@inproceedings{sung-etal-2023-fake,
title = "Not all Fake News is Written: A Dataset and Analysis of Misleading Video Headlines",
author = "Sung, Yoo and
Boyd-Graber, Jordan and
Hassan, Naeemul",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.1010",
doi = "10.18653/v1/2023.emnlp-main.1010",
pages = "16241--16258",
abstract = "Polarization and the marketplace for impressions have conspired to make navigating information online difficult for users, and while there has been a significant effort to detect false or misleading text, multimodal datasets have received considerably less attention. To complement existing resources, we present multimodal Video Misleading Headline (VMH), a dataset that consists of videos and whether annotators believe the headline is representative of the video{'}s contents. After collecting and annotating this dataset, we analyze multimodal baselines for detecting misleading headlines. Our annotation process also focuses on why annotators view a video as misleading, allowing us to better understand the interplay of annotators{'} background and the content of the videos.",
}
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%0 Conference Proceedings
%T Not all Fake News is Written: A Dataset and Analysis of Misleading Video Headlines
%A Sung, Yoo
%A Boyd-Graber, Jordan
%A Hassan, Naeemul
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F sung-etal-2023-fake
%X Polarization and the marketplace for impressions have conspired to make navigating information online difficult for users, and while there has been a significant effort to detect false or misleading text, multimodal datasets have received considerably less attention. To complement existing resources, we present multimodal Video Misleading Headline (VMH), a dataset that consists of videos and whether annotators believe the headline is representative of the video’s contents. After collecting and annotating this dataset, we analyze multimodal baselines for detecting misleading headlines. Our annotation process also focuses on why annotators view a video as misleading, allowing us to better understand the interplay of annotators’ background and the content of the videos.
%R 10.18653/v1/2023.emnlp-main.1010
%U https://aclanthology.org/2023.emnlp-main.1010
%U https://doi.org/10.18653/v1/2023.emnlp-main.1010
%P 16241-16258
Markdown (Informal)
[Not all Fake News is Written: A Dataset and Analysis of Misleading Video Headlines](https://aclanthology.org/2023.emnlp-main.1010) (Sung et al., EMNLP 2023)
ACL