Fake News Research

Fundamental Theories, Detection Strategies & Open Problems

Aug. 4 (8 am - 12 pm) | Summit 5 - Ground Level, Egan

KDD 2019 | Anchorage, Alaska


The explosive growth of fake news and its erosion to democracy, justice, and public trust increases the demand for research on fake news. The goal of this tutorial is to


(I) clearly introduce the concept and characteristics of fake news and how it can be formally differentiated from other similar concepts such as false/satire news, mis-/dis-information, among others, which helps deepen the understanding of fake news;


(II) provide a comprehensive review of fundamental theories across disciplines and illustrate how they can be used to conduct interdisciplinary fake news research, facilitating a concerted effort of experts in computer and information science, political science, journalism, social science, psychology and economics. Such concerted efforts can result in highly efficient and interpretable fake news detection;


(III) systematically present fake news detection strategies from four perspectives (i.e., knowledge, style, propagation, and credibility) and the ways that each perspective utilizes techniques developed in data/graph mining, machine learning, natural language processing, information retrieval; and


(IV) detail open issues within current fake news studies to reveal its great potential research opportunities, hoping to attract researchers within a broader area to work on fake news detection and further facilitate its development.


The tutorial aims to promote a fair, healthy and safe online information and news dissemination ecosystem, hoping to attract more researchers, engineers and students with various interests to fake news research. Few prerequisite are required for KDD participants to attend.


Reza Zafarani

Reza Zafarani is an Assistant Professor of EECS at Syracuse University. Reza's research interests are in Social Media Mining, Data Mining, Machine Learning, and Social Network Analysis.  His research emphasis has been on addressing challenges in large-scale data analytics to enhance the scientific discovery process from big data, especially in social media. These challenges include evaluation without ground truth, fast identification of relevant information in massive datasets, learning with limited information, user behavior analysis and modeling at scale, and information integration and modeling across multiple data sources.  His research has been published at major academic venues, and highlighted in various scientific outlets. Reza is the principal author of “Social Media Mining: An Introduction”, a textbook by Cambridge University Press and the associate editor for SIGKDD Explorations and Frontiers in communication. He is the winner of the President's Award for Innovation and outstanding teaching award at Arizona State University. 

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Xinyi Zhou

Xinyi Zhou is a Ph.D. candidate of Computer and Information Science and Engineering at Syracuse University. She also works as a research assistant at Data Lab advised by Dr. Reza Zafarani. Her research interests span machine learning, text and graph mining, and social computing with an emphasis on fake news research. Her work has been published on conferences such as KDD, WSDM, PAKDD, ICWSM, and ASONAM and journals such as ACM Computing Surveys (CSUR), Annals of Operations Research (ANOR), Safety Science, and SIGKDD Explorations, where two of her publications have been selected in The 100 Most Influential Research Papers in China in 2017 (0.3%). She was a reviewer for conferences such as KDD, WWW, AAAI, WSDM, ICDM, CIKM, SDM, and PAKDD, and journals such as CSUR, TWEB, and NeurocomputingXinyi awarded the National Scholarship (1\%) and the Varshney Scholarship recognized to only one student in the college.

Kai Shu

Kai Shu is a PhD student majoring in computer science at Arizona State University. He also works as a research assistant at the Data Mining and Machine Learning Lab (DMML), supervised by Dr. Huan Liu. He has published innovative works in top-tier conferences such as CIKM, WSDM, WWW, IJCAI, AAAI, ICDM. His current research interests include social media mining, machine learning, deep learning based generative models, fake news detection. He is the principal architect for FakeNewsTracker, an advanced visual analytic system for fake news collection, detection, and visualization, which also won the SBP disinformation challenge award in 2018. He also worked as a research intern at Yahoo Research in 2018.

Huan Liu

Huan Liu is a professor of Computer Science and Engineering at Arizona State University. He obtained his Ph.D. in Computer Science at University of Southern California and B.Eng. in Computer Science and Electrical Engineering at Shanghai JiaoTong University. Before he joined ASU, he worked at Telecom Australia Research Labs and was on the faculty at National University of Singapore. At Arizona State University, he was recognized for excellence in teaching and research in Computer Science and Engineering and received the 2014 President's Award for Innovation. His research interests are in data mining, machine learning, social computing, and artificial intelligence, investigating interdisciplinary problems that arise in many real-world, data-intensive applications with high-dimensional data of disparate forms such as social media. His well-cited publications include books, book chapters, encyclopedia entries as well as conference and journal papers. He is an IEEE Fellow.


Background & Introduction
30 minutes
Research Background
Definition Fake News and Its Characteristics
Comparison of Fake News and Related Concepts
Fundamental Theories
30 minutes
Introduction of Fundamental Theories
Roles in Fake News Detection
Fake News Detection
120 minutes
Knowledge-based fake news detection
Style-based fake news detection
Propagation-based fake news detection
Credibility-based fake news detection
30 minutes
Challenges & Open Issues


Related Tutorial

  • Fake News: Fundamental Theories, Detection Strategies and Challenges, Xinyi Zhou, Reza Zafarani, Kai Shu and Huan Liu, WSDM, 2019. [slides] [pdf]

Technical Papers

  • SAFE: Similarity-Aware Multimodal Fake News Detection. Xinyi Zhou,* Jindi Wu,* and Reza Zafarani. PAKDD, 2020. [pdf]

  • Niraj Sitaula, Chilukuri K. Mohan, Jennifer Grygiel, Xinyi Zhou, and Reza Zafarani. Credibility-based Fake News Detection. Lecture Notes in Social Network, Springer, 2020.  ​

  • The Role of User Profiles for Fake News Detection. Kai Shu, Xinyi Zhou, Suhang Wang, Reza Zafarani, and Huan Liu. ASONAM, 2019. [pdf]

  • Network-based Fake News Detection: A Pattern-driven Approach. Xinyi Zhou and Reza Zafarani. ACM SIGKDD Explorations Newsletter, 2019. [pdf]

  • dEFEND: Explainable Fake News Detection. Kai Shu, Limeng Cui, Suhang Wang, Dongwon Lee, and Huan Liu. KDD, 2019. [pdf]

  • Fake News Early Detection: A Theory-driven Model. Xinyi Zhou, Atishay Jain, Vir V. Phoha, and Reza Zafarani. arXiv, 2019. [pdf]​​

  • Unsupervised Fake News Detection on Social Media: A Generative Approach. Shuo Yang, Kai Shu, Suhang Wang, Renjie Gu, Fan Wu, and Huan Liu. AAAI, 2019. [pdf]

  • Beyond News Contents: The Role of Social Context for Fake News Detection. Kai Shu, Suhang Wang, and Huan Liu. WSDM, 2019. [pdf]

  • Deep Headline Generation for Clickbait Detection. Kai Shu, Suhang Wang, Thai Le, Dongwon Lee, and Huan Liu. ICDM, 2018. [pdf]



  • FakeNewsNet [link]


Slides [pdf]  |  Proposal [pdf]

  • A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities. Xinyi Zhou and Reza Zafarani. ACM Computing Surveys (CSUR), 2020. [pdf]

  • Fake News Detection on Social Media: A Data Mining Perspective. Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang and Huan Liu. ACM SIGKDD Exploration Newsletter, 2017. [pdf]