Detecting Fake News Belief via Skin and Blood Flow Signals

TL;DR

This paper explores the use of wearable sensors to detect belief in fake news by analyzing physiological responses, achieving up to 67.83% accuracy, and introduces a new dataset for misinformation research.

Contribution

It introduces a novel dataset of physiological responses to misinformation and demonstrates machine learning models can detect belief and prior exposure using wearable sensor data.

Findings

Skin conductance outperforms PPG in detection accuracy

Best model achieved 67.83% accuracy in classifying belief

Physiological signals show promise for real-time misinformation detection

Abstract

Misinformation poses significant risks to public opinion, health, and security. While most fake news detection methods rely on text analysis, little is known about how people physically respond to false information or repeated exposure to the same statements. This study investigates whether wearable sensors can detect belief in a statement or prior exposure to it. We conducted a controlled experiment where participants evaluated statements while wearing an EmotiBit sensor that measured their skin conductance (electrodermal activity, EDA) and peripheral blood flow (photoplethysmography, PPG). From 28 participants, we collected a dataset of 672 trials, each labeled with whether the participant believed the statement and whether they had seen it before. This dataset introduces a new resource for studying physiological responses to misinformation. Using machine learning models, including KNN, CNN, and LightGBM, we analyzed these physiological patterns. The best-performing model achieved 67.83\% accuracy, with skin conductance outperforming PPG. These findings demonstrate the potential of wearable sensors as a minimally intrusive tool for detecting belief and prior exposure, offering new directions for real-time misinformation detection and adaptive, user-aware systems.