Beyond Risk: Individual Mental Health Trajectories from Large-Scale Social Media Data

Johan Bollen; Marijn ten Thij; Lorenzo Lorenzo-Luaces; Lauren A. Rutter

doi:10.1007/978-3-031-04431-1_12

Beyond Risk: Individual Mental Health Trajectories from Large-Scale Social Media Data

Johan Bollen^*, Marijn ten Thij, Lorenzo Lorenzo-Luaces, Lauren A. Rutter

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic

Abstract

Depression and anxiety are now the 1st and 10th leading causes of disability worldwide. However, their variegated presentation and symptoms complicate efforts to develop a better understanding of the complex factors that shape the dynamics of their development within individuals. The development of personalized detection, diagnostics, and treatment options has been hindered by the lack of within-subject longitudinal observations at high temporal resolution and for large samples of individuals across the spectrum of internalizing disorders. Here, we discuss our efforts in the burgeoning field of precision mental health which leverages large-scale data of the behavioral, cognitive, emotional and social traces that billions of individuals leave behind when they interact with social media platforms. Our results point towards the possibility of modeling individualized mental health trajectories at population scale to identify high-precision targets for the detection, intervention, and mitigation of internalizing disorders.

Original language	English
Title of host publication	Early Detection of Mental Health Disorders by Social Media Monitoring: The First Five Years of the eRisk Project
Editors	Fabio Crestani, David E. Losada, Javier Parapar
Place of Publication	Cham
Publisher	Springer International Publishing
Pages	265-287
Number of pages	23
Volume	1018
ISBN (Print)	978-3-031-04431-1
DOIs	https://doi.org/10.1007/978-3-031-04431-1_12
Publication status	Published - 2022

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10.1007/978-3-031-04431-1_12

https://doi.org/10.1007/978-3-031-04431-1_12

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Bollen, J., ten Thij, M., Lorenzo-Luaces, L., & Rutter, L. A. (2022). Beyond Risk: Individual Mental Health Trajectories from Large-Scale Social Media Data. In F. Crestani, D. E. Losada, & J. Parapar (Eds.), Early Detection of Mental Health Disorders by Social Media Monitoring: The First Five Years of the eRisk Project (Vol. 1018, pp. 265-287). Springer International Publishing. https://doi.org/10.1007/978-3-031-04431-1_12

Bollen, Johan ; ten Thij, Marijn ; Lorenzo-Luaces, Lorenzo et al. / Beyond Risk: Individual Mental Health Trajectories from Large-Scale Social Media Data. Early Detection of Mental Health Disorders by Social Media Monitoring: The First Five Years of the eRisk Project. editor / Fabio Crestani ; David E. Losada ; Javier Parapar. Vol. 1018 Cham : Springer International Publishing, 2022. pp. 265-287

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abstract = "Depression and anxiety are now the 1st and 10th leading causes of disability worldwide. However, their variegated presentation and symptoms complicate efforts to develop a better understanding of the complex factors that shape the dynamics of their development within individuals. The development of personalized detection, diagnostics, and treatment options has been hindered by the lack of within-subject longitudinal observations at high temporal resolution and for large samples of individuals across the spectrum of internalizing disorders. Here, we discuss our efforts in the burgeoning field of precision mental health which leverages large-scale data of the behavioral, cognitive, emotional and social traces that billions of individuals leave behind when they interact with social media platforms. Our results point towards the possibility of modeling individualized mental health trajectories at population scale to identify high-precision targets for the detection, intervention, and mitigation of internalizing disorders.",

author = "Johan Bollen and {ten Thij}, Marijn and Lorenzo Lorenzo-Luaces and Rutter, {Lauren A.}",

note = "Funding Information: Acknowledgements Johan Bollen is grateful for support from the Urban Mental Health Institute of the University of Amsterdam, Wageningen University and Research, the NSF (NSF SMASBE #1636636), and the IU Vice Provost for COVID-19 Research. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.",

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Bollen, J, ten Thij, M, Lorenzo-Luaces, L & Rutter, LA 2022, Beyond Risk: Individual Mental Health Trajectories from Large-Scale Social Media Data. in F Crestani, DE Losada & J Parapar (eds), Early Detection of Mental Health Disorders by Social Media Monitoring: The First Five Years of the eRisk Project. vol. 1018, Springer International Publishing, Cham, pp. 265-287. https://doi.org/10.1007/978-3-031-04431-1_12

Beyond Risk: Individual Mental Health Trajectories from Large-Scale Social Media Data. / Bollen, Johan; ten Thij, Marijn; Lorenzo-Luaces, Lorenzo et al.
Early Detection of Mental Health Disorders by Social Media Monitoring: The First Five Years of the eRisk Project. ed. / Fabio Crestani; David E. Losada; Javier Parapar. Vol. 1018 Cham: Springer International Publishing, 2022. p. 265-287.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic

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AB - Depression and anxiety are now the 1st and 10th leading causes of disability worldwide. However, their variegated presentation and symptoms complicate efforts to develop a better understanding of the complex factors that shape the dynamics of their development within individuals. The development of personalized detection, diagnostics, and treatment options has been hindered by the lack of within-subject longitudinal observations at high temporal resolution and for large samples of individuals across the spectrum of internalizing disorders. Here, we discuss our efforts in the burgeoning field of precision mental health which leverages large-scale data of the behavioral, cognitive, emotional and social traces that billions of individuals leave behind when they interact with social media platforms. Our results point towards the possibility of modeling individualized mental health trajectories at population scale to identify high-precision targets for the detection, intervention, and mitigation of internalizing disorders.

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Bollen J, ten Thij M, Lorenzo-Luaces L, Rutter LA. Beyond Risk: Individual Mental Health Trajectories from Large-Scale Social Media Data. In Crestani F, Losada DE, Parapar J, editors, Early Detection of Mental Health Disorders by Social Media Monitoring: The First Five Years of the eRisk Project. Vol. 1018. Cham: Springer International Publishing. 2022. p. 265-287 doi: 10.1007/978-3-031-04431-1_12

Beyond Risk: Individual Mental Health Trajectories from Large-Scale Social Media Data

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