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

TL;DR

This paper addresses the challenge of detecting selective exposure to online news in real time to counteract filter bubbles. It proposes a multimodal approach that combines eye-tracking and EEG to capture attentional and neural responses as readers encounter attitude-congruent versus attitude-discrepant content, with a focus on theta-band synchronization in the parietal region in the range of $5-8$ Hz as a potential marker. The authors outline metrics (gaze fixation patterns, fixation durations, ERP components such as N400) and a data-collection pipeline using synchronized streams (eye data, EEG, mouse/keyboard) to support real-time classification. An experimental plan with 40–60 participants seeks to validate these markers and inform bias-aware interfaces that can present alternative viewpoints in real time. If successful, this work contributes to AI-enhanced methodology for bias detection by enabling live monitoring of selective exposure in dynamic information environments.

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.

Figures (1)

• Figure 1: Process to collect different input streams to time-synchronized data and to compute sentence fixation times for the segmentation of EEG data and data annotation.