Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744373
Title: Assessment of individual differences in online social networks using machine learning
Author: Idani, Arman
ISNI:       0000 0004 7225 507X
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2017
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Abstract:
The services that define our personal and professional lives are increasingly accessed through digital devices, which store extensive records of our behaviour. An individual's psychological profile can be accurately assessed using offline behaviour, and I investigate if an automated machine learning system can measure the same psychological factors, only from observing the footprints of online behaviour, without observing any offline behaviour or any direct input from the individual. Prior research shows that psychological traits such as personality can be predicted using these digital footprints, although current state-of-the-art accuracy is below psychometric standards of reliability and self-reports consistently outperform machine-ratings in external validity. I introduce a new machine learning system that is capable of doing five-factor personality assessments, as well as other psychological assessments, from online data as accurately as self-report questionnaires in terms of reliability, internal consistency and external and discriminant validity, and demonstrate that passive psychological assessment can be a realistic option in addition to self-report questionnaires for both research and practice. Achieving this goal is not possible using conventional dimensionality reduction and linear regression models. Here I develop a supervised dimensionality reduction method capable of intelligently selecting only useful parts of data for the relevant prediction at hand which also does not lose variance when eliminating redundancies. In the learning stage, instead of linear regression models, I use an ensemble of decision trees which are able to distinguish scenarios where the same observations on digital data can mean different things for different individuals. This work highlights the interesting idea that similar to how a human expert who is able to assess personality from offline behaviour, an expert machine learning system is able to assess personality from online behaviour. It also demonstrates that big-5 personality are predictors of how predictable users are in social media, with neuroticism having the greatest correlation with unpredictability, while openness having the greatest correlation with predictability.
Supervisor: Rust, John ; Kohli, Pushmeet Sponsor: Microsoft Research
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.744373  DOI:
Keywords: machine learning ; personality ; big 5 ; artificial intelligence ; psychometrics ; psychological assessment ; passive psychometrics ; deep learning
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