Analysis of Faces in a Decade of US Cable TV News

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining Pub Date : 2021-08-14 DOI:10.1145/3447548.3467134

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

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引用次数: 8

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.

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十年来美国有线电视新闻的面孔分析

有线电视(电视)新闻每天到达数百万美国家庭。新闻利益相关者，如通信研究人员、记者和媒体监控组织，对有线电视新闻的视觉内容感兴趣，特别是谁在屏幕上。然而，手工分析是劳动密集型的，并且限制了先前研究的规模。我们对美国三大有线新闻网络(CNN, FOX和MSNBC)在2010年1月至2019年7月期间共244,038小时的十年有线新闻视频中的人脸进行了大规模的定量分析。我们的工作使用自动人脸识别和性别识别等技术来测量人脸的“屏幕时间”，并实现大规模的视觉分析和探索。我们的分析方法可以深入了解一系列广泛的社会相关话题。例如，男性面孔比女性面孔获得更多的屏幕时间(2010年为2.4倍，2019年为1.9倍)。为了使我们的数据集和注释可访问，我们在https://tvnews.stanford.edu上发布了一个公共接口，允许公众编写查询并执行他们自己的分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

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