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Fake News Research: Theories, Detection Strategies, and Open Problems

Authors:

Huan LiuAuthors Info & Claims

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2019

Pages 3207 - 3208

https://doi.org/10.1145/3292500.3332287

Published: 25 July 2019 Publication History

Abstract

Fake news has become a global phenomenon due its explosive growth, particularly on social media. The goal of this tutorial is to (1) clearly introduce the concept and characteristics of fake news and how it can be formally differentiated from other similar concepts such as mis-/dis-information, satire news, rumors, among others, which helps deepen the understanding of fake news; (2) 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 explainable fake news detection; (3) 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, and information retrieval; and (4) detail open issues within current fake news studies to reveal 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.

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References

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Xin Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Ni Lao, Kevin Murphy, Thomas Strohmann, Shaohua Sun, and Wei Zhang. 2014. Knowledge vault: A web-scale approach to probabilistic knowledge fusion. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 601--610.

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Kai Shu, Limeng Cui, Suhang Wang, Dongwon Lee, and Huan Liu. 2019a. dEFEND: Explainable Fake News Detection. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM.

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Kai Shu, Suhang Wang, and Huan Liu. 2019b. Beyond News Contents: The Role of Social Context for Fake News Detection. In WSDM.

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Yaqing Wang, Fenglong Ma, Zhiwei Jin, Ye Yuan, Guangxu Xun, Kishlay Jha, Lu Su, and Jing Gao. 2018. EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 849--857.

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Cited By

Zhang YSharma KDu LLiu YChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Toward Mitigating Misinformation and Social Media Manipulation in LLM EraCompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3641256(1302-1305)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3641256
Reshi JAli R(2024)Defending against Misinformation: Evaluating Transformer Architectures for Quick Misinformation Detection on Social MediaProcedia Computer Science10.1016/j.procs.2024.04.275235(2909-2919)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.04.275
Duzen ZRiveni MAktas M(2024)Time-Dynamics of (Mis)Information Spread on Social Networks: A COVID-19 Case StudyComplex Networks & Their Applications XII10.1007/978-3-031-53503-1_13(156-167)Online publication date: 29-Feb-2024
https://doi.org/10.1007/978-3-031-53503-1_13
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Index Terms

Fake News Research: Theories, Detection Strategies, and Open Problems

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Information & Contributors

Information

Published In

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2019

3305 pages

ISBN:9781450362016

DOI:10.1145/3292500

General Chairs:
Ankur Teredesai
KenSci
,
Vipin Kumar
University of Minnesota
,
Program Chairs:
Ying Li
EV Analysis Corporation
,
Rómer Rosales
LinkedIn
,
Evimaria Terzi
Boston University
,
George Karypis
University of Minnesota

Copyright © 2019 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 July 2019

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Tutorial

Conference

KDD '19

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KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 4 - 8, 2019

AK, Anchorage, USA

Acceptance Rates

KDD '19 Paper Acceptance Rate 110 of 1,200 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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KDD '24

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The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 25 - 29, 2024

Barcelona , Spain

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Cited By

Zhang YSharma KDu LLiu YChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Toward Mitigating Misinformation and Social Media Manipulation in LLM EraCompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3641256(1302-1305)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3641256
Reshi JAli R(2024)Defending against Misinformation: Evaluating Transformer Architectures for Quick Misinformation Detection on Social MediaProcedia Computer Science10.1016/j.procs.2024.04.275235(2909-2919)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.04.275
Duzen ZRiveni MAktas M(2024)Time-Dynamics of (Mis)Information Spread on Social Networks: A COVID-19 Case StudyComplex Networks & Their Applications XII10.1007/978-3-031-53503-1_13(156-167)Online publication date: 29-Feb-2024
https://doi.org/10.1007/978-3-031-53503-1_13
Xu CKechadi M(2023)Fuzzy Deep Hybrid Network for Fake News DetectionProceedings of the 12th International Symposium on Information and Communication Technology10.1145/3628797.3628971(118-125)Online publication date: 7-Dec-2023
https://dl.acm.org/doi/10.1145/3628797.3628971
Ren JXia FLee INoori Hoshyar AAggarwal C(2023)Graph Learning for Anomaly Analytics: Algorithms, Applications, and ChallengesACM Transactions on Intelligent Systems and Technology10.1145/357090614:2(1-29)Online publication date: 16-Feb-2023
https://dl.acm.org/doi/10.1145/3570906
Hsu RChen BDai C(2023)Intelligent Detection of Disinformation Based on Chronological and Spatial Topologies2023 9th International Conference on Applied System Innovation (ICASI)10.1109/ICASI57738.2023.10179599(258-260)Online publication date: 21-Apr-2023
https://doi.org/10.1109/ICASI57738.2023.10179599
Kozik RMazurczyk WCabaj KPawlicka APawlicki MChoraś M(2023)Combating Disinformation with Holistic Architecture, Neuro-symbolic AI and NLU Models2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA60987.2023.10302543(1-9)Online publication date: 9-Oct-2023
https://doi.org/10.1109/DSAA60987.2023.10302543
Hua JCui XLi XTang KZhu P(2023)Multimodal fake news detection through data augmentation-based contrastive learningApplied Soft Computing10.1016/j.asoc.2023.110125136(110125)Online publication date: Mar-2023
https://doi.org/10.1016/j.asoc.2023.110125
Thompson RJoseph SAdeliyi T(2022)A Systematic Literature Review and Meta-Analysis of Studies on Online Fake News DetectionInformation10.3390/info1311052713:11(527)Online publication date: 4-Nov-2022
https://doi.org/10.3390/info13110527
Chen YChen YYang KLai FHuang CChen YTu Y(2022)The Prevalence and Impact of Fake News on COVID-19 Vaccination in Taiwan: Retrospective Study of Digital MediaJournal of Medical Internet Research10.2196/3683024:4(e36830)Online publication date: 26-Apr-2022
https://doi.org/10.2196/36830
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