Discovering latent affective dynamics among individuals in online mental health-related communities

Bo Dao; Thin Nguyen; Svetha Venkatesh; Dinh Phung

doi:10.1109/ICME.2016.7552947

Discovering latent affective dynamics among individuals in online mental health-related communities

Bo Dao, Thin Nguyen, Svetha Venkatesh, Dinh Phung

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

2 Citations (Scopus)

Abstract

Discovering dynamics of emotion and mood changes for individuals has the potential to enhance the diagnosis and treatment of mental disorders. In this paper we study affective transitions and dynamics among individuals in online mental health communities. Using social media as form of 'sensor', we crawl a large dataset of blogs posted by online communities whose descriptions declared to be associated with affective disorder conditions such as depression, anxiety, or autism. We then apply nonnegative matrix factorization model to extract the common and individual factors of affective transitions across groups of individuals in different levels of affective disorders. We examine the latent patterns of emotional transitions and investigate the effects of emotional transitions across the cohorts. Our framework is novel as it utilizes social media as an online sensing platform of mood and emotional dynamics. Hence, our work has implication in constructing systems to screen individuals and communities at high risks of mental health problems in online settings.

Original language	English
Title of host publication	2016 IEEE International Conference on Multimedia and Expo (ICME)
Subtitle of host publication	Seattle, USA \| July 11-15, 2016
Editors	Philip Chou, Anthony Vetro, Lap-Pui Chau, Jenq-Neng Hwang, Yung-Hsiang Lu, Max Mühlhäuser
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Number of pages	6
ISBN (Electronic)	9781467372589
ISBN (Print)	9781467372596
DOIs	https://doi.org/10.1109/ICME.2016.7552947
Publication status	Published - 2016
Externally published	Yes
Event	IEEE International Conference on Multimedia and Expo 2016 - Seattle, United States of America Duration: 11 Jul 2016 → 15 Jul 2016 Conference number: 9th https://signalprocessingsociety.org/blog/2016-9th-2016-ieee-international-conference-multimedia-and-expo-icme-2016 http://www.icme2016.org/

Conference

Conference	IEEE International Conference on Multimedia and Expo 2016
Abbreviated title	ICME 2016
Country/Territory	United States of America
City	Seattle
Period	11/07/16 → 15/07/16
Internet address	https://signalprocessingsociety.org/blog/2016-9th-2016-ieee-international-conference-multimedia-and-expo-icme-2016 http://www.icme2016.org/

Keywords

Affective Transition
Joint Factor Analysis
Mental Health
Nonnegative Matrix Factorization
Online Communities

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICME.2016.7552947

Cite this

Dao, B., Nguyen, T., Venkatesh, S., & Phung, D. (2016). Discovering latent affective dynamics among individuals in online mental health-related communities. In P. Chou, A. Vetro, L.-P. Chau, J.-N. Hwang, Y.-H. Lu, & M. Mühlhäuser (Eds.), 2016 IEEE International Conference on Multimedia and Expo (ICME): Seattle, USA | July 11-15, 2016 Article 7552947 IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICME.2016.7552947

Dao, Bo ; Nguyen, Thin ; Venkatesh, Svetha et al. / Discovering latent affective dynamics among individuals in online mental health-related communities. 2016 IEEE International Conference on Multimedia and Expo (ICME): Seattle, USA | July 11-15, 2016. editor / Philip Chou ; Anthony Vetro ; Lap-Pui Chau ; Jenq-Neng Hwang ; Yung-Hsiang Lu ; Max Mühlhäuser. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2016.

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title = "Discovering latent affective dynamics among individuals in online mental health-related communities",

abstract = "Discovering dynamics of emotion and mood changes for individuals has the potential to enhance the diagnosis and treatment of mental disorders. In this paper we study affective transitions and dynamics among individuals in online mental health communities. Using social media as form of 'sensor', we crawl a large dataset of blogs posted by online communities whose descriptions declared to be associated with affective disorder conditions such as depression, anxiety, or autism. We then apply nonnegative matrix factorization model to extract the common and individual factors of affective transitions across groups of individuals in different levels of affective disorders. We examine the latent patterns of emotional transitions and investigate the effects of emotional transitions across the cohorts. Our framework is novel as it utilizes social media as an online sensing platform of mood and emotional dynamics. Hence, our work has implication in constructing systems to screen individuals and communities at high risks of mental health problems in online settings.",

keywords = "Affective Transition, Joint Factor Analysis, Mental Health, Nonnegative Matrix Factorization, Online Communities",

author = "Bo Dao and Thin Nguyen and Svetha Venkatesh and Dinh Phung",

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booktitle = "2016 IEEE International Conference on Multimedia and Expo (ICME)",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

