research-article

Event detection for exploring emotional upheavals of depressive people

Authors:

Arbee L. P. ChenAuthors Info & Claims

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

Pages 2086 - 2095

https://doi.org/10.1145/3297280.3297485

Published: 08 April 2019 Publication History

Abstract

Depression is a common illness that negatively affects how people feel, think, and act. It causes feelings of sadness and sleeping disorders. In serious cases, it leads to self-harm or suicide. Many researchers in computer science addressed the problem of depression detection. However, less research concerns the emotional upheaval of depressive people and investigates the reasons behind the depression. In this paper, a deep learning model is first constructed to automatically determine the negative sentiment degree for a Facebook post. The curves of emotional upheavals for depressive users are then generated. Based on the post contents, weather, and news data, relevant events are detected to infer the reasons of the negative emotions. A correlation analysis between the behavioral data of the depressive users on Facebook and their negative emotions is also conducted. The results of this study can not only provide a self-examination tool for depressive people, but also serve as a diagnostic assessment reference for medical personnel.

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Cited By

Burgess EReddy MMohr D(2022)"I Just Can't Help But Smile Sometimes": Collaborative Self-Management of DepressionProceedings of the ACM on Human-Computer Interaction10.1145/35129176:CSCW1(1-32)Online publication date: 7-Apr-2022
https://dl.acm.org/doi/10.1145/3512917
Gooding PKariotis T(2021)Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping ReviewJMIR Mental Health10.2196/246688:6(e24668)Online publication date: 10-Jun-2021
https://doi.org/10.2196/24668
Tai LSetyonugroho WChen A(2020)Finding discriminatory features from electronic health records for depression predictionJournal of Intelligent Information Systems10.1007/s10844-020-00611-y55:2(371-396)Online publication date: 24-Jul-2020
https://doi.org/10.1007/s10844-020-00611-y

Index Terms

Event detection for exploring emotional upheavals of depressive people
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

April 2019

2682 pages

ISBN:9781450359337

DOI:10.1145/3297280

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University, Marietta, Georgia
,
George A. Papadopoulos
University of Cyprus, Nicosia, Cyprus

Copyright © 2019 ACM.

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Published: 08 April 2019

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SAC '19: The 34th ACM/SIGAPP Symposium on Applied Computing

April 8 - 12, 2019

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Cited By

Burgess EReddy MMohr D(2022)"I Just Can't Help But Smile Sometimes": Collaborative Self-Management of DepressionProceedings of the ACM on Human-Computer Interaction10.1145/35129176:CSCW1(1-32)Online publication date: 7-Apr-2022
https://dl.acm.org/doi/10.1145/3512917
Gooding PKariotis T(2021)Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping ReviewJMIR Mental Health10.2196/246688:6(e24668)Online publication date: 10-Jun-2021
https://doi.org/10.2196/24668
Tai LSetyonugroho WChen A(2020)Finding discriminatory features from electronic health records for depression predictionJournal of Intelligent Information Systems10.1007/s10844-020-00611-y55:2(371-396)Online publication date: 24-Jul-2020
https://doi.org/10.1007/s10844-020-00611-y

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