Revealing the hidden patterns of news photos: Analysis of millions of news photos through GDELT & deep learning-based vision APIs

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2 Citations (Scopus)

Abstract

In this work, we analyze more than two million news photos published in January 2016. We demonstrate i) which objects appear the most in news photos; ii) what the sentiments of news photos are; iii) whether the sentiment of news photos is aligned with the tone of the text; iv) how gender is treated; and v) how differently political candidates are portrayed. To our best knowledge, this is the first large-scale study of news photo contents using deep learning-based vision APIs.

Original languageEnglish
Title of host publicationWS-16-16
Subtitle of host publicationCitiLab; WS-16-17: Wiki; WS-16-18: News and Public Opinion; WS-16-19: Social Media in the Newroom; WS-16-20: Social Web for Envioronmental and Ecological Monitoring
PublisherAI Access Foundation
Pages99-107
Number of pages9
VolumeWS-16-16 - WS-16-20
ISBN (Electronic)9781577357681
Publication statusPublished - 1 Jan 2016
Event10th International AAAI Conference on Web and Social Media, ICWSM 2016 - Cologne, Germany
Duration: 17 May 201620 May 2016

Other

Other10th International AAAI Conference on Web and Social Media, ICWSM 2016
CountryGermany
CityCologne
Period17/5/1620/5/16

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Kwak, H., & An, J. (2016). Revealing the hidden patterns of news photos: Analysis of millions of news photos through GDELT & deep learning-based vision APIs. In WS-16-16: CitiLab; WS-16-17: Wiki; WS-16-18: News and Public Opinion; WS-16-19: Social Media in the Newroom; WS-16-20: Social Web for Envioronmental and Ecological Monitoring (Vol. WS-16-16 - WS-16-20, pp. 99-107). AI Access Foundation.