Personality is Revealed During Weekends: Towards Data Minimisation for Smartphone Based Personality Classification
July 26, 2019 Β· Declared Dead Β· π IFIP TC13 International Conference on Human-Computer Interaction
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Mohammed Khwaja, Aleksandar Matic
arXiv ID
1907.11498
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.CY
Citations
2
Venue
IFIP TC13 International Conference on Human-Computer Interaction
Last Checked
4 months ago
Abstract
Previous literature has explored automatic personality modelling using smartphone data for its potential to personalise mobile services. Although passive modelling of personality removes the burden of completing lengthy questionnaires, the fact that such models typically require a few weeks or months of personal data can negatively impact user's engagement. In this study, we explore the feasibility of reducing the duration of data collection in the context of personality classification. We found that only one or two weekends can suffice for achieving state-of-the-art accuracy between 66% and 71% for classifying the five personality traits. These results provide lessons for practicing "data minimisation" - a key principle of privacy laws.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted