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Recent years have witnessed an explosion of science conspiracy videos on theInternet, challenging science epistemology and public understanding of science.Scholars have started to examine the persuasion techniques used in conspiracymessages such as uncertainty and fear yet, little is understood about thevisual narratives, especially how visual narratives differ in videos thatdebunk conspiracies versus those that propagate conspiracies. This paperaddresses this gap in understanding visual framing in conspiracy videos throughanalyzing millions of frames from conspiracy and counter-conspiracy YouTubevideos using computational methods. We found that conspiracy videos tended touse lower color variance and brightness, especially in thumbnails and earlierparts of the videos. This paper also demonstrates how researchers can integratetextual and visual features in machine learning models to study conspiracies onsocial media and discusses the implications of computational modeling forscholars interested in studying visual manipulation in the digital era. Theanalysis of visual and textual features presented in this paper could be usefulfor future studies focused on designing systems to identify conspiracy contenton the Internet.

Visual Framing of Science Conspiracy Videos: Integrating Machine Learning with Communication Theories to Study the Use of Color and Brightness