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We study the problem of profiling news media on the Web with respect to theirfactuality of reporting and bias. This is an important but under-studiedproblem related to disinformation and "fake news" detection, but it addressesthe issue at a coarser granularity compared to looking at an individual articleor an individual claim. This is useful as it allows to profile entire mediaoutlets in advance. Unlike previous work, which has focused primarily on text(e.g.,~on the text of the articles published by the target website, or on thetextual description in their social media profiles or in Wikipedia), here ourmain focus is on modeling the similarity between media outlets based on theoverlap of their audience. This is motivated by homophily considerations,i.e.,~the tendency of people to have connections to people with similarinterests, which we extend to media, hypothesizing that similar types of mediawould be read by similar kinds of users. In particular, we propose GREENER(GRaph nEural nEtwork for News mEdia pRofiling), a model that builds a graph ofinter-media connections based on their audience overlap, and then uses graphneural networks to represent each medium. We find that such representations arequite useful for predicting the factuality and the bias of news media outlets,yielding improvements over state-of-the-art results reported on two datasets.When augmented with conventionally used representations obtained from newsarticles, Twitter, YouTube, Facebook, and Wikipedia, prediction accuracy isfound to improve by 2.5-27 macro-F1 points for the two tasks.

GREENER: Graph Neural Networks for News Media Profiling