Master of Science in Computer Science (MSCS)
Fabio Di Troia
keystroke dynamics, XGBoost, CNNs, RNNS
YouTube videos often include captivating descriptions and intriguing thumbnails designed to increase the number of views, and thereby increase the revenue for the person who posted the video. This creates an incentive for people to post clickbait videos, in which the content might deviate significantly from the title, description, or thumbnail. In effect, users are tricked into clicking on clickbait videos. In this research, we consider the challenging problem of detecting clickbait YouTube videos. We experiment with multiple state of the art machine learning techniques and a variety of textual features.
Li, Jianwei, "Keystroke Dynamics for User Authentication with Fixed and Free Text" (2021). Master's Projects. 1004.