Factuality Checking in News Headlines with Eye Tracking

Autor:	Hansen, Christian, Hansen, Casper, Simonsen, Jakob Grue, Larsen, Birger, Alstrup, Stephen, Lioma, Christina
Rok vydání:	2020
Předmět:	Computer Science - Human-Computer Interaction Computer Science - Information Retrieval
Druh dokumentu:	Working Paper
DOI:	10.1145/3397271.3401221
Popis:	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. Comment: Accepted to SIGIR 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2006.09736 Zobrazit plný text záznamu View this record from Arxiv