4-2 %@ 2088-5334

IEEE

Rohizah Abd Rahman,Khairuddin Omar,Shahrul Azman Mohd Noah and Mohd Shahrul Nizam Mohd Danuri,"A Survey on Mental Health Detection in Online Social Network," International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 4-2, pp. 1431-1436, 2018. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.8.4-2.6830.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Abd Rahman, Rohizah
AU  - Omar, Khairuddin
AU  - Mohd Noah, Shahrul Azman
AU  - Mohd Danuri, Mohd Shahrul Nizam
PY  - 2018
TI  - A Survey on Mental Health Detection in Online Social Network
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 8 (2018) No. 4-2
Y2  - 2018
SP  - 1431
EP  - 1436
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Stress; Depression; Twitter; Big Data; Machine Learning
N2  - 

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.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6830 DO - 10.18517/ijaseit.8.4-2.6830

RefWorks

RT Journal Article
ID 6830
A1 Abd Rahman, Rohizah
A1 Omar, Khairuddin
A1 Mohd Noah, Shahrul Azman
A1 Mohd Danuri, Mohd Shahrul Nizam
T1 A Survey on Mental Health Detection in Online Social Network
JF International Journal on Advanced Science, Engineering and Information Technology
VO 8
IS 4-2
YR 2018
SP 1431
OP 1436
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Stress; Depression; Twitter; Big Data; Machine Learning
AB 

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.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6830 DO - 10.18517/ijaseit.8.4-2.6830