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

• URL: http://arxiv.org/abs/2108.12519v1
• Date: Fri, 27 Aug 2021 22:43:00 GMT
• Title: Predicting the Factuality of Reporting of News Media Using Observations About User Attention in Their YouTube Channels
• Authors: Krasimira Bozhanova, Yoan Dinkov, Ivan Koychev, Maria Castaldo, Tommaso Venturini, Preslav Nakov
• Abstract summary: 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.
• Score: 15.650835825104103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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