Visual Framing of Science Conspiracy Videos

Integrating Machine Learning with Communication Theories to Study the Use of Color and Brightness



conspiracy, color and brightness, YouTube, computer vision, machine learning, text analysis


Recent years have witnessed an explosion of science conspiracy videos on the Internet, challenging science epistemology and public understanding of science. Scholars have started to examine the persuasion techniques used in conspiracy messages such as uncertainty and fear yet, little is understood about the visual narratives, especially how visual narratives differ in videos that debunk conspiracies versus those that propagate conspiracies. This paper addresses this gap in understanding visual framing in conspiracy videos through analyzing millions of frames from conspiracy and counter-conspiracy YouTube videos using computational methods. We found that conspiracy videos tended to use lower color variance and brightness, especially in thumbnails and earlier parts of the videos. This paper also demonstrates how researchers can integrate textual and visual features in machine learning modelsto study conspiracies on social mediaand discusses the implications of computational modeling for scholars interested in studying visual manipulation in the digital era. The analysis of visual and textual features presented in this paper could be useful for future studies focused on designing systems to identify conspiracy content on the Internet.

Author Biographies

Kaiping Chen, University of Wisconsin-Madison

Kaiping Chen is an Assistant Professor in Computational Communication at the Life Sciences Communication Department at the University of Wisconsin-Madison. Kaiping's research employs data science to examine how digital media and technologies affect politicians' accountability to public well-being and how deliberative designs can improve public discourse on controversial and emerging technologies. Kaiping's work has been supported by the National Science Foundation and was published/accepted in journals across disciplines, including American Political Science Review, Public Opinion Quarterly, Public Understanding of Science, Journal of Science CommunicationHarvard Kennedy School Misinformation Review, and among others.

Sang Jung Kim, University of Wisconsin-Madison

Sang Jung Kim is a Ph.D student at the Department of Journalism and Mass Communication at the University of Wisconsin-Madison. Sang's current research centers on citizens’ susceptibility to misleading information on social media platforms.

Qiantong Gao, University of Wisconsin-Madison

Qiantong Gao is a senior undergraduate majoring in Computer Science at the University of Wisconsin-Madison.

Sebastian Raschka, University of Wisconsin-Madison

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin Madison focusing on deep learning and machine learning research. This includes both deep learning method development and applications to both computer vision and computational biology.




How to Cite

Chen, K., Kim, S. J., Gao, Q., & Raschka, S. (2022). Visual Framing of Science Conspiracy Videos: Integrating Machine Learning with Communication Theories to Study the Use of Color and Brightness. Computational Communication Research, 4(1), 98–134. Retrieved from