  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 models to study conspiracies on
social media and 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.
