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 for identifying conspiracies on social media and discusses the implications of computational modeling for scholars interested in studying visual manipulation in the digital era.
翻译:近些年来,互联网上出现了科学阴谋视频爆炸,科学认知学和公众对科学的理解受到挑战,学者们开始研究阴谋信息(如不确定性和恐惧)中使用的说服技巧,然而,对于视觉叙事,特别是揭发阴谋的视频与传播阴谋的视频的视觉叙事有何不同,人们对此知之甚少。本文通过利用计算方法分析阴谋和反阴谋YouTube视频中的数百万个框架,解决了对阴谋录像中视觉框架的理解差距。我们发现阴谋视频往往使用更低的颜色差异和亮度,特别是在缩略图和早期的视频中。本文还展示了研究人员如何将文字和视觉特征整合到社会媒体上识别阴谋,并讨论计算模型对于有兴趣在数字时代研究视觉操纵的学者的影响。