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A multilevel predictive model for detecting social network users with depression

Research output: Chapter in Book/Report/Conference proceedingConference paper

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A multilevel predictive model for detecting social network users with depression. / Wongkoblap, Akkapon; Vadillo, Miguel A.; Curcin, Vasa.

Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 130-135 8419355.

Research output: Chapter in Book/Report/Conference proceedingConference paper

Harvard

Wongkoblap, A, Vadillo, MA & Curcin, V 2018, A multilevel predictive model for detecting social network users with depression. in Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018., 8419355, Institute of Electrical and Electronics Engineers Inc., pp. 130-135, 6th IEEE International Conference on Healthcare Informatics, ICHI 2018, New York, United States, 4/06/2018. https://doi.org/10.1109/ICHI.2018.00022

APA

Wongkoblap, A., Vadillo, M. A., & Curcin, V. (2018). A multilevel predictive model for detecting social network users with depression. In Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018 (pp. 130-135). [8419355] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI.2018.00022

Vancouver

Wongkoblap A, Vadillo MA, Curcin V. A multilevel predictive model for detecting social network users with depression. In Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 130-135. 8419355 https://doi.org/10.1109/ICHI.2018.00022

Author

Wongkoblap, Akkapon ; Vadillo, Miguel A. ; Curcin, Vasa. / A multilevel predictive model for detecting social network users with depression. Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 130-135

Bibtex Download

@inbook{f544163fc61d42a582d8eb8f6e33448e,
title = "A multilevel predictive model for detecting social network users with depression",
abstract = "The society is currently witnessing an unprecedented growth in the incidence of mental disorders, with an estimated 300 million people suffering from depression globally. People with high life satisfaction tend to suffer fewer mental health issues. The large volume of data generated on social network platforms enables us to detect hidden patterns in data and obtain new insights. This work aims to (a) explore the relationship between life satisfaction and depression in social network users, using Facebook as an example, and (b) develop a multilevel predictive model to detect users with depression. We trained a set of predictive models on datasets from myPersonality project including 2,085 participants who took the Satisfaction with Life Scale and 614 users who submitted the Centre for Epidemiological Study Depression (CES-D) scale. The resulting multilevel model establishes a negative correlation between life satisfaction and depression, and it can also improve the accuracy of a predictive model using depressive labels alone.",
keywords = "Depression, Life Satisfaction, Machine Learning, Mental Health, Predictive Model, Social Network",
author = "Akkapon Wongkoblap and Vadillo, {Miguel A.} and Vasa Curcin",
year = "2018",
month = "7",
day = "24",
doi = "10.1109/ICHI.2018.00022",
language = "English",
pages = "130--135",
booktitle = "Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

RIS (suitable for import to EndNote) Download

TY - CHAP

T1 - A multilevel predictive model for detecting social network users with depression

AU - Wongkoblap, Akkapon

AU - Vadillo, Miguel A.

AU - Curcin, Vasa

PY - 2018/7/24

Y1 - 2018/7/24

N2 - The society is currently witnessing an unprecedented growth in the incidence of mental disorders, with an estimated 300 million people suffering from depression globally. People with high life satisfaction tend to suffer fewer mental health issues. The large volume of data generated on social network platforms enables us to detect hidden patterns in data and obtain new insights. This work aims to (a) explore the relationship between life satisfaction and depression in social network users, using Facebook as an example, and (b) develop a multilevel predictive model to detect users with depression. We trained a set of predictive models on datasets from myPersonality project including 2,085 participants who took the Satisfaction with Life Scale and 614 users who submitted the Centre for Epidemiological Study Depression (CES-D) scale. The resulting multilevel model establishes a negative correlation between life satisfaction and depression, and it can also improve the accuracy of a predictive model using depressive labels alone.

AB - The society is currently witnessing an unprecedented growth in the incidence of mental disorders, with an estimated 300 million people suffering from depression globally. People with high life satisfaction tend to suffer fewer mental health issues. The large volume of data generated on social network platforms enables us to detect hidden patterns in data and obtain new insights. This work aims to (a) explore the relationship between life satisfaction and depression in social network users, using Facebook as an example, and (b) develop a multilevel predictive model to detect users with depression. We trained a set of predictive models on datasets from myPersonality project including 2,085 participants who took the Satisfaction with Life Scale and 614 users who submitted the Centre for Epidemiological Study Depression (CES-D) scale. The resulting multilevel model establishes a negative correlation between life satisfaction and depression, and it can also improve the accuracy of a predictive model using depressive labels alone.

KW - Depression

KW - Life Satisfaction

KW - Machine Learning

KW - Mental Health

KW - Predictive Model

KW - Social Network

UR - http://www.scopus.com/inward/record.url?scp=85051106897&partnerID=8YFLogxK

U2 - 10.1109/ICHI.2018.00022

DO - 10.1109/ICHI.2018.00022

M3 - Conference paper

AN - SCOPUS:85051106897

SP - 130

EP - 135

BT - Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

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