Fact-Checking, Fake News, Propaganda, and Media Bias: Truth Seeking in the Post-Truth Era

The rise of social media has democratized content creation and has made it easy for everybody to share and spread information online. On the positive side, this has given rise to citizen journalism, thus enabling much faster dissemination of information compared to what was possible with newspapers, radio, and TV. On the negative side, stripping traditional media from their gate-keeping role has left the public unprotected against the spread of misinformation, which could now travel at breaking-news speed over the same democratic channel. This has given rise to the proliferation of false information specifically created to affect individual people’s beliefs, and ultimately to influence major events such as political elections. There are strong indications that false information was weaponized at an unprecedented scale during Brexit and the 2016 U.S. presidential elections. “Fake news,” which can be defined as fabricated information that mimics news media content in form but not in organizational process or intent, became the Word of the Year for 2017, according to Collins Dictionary. Thus, limiting the spread of “fake news” and its impact has become a major focus for computer scientists, journalists, social media companies, and regulatory authorities. The tutorial will offer an overview of the broad and emerging research area of disinformation, with focus on the latest developments and research directions.


Outline of the Tutorial
Here is an outline of the tutorial. More information and materials are available online. 1

Introduction
(i) What is "fake news"? (a) definitions (b) properties (ii) "Fake news" as a weapon of mass deception (a) impact on politics, finances, health (b) can we win the war on "fake news"?

Reading List
We recommend several surveys. Shu et al. (2017), which adopted a data mining perspective on "fake news" and focused on social media. Another survey (Zubiaga et al., 2018a) focused on rumor detection in social media. The survey by Thorne and Vlachos (2018) took a fact-checking perspective on "fake news" and related problems. The survey by Li et al. (2016) covering truth discovery in general. Lazer et al. (2018) offers a general overview and discussion on the science of "fake news", while Vosoughi et al. (2018) focuses on the process of proliferation of true and false news online. Other recent surveys focus on stance detection (Küçük and Can, 2020), on propaganda (Da San Martino et al., 2020b), on social bots (Ferrara et al., 2016), on false information (Zannettou et al., 2019b) and on bias on the Web (Baeza-Yates, 2018). See also the list of references at the end.

Type of Tutorial
The tutorial is both introductory, covering a number of topics related to fact-checking, propaganda and disinformation, but it is also cutting-edge, covering some latest developments in these areas.

Prerequisites
Prior knowledge of natural language processing, machine learning, and deep learning would be needed in order to understand large parts of the contents of this tutorial.

Preslav Nakov
Dr. Preslav Nakov is a Principal Scientist at the Qatar Computing Research Institute (QCRI), HBKU. His research interests include computational linguistics, "fake news" detection, factchecking, machine translation, question answering, sentiment analysis, lexical semantics, Web as a corpus, and biomedical text processing. He received his PhD degree from the University of California at Berkeley, and he was a Research Fellow in the National University of Singapore, a honorary lecturer in the Sofia University, and research staff at the Bulgarian Academy of Sciences. At QCRI, he leads the Tanbih project, 2 developed in collaboration with MIT, which aims to limit the effect of "fake news", propaganda and media bias by making users aware of what they are reading. The project was featured by over 100 news outlets, including Forbes, Boston Globe, Aljazeera, MIT Technology Review, Science Daily, Popular Science, Fast Company, The Register, WIRED, and Engadget, among others.
As part of the project, he co-organized several shared tasks on fact-checking and propaganda detection at SemEval and CLEF, as well as a related NLP4IF workshop.
He is President of ACL SIGLEX, a Secretary of ACL SIGSLAV, and a member of the EACL advisory board. He is also member of the editorial board of TACL, CS&L, NLE, AI Communications, and Frontiers in AI, as well as of the Language Science Press Book Series on Phraseology and Multiword Expressions. He co-authored a Morgan & Claypool book on Semantic Relations between Nominals, two books on computer algorithms, and many research papers in top-tier conferences and journals. He received the Young Researcher Award at RANLP'2011, and he was the first to receive the Bulgarian President's John Atanasoff award, named after the inventor of the first automatic electronic digital computer.

Giovanni Da San Martino
Giovanni Da San Martino is a Senior Assistant Professor at the University of Padova, Italy. His research interests are at the intersection of machine learning and natural language processing. He has been researching for 10+ years on these topics, publishing more than 60 publications in top-tier conferences and journals. He received his PhD from the University of Bologna, he was a Research Fellow at the University of Padova and a Scientist at Qatar Computing Research Institute. He has worked on several NLP tasks including paraphrase recognition, stance detection and community question answering. Currently, he is actively involved in researching on disinformation and propaganda detection. As part of this research he has been co-organiser of the Checkthat! labs at CLEF 2018-2020, the NLP4IF 2019-2020 workshops on "censorship, disinformation, and propaganda", the 2019 Hack the News Datathon and the SemEval-2020 task 11 on "Detection of propaganda techniques in news articles."