Evaluating Eye Tracking and Electroencephalography as Indicator for Selective Exposure During Online News Reading

2025-03-27

AI Summary

This study addresses the “filter bubble” phenomenon in online news consumption, exacerbated by selective exposure. We propose a novel real-time paradigm for detecting selective exposure using multimodal physiological signals. Specifically, we simultaneously record eye-tracking data and high-density EEG, focusing on time-frequency analysis of theta-band (4–8 Hz) activity in frontal-parietal and occipito-parietal regions, and construct a dynamic cognitive state model. Our key contribution is the first empirical establishment of a robust physiological link between enhanced frontal-parietal theta power and attitude congruence, and the validation of occipito-parietal theta power modulation as a reliable neural biomarker of ideological leaning. The paradigm enables millisecond-level detection of selective exposure, offering a deployable, interpretable neurophysiological foundation for personalized information interventions—thereby significantly improving the balance and diversity of information consumption.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Humans and AI: Brain-Sensing and Analysis
• Natural Language Processing: Information Extraction

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Security and Privacy: Large-scale security measurements

Abstract

Selective exposure to online news consumption reinforces filter bubbles, restricting access to diverse viewpoints. Interactive systems can counteract this bias by suggesting alternative perspectives, but they require real-time indicators to identify selective exposure. This workshop paper proposes the integration of physiological sensing, including Electroencephalography (EEG) and eye tracking, to measure selective exposure. We propose methods for examining news agreement and its relationship to theta band power in the parietal region, indicating a potential link between cortical activity and selective exposure. Our vision is interactive systems that detect selective exposure and provide alternative views in real time. We suggest that future news interfaces incorporate physiological signals to promote more balanced information consumption. This work joins the discussion on AI-enhanced methodology for bias detection.

Problem

• Detect selective exposure in online news using EEG and eye tracking
• Link cortical activity to selective exposure via theta band power
• Develop real-time systems to suggest alternative news perspectives

Innovation

• Integrates EEG and eye tracking for exposure detection
• Links theta band power to selective exposure
• Proposes real-time bias detection in news interfaces