Factuality Checking in News Headlines with Eye Tracking

06/17/2020
by   Christian Hansen, et al.
0

We study whether it is possible to infer if a news headline is true or false using only the movement of the human eyes when reading news headlines. Our study with 55 participants who are eye-tracked when reading 108 news headlines (72 true, 36 false) shows that false headlines receive statistically significantly less visual attention than true headlines. We further build an ensemble learner that predicts news headline factuality using only eye-tracking measurements. Our model yields a mean AUC of 0.688 and is better at detecting false than true headlines. Through a model analysis, we find that eye-tracking 25 users when reading 3-6 headlines is sufficient for our ensemble learner.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/16/2023

True or false? Cognitive load when reading COVID-19 news headlines: an eye-tracking study

Misinformation is an important topic in the Information Retrieval (IR) c...
research
05/07/2018

Relating Eye-Tracking Measures With Changes In Knowledge on Search Tasks

We conducted an eye-tracking study where 30 participants performed searc...
research
02/17/2022

The Effects of Interactive AI Design on User Behavior: An Eye-tracking Study of Fact-checking COVID-19 Claims

We conducted a lab-based eye-tracking study to investigate how the inter...
research
10/22/2021

FakeNewsLab: Experimental Study on Biases and Pitfalls Preventing us from Distinguishing True from False News

Misinformation posting and spreading in Social Media is ignited by perso...
research
07/27/2021

Estudo Abordando o Contexto de Notícias Falsas em Países de Língua Portuguesa (Fake News)

This work consists of a study that addresses the context of false news i...
research
09/26/2019

Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation

Automatic news comment generation is beneficial for real applications bu...
research
01/15/2020

Relevance Prediction from Eye-movements Using Semi-interpretable Convolutional Neural Networks

We propose an image-classification method to predict the perceived-relev...

Please sign up or login with your details

Forgot password? Click here to reset