DOI: 10.1177/20563051231195582 ISSN:

Seeing the Black Lives Matter Movement Through Computer Vision? An Automated Visual Analysis of News Media Images on Facebook

Minchul Kim, Ozen Bas
  • Computer Science Applications
  • Communication
  • Cultural Studies

In this study, automated visual analysis was used to explore how the political leanings of news media are associated with their visual representation of the Black Lives Matter (BLM) movement. We analyzed more than 9,000 images posted on Facebook pages run by U.S. news media between August 2014 and October 2020 using commercially developed computer vision tools and a topic modeling algorithm. The results show that images used in BLM-related news coverage can be categorized into 10 distinctively themed groups that overlap with the main types of protest images uncovered by manual content analysis. Furthermore, news sources engaged in different visual representation practices depending on their partisan leanings. The patterns uncovered in this study imply that (de)legitimization of protests may take either active or passive forms. These findings contribute to theorization of the way news media might use social media platforms to (de)legitimize social protests, which may influence public opinion on social issues.

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