Predicting the Factuality of Reporting of News Media Using Observations About User Attention in Their YouTube Channels
This addresses misinformation detection for media consumers, but it is incremental as it builds on existing factuality prediction with new features.
The paper tackles predicting the factuality of news media outlets by analyzing user attention cycles on their YouTube channels, achieving sizable improvements over state-of-the-art textual methods.
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.