Filtered States: Active Inference, Social Media and Mental Health

Ben White; Mark Miller

doi:10.1007/978-3-030-93736-2_54

Filtered States: Active Inference, Social Media and Mental Health

Ben White
, Mark Miller

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

1 Citation (Scopus)

Abstract

Social media is implicated today in an array of mental health concerns. While worries around social media have become mainstream, little is known about the specific cognitive mechanisms underlying the correlations seen in these studies, or why we find it so hard to stop engaging with these platforms when things obviously begin to deteriorate for us. New advances in computational neuroscience are now perfectly poised to shed light on this matter. In this paper we approach these concerns around social media and mental health issues, including the troubling rise in Snapchat surgeries, depression and addiction, through the lens of the Active Inference Framework (AIF).

Original language	English
Title of host publication	Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Proceedings
Publisher	Springer
Pages	772-783
Number of pages	12
ISBN (Print)	9783030937355
DOIs	https://doi.org/10.1007/978-3-030-93736-2_54
Publication status	Published - 2021
Externally published	Yes
Event	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2021 - Virtual, Online Duration: 13 Sept 2021 → 17 Sept 2021 Conference number: 21st

Publication series

Name	Communications in Computer and Information Science
Volume	1524 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2021
Abbreviated title	ECML PKDD 2021
City	Virtual, Online
Period	13/09/21 → 17/09/21

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Active inference
Addiction
Depression
Social media

Access to Document

10.1007/978-3-030-93736-2_54

Cite this

White, B., & Miller, M. (2021). Filtered States: Active Inference, Social Media and Mental Health. In Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Proceedings (pp. 772-783). (Communications in Computer and Information Science; Vol. 1524 CCIS). Springer. https://doi.org/10.1007/978-3-030-93736-2_54

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abstract = "Social media is implicated today in an array of mental health concerns. While worries around social media have become mainstream, little is known about the specific cognitive mechanisms underlying the correlations seen in these studies, or why we find it so hard to stop engaging with these platforms when things obviously begin to deteriorate for us. New advances in computational neuroscience are now perfectly poised to shed light on this matter. In this paper we approach these concerns around social media and mental health issues, including the troubling rise in Snapchat surgeries, depression and addiction, through the lens of the Active Inference Framework (AIF).",

keywords = "Active inference, Addiction, Depression, Social media",

author = "Ben White and Mark Miller",

note = "Funding Information: Acknowledgments. Mark Miller carried out this work with the support of Horizon 2020 European Union ERC Advanced Grant XSPECT - DLV-692739. Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2021, ECML PKDD 2021 ; Conference date: 13-09-2021 Through 17-09-2021",

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language = "English",

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White, B & Miller, M 2021, Filtered States: Active Inference, Social Media and Mental Health. in Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Proceedings. Communications in Computer and Information Science, vol. 1524 CCIS, Springer, pp. 772-783, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2021, Virtual, Online, 13/09/21. https://doi.org/10.1007/978-3-030-93736-2_54

Filtered States: Active Inference, Social Media and Mental Health. / White, Ben; Miller, Mark.
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Proceedings. Springer, 2021. p. 772-783 (Communications in Computer and Information Science; Vol. 1524 CCIS).

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

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