Abstract

Most news outlets offer a multi-model user experience. Besides texts, readers encounter images, audio, video, and interactive elements. News Images strives to understand better how images affect news consumption. Participants gain access to a large-scale dataset of news articles and images. The task consists of two subtasks. Participants can engage in both or one of them. In the first sub-task, participants must predict with which images publishers paired given news articles. In the second subtask, participants must estimate the chance that users will pay attention to pairs of articles and images. This paper describes the settings in detail and draws connections to existing research.

@inproceedings{KilleEtAl:NewsImagesInMediaEval2021,
	author    = {Benjamin Kille and Andreas Lommatzsch and {\"O}zlem {\"O}zg{\"o}bek and Mehdi Elahi and Duc-Tien Dang-Nguyen},
	title     = {News Images in MediaEval 2021},
	booktitle = {Proceedings of the MediaEval Benchmarking Initiative for Multimedia Evaluation 2021},
	year      = {2021},
	issn      = {1613-0073},
	url       = {https://ceur-ws.org/Vol-3181/paper2.pdf},
	location  = {Online},
	publisher = {CEUR Workshop Proceedings},
	keywords  = {workshop, paper, multi-media}
}