The news ecosystem has become increasingly complex, encompassing a wide range of sources with varying levels of trustworthiness, and with public commentary giving different spins to the same stories. In this paper, we present a multi-platform measurement of this ecosystem. We compile a list of 1,073 news websites and extract posts from four Web communities (Twitter, Reddit, 4chan, and Gab) that contain URLs from these sources. This yields a dataset of 38M posts containing 15M news URLs, spanning almost three years. We study the data along several axes, assessing the trustworthiness of shared news, designing a method to group news articles into stories, analyzing these stories are discussed and measuring the influence various Web communities have in that. Our analysis shows that different communities discuss different types of news, with polarized communities like Gab and /r/The_Donald subreddit disproportionately referencing untrustworthy sources. We also find that fringe communities often have a disproportionate influence on other platforms w.r.t. pushing narratives around certain news, for example about political elections, immigration, or foreign policy.
@article{arxiv.2103.03631,
title = {A Multi-Platform Analysis of Political News Discussion and Sharing on Web Communities},
author = {Yuping Wang and Savvas Zannettou and Jeremy Blackburn and Barry Bradlyn and Emiliano De Cristofaro and Gianluca Stringhini},
journal= {arXiv preprint arXiv:2103.03631},
year = {2021}
}