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        <title>Analysing the Causes of Depressed Mood from Depression Vulnerable Individuals</title>
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            <namePart type="given">Jitendra</namePart>
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    <abstract>We develop a computational model to discover the potential causes of depression by analysing the topics in a usergenerated text. 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.</abstract>
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%0 Conference Proceedings
%T Analysing the Causes of Depressed Mood from Depression Vulnerable Individuals
%A Abd Yusof, Noor Fazilla
%A Lin, Chenghua
%A Guerin, Frank
%Y Jonnagaddala, Jitendra
%Y Dai, Hong-Jie
%Y Chang, Yung-Chun
%S Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)
%D 2017
%8 November
%I Association for Computational Linguistics
%C Taipei, Taiwan
%F abd-yusof-etal-2017-analysing
%X We develop a computational model to discover the potential causes of depression by analysing the topics in a usergenerated text. 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.
%U https://aclanthology.org/W17-5802
%P 9-17

Markdown (Informal)

[Analysing the Causes of Depressed Mood from Depression Vulnerable Individuals](https://aclanthology.org/W17-5802) (Abd Yusof et al., 2017)

• Analysing the Causes of Depressed Mood from Depression Vulnerable Individuals (Abd Yusof et al., 2017)

ACL

• Noor Fazilla Abd Yusof, Chenghua Lin, and Frank Guerin. 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), pages 9–17, Taipei, Taiwan. Association for Computational Linguistics.