tutorial

Fake News: Fundamental Theories, Detection Strategies and Challenges

Authors:

Huan LiuAuthors Info & Claims

WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

Pages 836 - 837

https://doi.org/10.1145/3289600.3291382

Published: 30 January 2019 Publication History

Abstract

The explosive growth of fake news and its erosion to democracy, justice, and public trust increased the demand for fake news detection. As an interdisciplinary topic, the study of fake news encourages a concerted effort of experts in computer and information science, political science, journalism, social science, psychology, and economics. A comprehensive framework to systematically understand and detect fake news is necessary to attract and unite researchers in related areas to conduct research on fake news. This tutorial aims to clearly present (1) fake news research, its challenges, and research directions; (2) a comparison between fake news and other related concepts (e.g., rumors); (3) the fundamental theories developed across various disciplines that facilitate interdisciplinary research; (4) various detection strategies unified under a comprehensive framework for fake news detection; and (5) the state-of-the-art datasets, patterns, and models. We present fake news detection from various perspectives, which involve news content and information in social networks, and broadly adopt techniques in data mining, machine learning, natural language processing, information retrieval and social search. Facing the upcoming 2020 U.S. presidential election, challenges for automatic, effective and efficient fake news detection are also clarified in this tutorial.

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Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, and Huan Liu. 2018b. FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media. arXiv preprint arXiv:1809.01286 (2018).

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Cited By

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https://doi.org/10.5585/remark.v23i3.24363
Achyut Pal Ananya Majumder Suparna Biswas Antara Ghosal Palasri Dhar Sayan Roy Chaudhuri (2024)An Efficient Fake News Detection using Machine LearningInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-15015(91-96)Online publication date: 10-Jan-2024
https://doi.org/10.48175/IJARSCT-15015
Netshakhuma NKhadambi I(2024)Information Governance Framework to Achieve Information Hygiene in South AfricaBusiness Drivers in Promoting Digital Detoxification10.4018/979-8-3693-1107-3.ch011(177-194)Online publication date: 23-Feb-2024
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Fake News: Fundamental Theories, Detection Strategies and Challenges

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Published In

cover image ACM Conferences

WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

January 2019

874 pages

ISBN:9781450359405

DOI:10.1145/3289600

General Chairs:
J. Shane Culpepper
RMIT University
,
Alistair Moffat
The University of Melbourne
,
Program Chairs:
Paul N. Bennett
Microsoft
,
Kristina Lerman
University of Southern California

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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Published: 30 January 2019

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WSDM '19: The Twelfth ACM International Conference on Web Search and Data Mining

February 11 - 15, 2019

Melbourne VIC, Australia

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WSDM '19 Paper Acceptance Rate 84 of 511 submissions, 16%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Viacava JCoelho BSabin GLopes G(2024)“Make it difficult”ReMark - Revista Brasileira de Marketing10.5585/remark.v23i3.2436323:3(1023-1080)Online publication date: 26-Jul-2024
https://doi.org/10.5585/remark.v23i3.24363
Achyut Pal Ananya Majumder Suparna Biswas Antara Ghosal Palasri Dhar Sayan Roy Chaudhuri (2024)An Efficient Fake News Detection using Machine LearningInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-15015(91-96)Online publication date: 10-Jan-2024
https://doi.org/10.48175/IJARSCT-15015
Netshakhuma NKhadambi I(2024)Information Governance Framework to Achieve Information Hygiene in South AfricaBusiness Drivers in Promoting Digital Detoxification10.4018/979-8-3693-1107-3.ch011(177-194)Online publication date: 23-Feb-2024
https://doi.org/10.4018/979-8-3693-1107-3.ch011
Andronikos TSirokofskich A(2024)A Quantum Approach to News Verification from the Perspective of a News AggregatorInformation10.3390/info1504020715:4(207)Online publication date: 6-Apr-2024
https://doi.org/10.3390/info15040207
Papageorgiou EChronis CVarlamis IHimeur Y(2024)A Survey on the Use of Large Language Models (LLMs) in Fake NewsFuture Internet10.3390/fi1608029816:8(298)Online publication date: 19-Aug-2024
https://doi.org/10.3390/fi16080298
Zhou YPang AYu G(2024)Vae-Clip: Unveiling Deception through Cross-Modal Models and Multi-Feature Integration in Multi-Modal Fake News DetectionElectronics10.3390/electronics1315295813:15(2958)Online publication date: 26-Jul-2024
https://doi.org/10.3390/electronics13152958
Wei SWu BGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Intra and Inter-modality Incongruity Modeling and Adversarial Contrastive Learning for Multimodal Fake News DetectionProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658118(666-674)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658118
Yaqub WKay JGoldwater M(2024)Foundations for Enabling People to Recognise Misinformation in Social Media News based on Retracted ScienceProceedings of the ACM on Human-Computer Interaction10.1145/36373358:CSCW1(1-38)Online publication date: 26-Apr-2024
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Zhang YSharma KDu LLiu YChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Toward Mitigating Misinformation and Social Media Manipulation in LLM EraCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3641256(1302-1305)Online publication date: 13-May-2024
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Wu LLiu PZhao YWang PZhang Y(2024)Human Cognition-Based Consistency Inference Networks for Multi-Modal Fake News DetectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.328055536:1(211-225)Online publication date: Jan-2024
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