  We propose a novel framework for predicting the factuality of reporting of
news media outlets by studying the user attention cycles in their YouTube
channels. In particular, we design a rich set of features derived from the
temporal evolution of the number of views, likes, dislikes, and comments for a
video, which we then aggregate to the channel level. We develop and release a
dataset for the task, containing observations of user attention on YouTube
channels for 489 news media. Our experiments demonstrate both complementarity
and sizable improvements over state-of-the-art textual representations.
