Intention-use gaps and displacement of valued activities predict social media regret more strongly than duration, with pre-session context generalizing across users and physiological signals adding person-specific predictive power.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
On WESAD respiratory data under leave-one-subject-out validation, raw 1D-CNNs reach 96.72% accuracy for stress-vs-rest while grouped respiratory signatures yield higher MCC for baseline (65.34%), amusement (35.69%), and meditation (88.65%).
citing papers explorer
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Before You Scroll Again: Predicting Regretful Social Media Sessions from In-the-Wild Contextual and Wearable Sensing
Intention-use gaps and displacement of valued activities predict social media regret more strongly than duration, with pre-session context generalizing across users and physiological signals adding person-specific predictive power.
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State-Specific Respiratory Signatures for Affective and Stress Recognition: Interpretable Respiratory Markers, Autocorrelation Lags, and Compact CNN Models
On WESAD respiratory data under leave-one-subject-out validation, raw 1D-CNNs reach 96.72% accuracy for stress-vs-rest while grouped respiratory signatures yield higher MCC for baseline (65.34%), amusement (35.69%), and meditation (88.65%).