Multimodal news analytics using measures of cross-modal entity and context consistency

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Müller-Budack, E.; Theiner, J.; Diering, S.; Idahl, M.; Hakimov, S. et al.: Multimodal news analytics using measures of cross-modal entity and context consistency. In: International Journal of Multimedia Information Retrieval 10 (2021), Nr. 2, S. 111-125. DOI: https://doi.org/10.1007/s13735-021-00207-4

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/12349

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Sum total of downloads: 18




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Abstract: 
The World Wide Web has become a popular source to gather information and news. Multimodal information, e.g., supplement text with photographs, is typically used to convey the news more effectively or to attract attention. The photographs can be decorative, depict additional details, but might also contain misleading information. The quantification of the cross-modal consistency of entity representations can assist human assessors’ evaluation of the overall multimodal message. In some cases such measures might give hints to detect fake news, which is an increasingly important topic in today’s society. In this paper, we present a multimodal approach to quantify the entity coherence between image and text in real-world news. Named entity linking is applied to extract persons, locations, and events from news texts. Several measures are suggested to calculate the cross-modal similarity of the entities in text and photograph by exploiting state-of-the-art computer vision approaches. In contrast to previous work, our system automatically acquires example data from the Web and is applicable to real-world news. Moreover, an approach that quantifies contextual image-text relations is introduced. The feasibility is demonstrated on two datasets that cover different languages, topics, and domains. © 2021, The Author(s).
License of this version: CC BY 4.0 Unported
Document Type: article
Publishing status: publishedVersion
Issue Date: 2021
Appears in Collections:Forschungszentren

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downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 9 50.00%
2 image of flag of United States United States 8 44.44%
3 image of flag of Morocco Morocco 1 5.56%

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