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

Autor:	Bozhanova, Krasimira, Dinkov, Yoan, Koychev, Ivan, Castaldo, Maria, Venturini, Tommaso, Nakov, Preslav
Rok vydání:	2021
Předmět:	Computer Science - Computation and Language Computer Science - Information Retrieval Computer Science - Machine Learning Computer Science - Social and Information Networks 68T50 I.2.7
Zdroj:	RANLP-2021
Druh dokumentu:	Working Paper
Popis:	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. Comment: Factuality, disinformation, misinformation, fake news, Youtube channels, propaganda, attention cycles
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2108.12519 Zobrazit plný text záznamu View this record from Arxiv