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 proceedingConference 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 languageEnglish
Title of host publicationProceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)
PublisherAFNLP
Pages9-17
Number of pages9
ISBN (Print)978-1-948087-07-0
Publication statusPublished - 27 Nov 2017
EventThe 8th International Joint Conference on Natural Language Processing - Taipei, Taiwan, Province of China
Duration: 27 Nov 20171 Dec 2017
http://ijcnlp2017.org/site/page.aspx?pid=901&sid=1133&lang=en

Conference

ConferenceThe 8th International Joint Conference on Natural Language Processing
Abbreviated titleIJCNLP 2017
CountryTaiwan, Province of China
CityTaipei
Period27/11/171/12/17
Internet address

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  • Cite this

    Abd Yusof, N. F. B., 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) (pp. 9-17). AFNLP. http://www.aclweb.org/anthology/W/W17/W17-5800.pdf