Fighting the Global Social Media Infodemic: from Fake News to Harmful Content
The COVID-19 pandemic has brought us the first global social media infodemic. While fighting this infodemic is typically thought of in terms of factuality, the problem is much broader as malicious content includes not only «fake news», rumors, and conspiracy theories, but also hate speech, racism, xenophobia, panic, and mistrust in authorities, among others. Thus, we argue for the need for a holistic approach combining the perspectives of journalists, fact-checkers, policymakers, social media platforms, and society as a whole.
We further argue for the need to analyze entire news outlets, which can be done in advance; then, we can fact-check the news before it was even written: by checking how trustworthy the outlet that has published it is (which is what journalists actually do). We will show how this can be automated by looking at a variety of information sources.
The infodemic is often described using terms such as «fake news», which mislead people to focus exclusively on factuality and to ignore the other half of the problem: the potential malicious intent. We aim to bridge this gap by focusing on the detection of specific propaganda techniques in text, e.g., appeal to emotions, fear, prejudices, logical fallacies, etc. This is the target of the ongoing SemEval-2023 task 3, which focuses on multilingual aspects of the problem, covering English, French, German, Italian, Polish, and Russian. We further present extensions of this work to the automatic analysis of various types of harmful memes: from propaganda to harmfulness and harm’s target identification to role-labeling in terms of who is portrayed as hero/villain/victim, and generating natural text explanations.
Speaker Bios
Preslav Nakov is Professor at Mohamed bin Zayed University of Artificial Intelligence. Previously, he was Principal Scientist at the Qatar Computing Research Institute (QCRI), HBKU, where he led the Tanbih mega-project, developed in collaboration with MIT, which aims to limit the impact of «fake news», propaganda and media bias by making users aware of what they are reading, thus promoting media literacy and critical thinking. He received his PhD degree in Computer Science from the University of California at Berkeley, supported by a Fulbright grant. He is Chair-Elect of the Association for Computational Linguistics (ACL), Secretary of ACL SIGSLAV, and Secretary of the Truth and Trust Online board of trustees. Formerly, he was PC chair of ACL 2022, and President of ACL SIGLEX. He is also member of the editorial board of several journals including Computational Linguistics, TACL, ACM TOIS, IEEE TASL, IEEE TAC, CS&L, NLE, AI Communications, and Frontiers in AI. He authored a Morgan & Claypool book on Semantic Relations between Nominals, two books on computer algorithms, and 250+ research papers. He received a Best Paper Award at ACM WebSci’2022, a Best Long Paper Award at CIKM’2020, a Best Demo Paper Award (Honorable Mention) at ACL’2020, a Best Task Paper Award (Honorable Mention) at SemEval’2020, a Best Poster Award at SocInfo’2019, and the Young Researcher Award at RANLP’2011. He was also the first to receive the Bulgarian President’s John Atanasoff award, named after the inventor of the first automatic electronic digital computer. Dr. Nakov’s research was featured by over 100 news outlets, including Forbes, Boston Globe, Aljazeera, DefenseOne, Business Insider, MIT Technology Review, Science Daily, Popular Science, Fast Company, The Register, WIRED, and Engadget, among others.
- Date:
- Haut-parleurs:
- Dr. Preslav Nakov
- Affiliation:
- Mohamed bin Zayed University of Artificial Intelligence
