Factuality Checking in News Headlines with Eye Tracking

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Standard

Factuality Checking in News Headlines with Eye Tracking. / Hansen, Christian; Hansen, Casper; Simonsen, Jakob Grue; Larsen, Birger; Alstrup, Stephen; Lioma, Christina.

SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, 2020. s. 2013-2016.

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Harvard

Hansen, C, Hansen, C, Simonsen, JG, Larsen, B, Alstrup, S & Lioma, C 2020, Factuality Checking in News Headlines with Eye Tracking. i SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, s. 2013-2016, 43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020, Virtual, Online, Kina, 25/07/2020. https://doi.org/10.1145/3397271.3401221

APA

Hansen, C., Hansen, C., Simonsen, J. G., Larsen, B., Alstrup, S., & Lioma, C. (2020). Factuality Checking in News Headlines with Eye Tracking. I SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (s. 2013-2016). Association for Computing Machinery. https://doi.org/10.1145/3397271.3401221

Vancouver

Hansen C, Hansen C, Simonsen JG, Larsen B, Alstrup S, Lioma C. Factuality Checking in News Headlines with Eye Tracking. I SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery. 2020. s. 2013-2016 https://doi.org/10.1145/3397271.3401221

Author

Hansen, Christian ; Hansen, Casper ; Simonsen, Jakob Grue ; Larsen, Birger ; Alstrup, Stephen ; Lioma, Christina. / Factuality Checking in News Headlines with Eye Tracking. SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, 2020. s. 2013-2016

Bibtex

@inproceedings{007537ed41d948a8a03c75ad9030e756,
title = "Factuality Checking in News Headlines with Eye Tracking",
abstract = "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.",
keywords = "eye tracking, factuality checking, fake news",
author = "Christian Hansen and Casper Hansen and Simonsen, {Jakob Grue} and Birger Larsen and Stephen Alstrup and Christina Lioma",
year = "2020",
doi = "10.1145/3397271.3401221",
language = "English",
pages = "2013--2016",
booktitle = "SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval",
publisher = "Association for Computing Machinery",
note = "43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020 ; Conference date: 25-07-2020 Through 30-07-2020",

}

RIS

TY - GEN

T1 - Factuality Checking in News Headlines with Eye Tracking

AU - Hansen, Christian

AU - Hansen, Casper

AU - Simonsen, Jakob Grue

AU - Larsen, Birger

AU - Alstrup, Stephen

AU - Lioma, Christina

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - eye tracking

KW - factuality checking

KW - fake news

UR - http://www.scopus.com/inward/record.url?scp=85090120754&partnerID=8YFLogxK

U2 - 10.1145/3397271.3401221

DO - 10.1145/3397271.3401221

M3 - Article in proceedings

AN - SCOPUS:85090120754

SP - 2013

EP - 2016

BT - SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

PB - Association for Computing Machinery

T2 - 43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020

Y2 - 25 July 2020 through 30 July 2020

ER -

ID: 260413700