Analysis of Faces in a Decade of US Cable TV News

James Hong, Will Crichton, Haotian Zhang, Daniel Y. Fu, Jacob Ritchie, Jeremy Barenholtz, Ben Hannel, Xinwei Yao, Michaela Murray, Geraldine Moriba, Maneesh Agrawala, Kayvon Fatahalian

ACM Conference on Knowledge Discovery and Data Mining (KDD) 2021

Abstract

Cable (TV) news reaches millions of US households each day. News stakeholders such as communications researchers, journalists, and media monitoring organizations are interested in the visual content of cable news, especially who is on-screen. Manual analysis, however, is labor intensive and limits the size of prior studies.

We conduct a large-scale, quantitative analysis of the faces in a decade of cable news video from the top three US cable news networks (CNN, FOX, and MSNBC), totaling 244,038 hours between January 2010 and July 2019. Our work uses technologies such as automatic face and gender recognition to measure the “screen time” of faces and to enable visual analysis and exploration at scale. Our analysis method gives insight into a broad set of socially relevant topics. For instance, male-presenting faces receive much more screen time than female-presenting faces (2.4x in 2010, 1.9x in 2019).

To make our dataset and annotations accessible, we release a public interface, at https://tvnews.stanford.edu/, that allows the general public to write queries and to perform their own analyses.

Paper

PDF and Supplemental PDF (with additional analysis and methodology). [ACM Library]

To reference our work or the analyzer tool, please cite:

@InProceedings{cabletvnews,
    author={James Hong and Will Crichton and Haotian Zhang and Daniel Y. Fu and Jacob Ritchie and Jeremy Barenholtz and Ben Hannel and Xinwei Yao and Michaela Murray and Geraldine Moriba and Maneesh Agrawala and Kayvon Fatahalian},
    title={Analysis of Faces in a Decade of US Cable TV News},
    publisher = {Association for Computing Machinery},
    booktitle = {Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
    year = {2021}
}

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