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Domain Adaptive Fake News Detection via Reinforcement Learning

Authors:

Ahmadreza Mosallanezhad,

Mansooreh Karami,

Michelle V. Mancenido,

Huan LiuAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 3632 - 3640

https://doi.org/10.1145/3485447.3512258

Published: 25 April 2022 Publication History

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Abstract

With social media being a major force in information consumption, accelerated propagation of fake news has presented new challenges for platforms to distinguish between legitimate and fake news. Effective fake news detection is a non-trivial task due to the diverse nature of news domains and expensive annotation costs. In this work, we address the limitations of existing automated fake news detection models by incorporating auxiliary information (e.g., user comments and user-news interactions) into a novel reinforcement learning-based model called REinforced Adaptive Learning Fake News Detection (REAL-FND). REAL-FND exploits cross-domain and within-domain knowledge that makes it robust in a target domain, despite being trained in a different source domain. Extensive experiments on real-world datasets illustrate the effectiveness of the proposed model, especially when limited labeled data is available in the target domain.

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Cited By

Krishnamurthi IV SN H M(2024)Empirical Analysis for Classification of Fake News through Text RepresentationJournal of Information Technology and Digital World10.36548/jitdw.2024.1.0036:1(27-45)Online publication date: Mar-2024
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https://doi.org/10.3390/systems12080274
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Index Terms

Domain Adaptive Fake News Detection via Reinforcement Learning

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

Copyright © 2022 ACM.

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Published: 25 April 2022

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Cited By

Krishnamurthi IV SN H M(2024)Empirical Analysis for Classification of Fake News through Text RepresentationJournal of Information Technology and Digital World10.36548/jitdw.2024.1.0036:1(27-45)Online publication date: Mar-2024
https://doi.org/10.36548/jitdw.2024.1.003
Song GWang YChen XHu HLiu F(2024)Evaluating User Engagement in Online News: A Deep Learning Approach Based on Attractiveness and Multiple FeaturesSystems10.3390/systems1208027412:8(274)Online publication date: 30-Jul-2024
https://doi.org/10.3390/systems12080274
Alnabhan MBranco P(2024)BERTGuard: Two-Tiered Multi-Domain Fake News Detection with Class Imbalance MitigationBig Data and Cognitive Computing10.3390/bdcc80800938:8(93)Online publication date: 16-Aug-2024
https://doi.org/10.3390/bdcc8080093
Shang LZhang YChen BZong RYue ZZeng HWei NWang DChua TNgo CKa-Wei Lee RKumar RLauw H(2024)MMAdapt: A Knowledge-guided Multi-source Multi-class Domain Adaptive Framework for Early Health Misinformation DetectionProceedings of the ACM Web Conference 202410.1145/3589334.3648152(4653-4663)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3648152
Tao XWang LLiu QWu SWang LChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Semantic Evolvement Enhanced Graph Autoencoder for Rumor DetectionProceedings of the ACM Web Conference 202410.1145/3589334.3645478(4150-4159)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645478
Liu HWang WSun HRocha ALi H(2024)Robust Domain Misinformation Detection via Multi-Modal Feature AlignmentIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.332636819(793-806)Online publication date: 1-Jan-2024
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Shi JZhao XZhang NLei YMin L(2024)Rough-Fuzzy Graph Learning Domain Adaptation for Fake News DetectionIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.331218211:4(5275-5286)Online publication date: Aug-2024
https://doi.org/10.1109/TCSS.2023.3312182
Zheng PDou YYan Y(2024)Sensing the diversity of rumors: Rumor detection with hierarchical prototype contrastive learningInformation Processing & Management10.1016/j.ipm.2024.10383261:6(103832)Online publication date: Nov-2024
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Sharma USingh J(2024)A comprehensive overview of fake news detection on social networksSocial Network Analysis and Mining10.1007/s13278-024-01280-314:1Online publication date: 24-Jun-2024
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Arunthavachelvan KRaza SDing C(2024)A deep neural network approach for fake news detection using linguistic and psychological featuresUser Modeling and User-Adapted Interaction10.1007/s11257-024-09413-1Online publication date: 28-Jul-2024
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