research-article

Proactive Discovery of Fake News Domains from Real-Time Social Media Feeds

Authors:

Juliana FreireAuthors Info & Claims

WWW '20: Companion Proceedings of the Web Conference 2020

Pages 584 - 592

https://doi.org/10.1145/3366424.3385772

Published: 20 April 2020 Publication History

Abstract

The proliferation of web sites that disseminate fake news is a growing problem in our society. Not surprisingly, the problem of identifying whether a web page contains fake news has attracted substantial attention. However, the problem of discovering new sources of fake news has been largely unexplored. Timely discovery of such sources is critical to combat misinformation and minimize its potential harm. In this paper, we present an automatic discovery system that proactively surfaces fake news domains before they are flagged by humans. Our system operates in two-steps: first, it uses Twitter feeds to uncover user co-sharing structures to discover political websites; then it uses a topic-agnostic classifier to score and rank newly discovered domains. To demonstrate the effectiveness of our system, we conduct an experimental evaluation in which we collect tweets related to the 2020 presidential impeachment process in the United States, and show that not only our system is able to discover new sites, but that a large percentage of these sites are indeed publishing fake news. We also design an integrated user interface to support fact-checkers and leverage their knowledge. Through this interface, fact-checkers can visualize domain interaction networks, query domain fakeness score, and tag incorrectly predicted results. Our proactive discovery system will expedite fact-checking process and can be a powerful weapon in the toolbox to combat misinformation.

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https://dl.acm.org/doi/10.1145/3637528.3671539
Gao XWang XChen ZZhou WHoi S(2024)Knowledge Enhanced Vision and Language Model for Multi-Modal Fake News DetectionIEEE Transactions on Multimedia10.1109/TMM.2023.333029626(8312-8322)Online publication date: 2024
https://doi.org/10.1109/TMM.2023.3330296
Zhang HLi ZLiu SHuang TNi ZZhang JLv Z(2024)Do Sentence-Level Sentiment Interactions Matter? Sentiment Mixed Heterogeneous Network for Fake News DetectionIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.326909011:4(5090-5100)Online publication date: Aug-2024
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Index Terms

Proactive Discovery of Fake News Domains from Real-Time Social Media Feeds

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

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Published In

cover image ACM Conferences

WWW '20: Companion Proceedings of the Web Conference 2020

April 2020

854 pages

ISBN:9781450370240

DOI:10.1145/3366424

Editors:
Amal El Fallah Seghrouchni
Sorbonne University, France
,
Gita Sukthankar
University of Central Florida, United States
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Copyright © 2020 ACM.

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Published: 20 April 2020

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WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

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Cited By

Reuben MFriedland LPuzis RGrinberg NBaeza-Yates RBonchi F(2024)Leveraging Exposure Networks for Detecting Fake News SourcesProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671539(5635-5646)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671539
Gao XWang XChen ZZhou WHoi S(2024)Knowledge Enhanced Vision and Language Model for Multi-Modal Fake News DetectionIEEE Transactions on Multimedia10.1109/TMM.2023.333029626(8312-8322)Online publication date: 2024
https://doi.org/10.1109/TMM.2023.3330296
Zhang HLi ZLiu SHuang TNi ZZhang JLv Z(2024)Do Sentence-Level Sentiment Interactions Matter? Sentiment Mixed Heterogeneous Network for Fake News DetectionIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.326909011:4(5090-5100)Online publication date: Aug-2024
https://doi.org/10.1109/TCSS.2023.3269090
Wu DTan ZZhao HJiang TGeng N(2024)Domain- and category-style clustering for general fake news detection via contrastive learningInformation Processing & Management10.1016/j.ipm.2024.10372561:4(103725)Online publication date: Jul-2024
https://doi.org/10.1016/j.ipm.2024.103725
Papadogiannakis EPapadopoulos PP. Markatos EKourtellis N(2023)Who Funds Misinformation? A Systematic Analysis of the Ad-related Profit Routines of Fake News SitesProceedings of the ACM Web Conference 202310.1145/3543507.3583443(2765-2776)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583443
Bajpai SSharma D(2022)A Review on Identification of Unreliability and Fakeness in Social Media Posts using Blockchain Technology2022 9th International Conference on Computing for Sustainable Global Development (INDIACom)10.23919/INDIACom54597.2022.9763221(831-837)Online publication date: 23-Mar-2022
https://doi.org/10.23919/INDIACom54597.2022.9763221
Gupta AKumar NPrabhat PGupta RTanwar SSharma GBokoro PSharma R(2022)Combating Fake News: Stakeholder Interventions and Potential SolutionsIEEE Access10.1109/ACCESS.2022.319367010(78268-78289)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3193670
Mishra SSinha HMitra TSahoo M(2022)I Hardly Lie: A Multistage Fake News Detection SystemBiologically Inspired Techniques in Many Criteria Decision Making10.1007/978-981-16-8739-6_23(253-261)Online publication date: 4-Jun-2022
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Li YJi KMa KChen ZWu JLi YXu G(2022)HACK: A Hierarchical Model for Fake News DetectionWeb Information Systems Engineering – WISE 202110.1007/978-3-030-90888-1_43(565-572)Online publication date: 1-Jan-2022
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Pelrine KDanovitch JRabbany R(2021)The Surprising Performance of Simple Baselines for Misinformation DetectionProceedings of the Web Conference 202110.1145/3442381.3450111(3432-3441)Online publication date: 19-Apr-2021
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