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.
Toward machine emotional intelligence: analysis of affective physiological state
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A unified inference-time augmentation framework using 13 methods with Bayesian optimization improves AUROC up to 8.5% and AUPRC up to 10.6% for PPG AF detection on five datasets with over 400 patients.
Transfer learning from an arachnophobia dataset yields 86% accuracy classifying PTSD status and 17% MAPE estimating severity via HR/GSR signals and MKDE in a 21-person military cohort.
citing papers explorer
-
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.