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 language | English |
|---|---|
| Title of host publication | WS-16-16 |
| Subtitle of host publication | 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 |
| Publisher | AI Access Foundation |
| Pages | 99-107 |
| Number of pages | 9 |
| Volume | WS-16-16 - WS-16-20 |
| ISBN (Electronic) | 9781577357681 |
| Publication status | Published - 1 Jan 2016 |
| Event | 10th International AAAI Conference on Web and Social Media, ICWSM 2016 - Cologne, Germany Duration: 17 May 2016 → 20 May 2016 |
Other
| Other | 10th International AAAI Conference on Web and Social Media, ICWSM 2016 |
|---|---|
| Country | Germany |
| City | Cologne |
| Period | 17/5/16 → 20/5/16 |
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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.