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note = "IEEE International Conference on Multimedia and Expo 2016, ICME 2016 ; Conference date: 11-07-2016 Through 15-07-2016",

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Dao, B, Nguyen, T, Venkatesh, S & Phung, D 2016, Discovering latent affective dynamics among individuals in online mental health-related communities. in P Chou, A Vetro, L-P Chau, J-N Hwang, Y-H Lu & M Mühlhäuser (eds), 2016 IEEE International Conference on Multimedia and Expo (ICME): Seattle, USA | July 11-15, 2016., 7552947, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, IEEE International Conference on Multimedia and Expo 2016, Seattle, Washington, United States of America, 11/07/16. https://doi.org/10.1109/ICME.2016.7552947

Discovering latent affective dynamics among individuals in online mental health-related communities. / Dao, Bo; Nguyen, Thin; Venkatesh, Svetha et al.
2016 IEEE International Conference on Multimedia and Expo (ICME): Seattle, USA | July 11-15, 2016. ed. / Philip Chou; Anthony Vetro; Lap-Pui Chau; Jenq-Neng Hwang; Yung-Hsiang Lu; Max Mühlhäuser. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2016. 7552947.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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T1 - Discovering latent affective dynamics among individuals in online mental health-related communities

AU - Dao, Bo

AU - Nguyen, Thin

AU - Venkatesh, Svetha

AU - Phung, Dinh

N1 - Conference code: 9th

PY - 2016

Y1 - 2016

N2 - Discovering dynamics of emotion and mood changes for individuals has the potential to enhance the diagnosis and treatment of mental disorders. In this paper we study affective transitions and dynamics among individuals in online mental health communities. Using social media as form of 'sensor', we crawl a large dataset of blogs posted by online communities whose descriptions declared to be associated with affective disorder conditions such as depression, anxiety, or autism. We then apply nonnegative matrix factorization model to extract the common and individual factors of affective transitions across groups of individuals in different levels of affective disorders. We examine the latent patterns of emotional transitions and investigate the effects of emotional transitions across the cohorts. Our framework is novel as it utilizes social media as an online sensing platform of mood and emotional dynamics. Hence, our work has implication in constructing systems to screen individuals and communities at high risks of mental health problems in online settings.

AB - Discovering dynamics of emotion and mood changes for individuals has the potential to enhance the diagnosis and treatment of mental disorders. In this paper we study affective transitions and dynamics among individuals in online mental health communities. Using social media as form of 'sensor', we crawl a large dataset of blogs posted by online communities whose descriptions declared to be associated with affective disorder conditions such as depression, anxiety, or autism. We then apply nonnegative matrix factorization model to extract the common and individual factors of affective transitions across groups of individuals in different levels of affective disorders. We examine the latent patterns of emotional transitions and investigate the effects of emotional transitions across the cohorts. Our framework is novel as it utilizes social media as an online sensing platform of mood and emotional dynamics. Hence, our work has implication in constructing systems to screen individuals and communities at high risks of mental health problems in online settings.

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KW - Joint Factor Analysis

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KW - Nonnegative Matrix Factorization

KW - Online Communities

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M3 - Conference Paper

AN - SCOPUS:84987621951

SN - 9781467372596

BT - 2016 IEEE International Conference on Multimedia and Expo (ICME)

A2 - Chou, Philip

A2 - Vetro, Anthony

A2 - Chau, Lap-Pui

A2 - Hwang, Jenq-Neng

A2 - Lu, Yung-Hsiang

A2 - Mühlhäuser, Max

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

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ER -

Dao B, Nguyen T, Venkatesh S, Phung D. Discovering latent affective dynamics among individuals in online mental health-related communities. In Chou P, Vetro A, Chau LP, Hwang JN, Lu YH, Mühlhäuser M, editors, 2016 IEEE International Conference on Multimedia and Expo (ICME): Seattle, USA | July 11-15, 2016. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2016. 7552947 doi: 10.1109/ICME.2016.7552947

Discovering latent affective dynamics among individuals in online mental health-related communities

Abstract

Conference

Keywords

UN SDGs

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