Scaling psychosocial insights into social systems via computational methodologies

Ram, Rohit

Scaling psychosocial insights into social systems via computational methodologies

Ram, Rohit

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Publication Type:: Thesis
Issue Date:: 2025

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Field	Value	Language
dc.contributor.author	Ram, Rohit
dc.date.accessioned	2026-04-15T04:03:26Z
dc.date.available	2026-04-15T04:03:26Z
dc.date.issued	2025
dc.identifier.uri	http://hdl.handle.net/10453/194713
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	This thesis examines the complexity of human interactions and societal dynamics, focusing on the phenomena associated with modern communication technologies, i.e. social media. It emphasizes the role of computational methodologies, particularly machine learning and deep learning, to analyze social data. Motivated by challenges like misinformation and radicalisation on social media, the thesis advocates for computational solutions and new perspectives on approaching psychosocial research. The thesis aims to bridge the gap between sociological and computational approaches, asserting that computational methods can advance our understanding of psychological and sociological phenomena. The research covers measurement, modeling, profiling, tooling, and collaboration, exploring the synergy between computational methods and psychosocial practices. It includes measuring social influence, introducing the General Influence Model, profiling political ideologies, developing tools for practitioners, and sharing the author's experiences at the intersection of psychosocial and computational disciplines. The goal is to demonstrate how computational methods offer a promising avenue for addressing enduring societal challenges and improving our comprehension of human behavior.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/194713/1/thesis.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2025 Rohit Ram
dc.rights	au.edu.uts.lib/cph
dc.title	Scaling psychosocial insights into social systems via computational methodologies	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

This thesis examines the complexity of human interactions and societal dynamics, focusing on the phenomena associated with modern communication technologies, i.e. social media. It emphasizes the role of computational methodologies, particularly machine learning and deep learning, to analyze social data. Motivated by challenges like misinformation and radicalisation on social media, the thesis advocates for computational solutions and new perspectives on approaching psychosocial research. The thesis aims to bridge the gap between sociological and computational approaches, asserting that computational methods can advance our understanding of psychological and sociological phenomena. The research covers measurement, modeling, profiling, tooling, and collaboration, exploring the synergy between computational methods and psychosocial practices. It includes measuring social influence, introducing the General Influence Model, profiling political ideologies, developing tools for practitioners, and sharing the author's experiences at the intersection of psychosocial and computational disciplines. The goal is to demonstrate how computational methods offer a promising avenue for addressing enduring societal challenges and improving our comprehension of human behavior.

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http://hdl.handle.net/10453/194713