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A multilevel predictive model for detecting social network users with depression

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

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages130-135
Number of pages6
ISBN (Electronic)9781538653777
DOIs
Publication statusPublished - 24 Jul 2018
Event6th IEEE International Conference on Healthcare Informatics, ICHI 2018 - New York, United States
Duration: 4 Jun 20187 Jun 2018

Conference

Conference6th IEEE International Conference on Healthcare Informatics, ICHI 2018
CountryUnited States
CityNew York
Period4/06/20187/06/2018

King's Authors

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.

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