Published on Fri Aug 27 2021

Predicting the Factuality of Reporting of News Media Using Observations About User Attention in Their YouTube Channels

Krasimira Bozhanova, Yoan Dinkov, Ivan Koychev, Maria Castaldo, Tommaso Venturini, Preslav Nakov

We design a rich set of features derived from the number of views, likes, dislikes, and comments for a video. We then aggregate to the channel level. Our experiments demonstrate both complementarity and sizable improvements over state-of-the-art textual representations.

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Abstract

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.

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