What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3364–3374 July 5 - 10, 2020. c⃝2020 Association for Computational Linguistics 3364 What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context Ramy Baly1, Georgi Karadzhov2, Jisun An3, Haewoon Kwak3, Yoan Dinkov4, Ahmed Ali3, James Glass1, Preslav Nakov3 1MIT Computer Science and Artificial Intelligence Laboratory 2University of Cambridge, 3Qatar Computing Research Institute, HBKU, 4Sofia University {baly,glass}@mit.edu, georgi.karadzhov@cl.cam.ac.uk {jan,hkwak,amali,pnakov}@hbku.edu.qa, jdinkov@uni-sofia.bg Abstract Predicting the political bias and the factuality of reporting of entire news outlets are critical elements of media profiling, which is an under- studied but an increasingly important research direction. The present level of proliferation of fake, biased, and propagandistic content on- line, has made it impossible to fact-check ev- ery single suspicious claim, either manually or automatically. Alternatively, we can profile en- tire news outlets and look for those that are likely to publish fake or biased content. This approach makes it possible to detect likely “fake news” the moment they are published, by simply checking the reliability of their source. From a practical perspective, political bias and factuality of reporting have a linguistic aspect but also a social context. Here, we study the impact of both, namely (i) what was writ- ten (i.e., what was published by the target medium, and how it describes itself on Twitter) vs. (ii) who read it (i.e., analyzing the read- ers of the target medium on Facebook, Twitter, and YouTube). We further study (iii) what was written about the target medium on Wikipedia. The evaluation results show that what was writ- ten matters most, and that putting all infor- mation sources together yields huge improve- ments over the current state-of-the-art. 1 Introduction The rise of the Web has made it possible for any- body to create a website or a blog and to become a news medium. Undoubtedly, this was a hugely positive development as it elevated freedom of ex- pression to a whole new level, thus allowing any- body to make their voice heard online. With the subsequent rise of social media, anybody could po- tentially reach out to a vast audience, something that until recently was only possible for major news outlets. One of the consequences was a trust crisis: with tra- ditional news media stripped off their gate-keeping role, the society was left unprotected against po- tential manipulation. The issue became a general concern in 2016, a year marked by micro-targeted online disinformation and misinformation at an unprecedented scale, primarily in connection to Brexit and the US Presidential campaign. These developments gave rise to the term “fake news”, which can be defined as “false, often sensational, information disseminated under the guise of news reporting.”1 It was declared Word of the Year 2016 by Macquarie Dictionary and of Year 2017 by the Collins English Dictionary. In an attempt to solve the trust problem, several initiatives such as Politifact, Snopes, FactCheck, and Full Fact, have been launched to fact-check sus- picious …

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