Truth and Trust: Fake News Detection via Biosignals

TL;DR

This study investigates the use of physiological signals, specifically EDA and PPG, to detect the truthfulness of information and its interaction with user belief, highlighting the potential and challenges of physiological-based fake news detection.

Contribution

It introduces a novel approach using biosignals for classifying truth and belief interactions, providing benchmark results and emphasizing the need for multimodal detection methods.

Findings

EDA outperforms PPG in truth detection

Performance drops in joint belief-veracity classification

Physiological signals reflect basic truth perception

Abstract

Understanding how individuals physiologically respond to false information is crucial for advancing misinformation detection systems. This study explores the potential of using physiological signals, specifically electrodermal activity (EDA) and photoplethysmography (PPG), to classify both the veracity of information and its interaction with user belief. In a controlled laboratory experiment, we collected EDA and PPG signals while participants evaluated the truthfulness of climate-related claims. Each trial was labeled based on the objective truth of the claim and the participant's belief, enabling two classification tasks: binary veracity detection and a novel four-class joint belief-veracity classification. We extracted handcrafted features from the raw signals and trained several machine learning models to benchmark the dataset. Our results show that EDA outperforms PPG, indicating its greater sensitivity to physiological responses related to truth perception. However, performance significantly drops in the joint belief-veracity classification task, highlighting the complexity of modeling the interaction between belief and truth. These findings suggest that while physiological signals can reflect basic truth perception, accurately modeling the intricate relationships between belief and veracity remains a significant challenge. This study emphasizes the importance of multimodal approaches that incorporate psychological, physiological, and cognitive factors to improve fake news detection systems. Our work provides a foundation for future research aimed at enhancing misinformation detection via addressing the complexities of human belief and truth processing.