Analysing the Causes of Depressed Mood from Depression Vulnerable Individuals

Noor Fazilla Binti Abd Yusof; Chenghua Lin; Frank Guerin

Analysing the Causes of Depressed Mood from Depression Vulnerable Individuals

Noor Fazilla Binti Abd Yusof, Chenghua Lin, Frank Guerin

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Abstract

We develop a computational model to discover the potential causes of depression by analysing the topics from user-generated contents. We show the most prominent causes, and how these causes evolve over time. Also, we highlight the differences in causes between students with low and high neuroticism. Our studies demonstrate that the topics reveal valuable clues about the causes contributing to depressed mood. Identifying causes can have a significant impact on improving the quality of depression care; thereby providing greater insights into a patient’s state for pertinent treatment recommendations. Hence, this study significantly expands the ability to discover the potential factors that trigger depression, making it possible to increase the efficiency of depression treatment.

Original language	English
Title of host publication	Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)
Publisher	AFNLP
Pages	9-17
Number of pages	9
ISBN (Print)	978-1-948087-07-0
Publication status	Published - 27 Nov 2017
Event	The 8th International Joint Conference on Natural Language Processing - Taipei, Taiwan, Province of China Duration: 27 Nov 2017 → 1 Dec 2017 http://ijcnlp2017.org/site/page.aspx?pid=901&sid=1133&lang=en

Conference

Conference	The 8th International Joint Conference on Natural Language Processing
Abbreviated title	IJCNLP 2017
Country/Territory	Taiwan, Province of China
City	Taipei
Period	27/11/17 → 1/12/17
Internet address	http://ijcnlp2017.org/site/page.aspx?pid=901&sid=1133&lang=en

Bibliographical note

This work is supported by the awards made by the UK Engineering and Physical Sciences Research Council (Grant number: EP/P005810/1). Lead author would like to thank Ministry of Higher Education Malaysia and Technical University of Malaysia Malacca for PhD scholarship award.

Access to Document

http://www.aclweb.org/anthology/W/W17/W17-5800.pdfLicence: Unspecified

Cite this

Analysing the Causes of Depressed Mood from Depression Vulnerable Individuals. / Abd Yusof, Noor Fazilla Binti; Lin, Chenghua; Guerin, Frank.
Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017). AFNLP, 2017. p. 9-17.

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Abd Yusof, NFB, Lin, C & Guerin, F 2017, Analysing the Causes of Depressed Mood from Depression Vulnerable Individuals. in Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017). AFNLP, pp. 9-17, The 8th International Joint Conference on Natural Language Processing, Taipei, Taiwan, Province of China, 27/11/17. <http://www.aclweb.org/anthology/W/W17/W17-5800.pdf>

@inproceedings{d2102c2e1c72458f850c94776bcfc8ff,

title = "Analysing the Causes of Depressed Mood from Depression Vulnerable Individuals",

abstract = "We develop a computational model to discover the potential causes of depression by analysing the topics from user-generated contents. We show the most prominent causes, and how these causes evolve over time. Also, we highlight the differences in causes between students with low and high neuroticism. Our studies demonstrate that the topics reveal valuable clues about the causes contributing to depressed mood. Identifying causes can have a significant impact on improving the quality of depression care; thereby providing greater insights into a patient{\textquoteright}s state for pertinent treatment recommendations. Hence, this study significantly expands the ability to discover the potential factors that trigger depression, making it possible to increase the efficiency of depression treatment.",

author = "{Abd Yusof}, {Noor Fazilla Binti} and Chenghua Lin and Frank Guerin",

note = "This work is supported by the awards made by the UK Engineering and Physical Sciences Research Council (Grant number: EP/P005810/1). Lead author would like to thank Ministry of Higher Education Malaysia and Technical University of Malaysia Malacca for PhD scholarship award.; The 8th International Joint Conference on Natural Language Processing, IJCNLP 2017 ; Conference date: 27-11-2017 Through 01-12-2017",

year = "2017",

month = nov,

day = "27",

language = "English",

isbn = "978-1-948087-07-0",

pages = "9--17",

booktitle = "Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)",

publisher = "AFNLP",

url = "http://ijcnlp2017.org/site/page.aspx?pid=901&sid=1133&lang=en",

}

TY - GEN

T1 - Analysing the Causes of Depressed Mood from Depression Vulnerable Individuals

AU - Abd Yusof, Noor Fazilla Binti

AU - Lin, Chenghua

AU - Guerin, Frank

N1 - This work is supported by the awards made by the UK Engineering and Physical Sciences Research Council (Grant number: EP/P005810/1). Lead author would like to thank Ministry of Higher Education Malaysia and Technical University of Malaysia Malacca for PhD scholarship award.

PY - 2017/11/27

Y1 - 2017/11/27

N2 - We develop a computational model to discover the potential causes of depression by analysing the topics from user-generated contents. We show the most prominent causes, and how these causes evolve over time. Also, we highlight the differences in causes between students with low and high neuroticism. Our studies demonstrate that the topics reveal valuable clues about the causes contributing to depressed mood. Identifying causes can have a significant impact on improving the quality of depression care; thereby providing greater insights into a patient’s state for pertinent treatment recommendations. Hence, this study significantly expands the ability to discover the potential factors that trigger depression, making it possible to increase the efficiency of depression treatment.

AB - We develop a computational model to discover the potential causes of depression by analysing the topics from user-generated contents. We show the most prominent causes, and how these causes evolve over time. Also, we highlight the differences in causes between students with low and high neuroticism. Our studies demonstrate that the topics reveal valuable clues about the causes contributing to depressed mood. Identifying causes can have a significant impact on improving the quality of depression care; thereby providing greater insights into a patient’s state for pertinent treatment recommendations. Hence, this study significantly expands the ability to discover the potential factors that trigger depression, making it possible to increase the efficiency of depression treatment.

M3 - Published conference contribution

SN - 978-1-948087-07-0

SP - 9

EP - 17

BT - Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)

PB - AFNLP

T2 - The 8th International Joint Conference on Natural Language Processing

Y2 - 27 November 2017 through 1 December 2017

ER -

Analysing the Causes of Depressed Mood from Depression Vulnerable Individuals

Abstract

Conference

Bibliographical note

Access to Document

Fingerprint

Cite this