How Digitally Extractable Attributes of YouTube Video Thumbnails and Titles Affect Video Views

YouTube is the world's largest video streaming platform, where videos appear in users" recommendation lists as thumbnail images and titles. Content creators compete for viewer attention, but as of now, what drives the popularity of YouTube videos is still a young and growing body of research, especially regarding users" pre-view behavior - what drives them to click a video they see. This research contributes to the field by exploring new content-independent visual attributes of thumbnails and titles entirely extractable by code. Several Python libraries and AI models are used to extract data such as image complexity, thumbnail text, sentiment, and faces from a dataset of 1600 videos. The extracted data is visualized to explore their relationship with video view count. This research unexpectedly finds that the studied attributes have negligible effect strength on a video's ability to attract views. Findings and possibilities regarding this outcome are discussed.