research-article

Combating fake news: a data management and mining perspective

Authors:

Laks V. S. Lakshmanan,

Michael Simpson,

Saravanan ThirumuruganathanAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 12, Issue 12

Pages 1990 - 1993

https://doi.org/10.14778/3352063.3352117

Published: 01 August 2019 Publication History

Abstract

Fake news is a major threat to global democracy resulting in diminished trust in government, journalism and civil society. The public popularity of social media and social networks has caused a contagion of fake news where conspiracy theories, disinformation and extreme views flourish. Detection and mitigation of fake news is one of the fundamental problems of our times and has attracted widespread attention. While fact checking websites such as snopes, politifact and major companies such as Google, Facebook, and Twitter have taken preliminary steps towards addressing fake news, much more remains to be done. As an interdisciplinary topic, various facets of fake news have been studied by communities as diverse as machine learning, databases, journalism, political science and many more.

The objective of this tutorial is two-fold. First, we wish to familiarize the database community with the efforts by other communities on combating fake news. We provide a panoramic view of the state-of-the-art of research on various aspects including detection, propagation, mitigation, and intervention of fake news. Next, we provide a concise and intuitive summary of prior research by the database community and discuss how it could be used to counteract fake news. The tutorial covers research from areas such as data integration, truth discovery and fusion, probabilistic databases, knowledge graphs and crowdsourcing from the lens of fake news. Effective tools for addressing fake news could only be built by leveraging the synergistic relationship between database and other research communities. We hope that our tutorial provides an impetus towards such synthesis of ideas and the creation of new ones.

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Cited By

Jaidka KChen TChesterman SHsu WKan MKankanhalli MLee MSeres GSim TTaeihagh ATung AXiao XYue A(2024)Misinformation, Disinformation, and Generative AI: Implications for Perception and PolicyDigital Government: Research and Practice10.1145/3689372Online publication date: 23-Aug-2024
https://dl.acm.org/doi/10.1145/3689372
Chen ZHe LMukherjee ADragut EBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)Comquest: Large Scale User Comment Crawling and IntegrationCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654736(432-435)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3654736
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
https://dl.acm.org/doi/10.1145/3589335.3641256
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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 12, Issue 12

August 2019

547 pages

ISSN:2150-8097

Editors:
Lei Chen,
Fatma Özcan

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VLDB Endowment

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Published: 01 August 2019

Published in PVLDB Volume 12, Issue 12

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Cited By

Jaidka KChen TChesterman SHsu WKan MKankanhalli MLee MSeres GSim TTaeihagh ATung AXiao XYue A(2024)Misinformation, Disinformation, and Generative AI: Implications for Perception and PolicyDigital Government: Research and Practice10.1145/3689372Online publication date: 23-Aug-2024
https://dl.acm.org/doi/10.1145/3689372
Chen ZHe LMukherjee ADragut EBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)Comquest: Large Scale User Comment Crawling and IntegrationCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654736(432-435)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3654736
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
https://dl.acm.org/doi/10.1145/3589335.3641256
Trummer I(2022)From BERT to GPT-3 codexProceedings of the VLDB Endowment10.14778/3554821.355489615:12(3770-3773)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.14778/3554821.3554896
Olaleye TAbayomi-Alli AAdesemowo KArogundade OMisra SKose U(2022)RETRACTED ARTICLE: SCLAVOEM: hyper parameter optimization approach to predictive modelling of COVID-19 infodemic tweets using smote and classifier vote ensembleSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-06940-027:6(3531-3550)Online publication date: 15-Mar-2022
https://dl.acm.org/doi/10.1007/s00500-022-06940-0
Thirumuruganathan SSimpson MLakshmanan LLi GLi ZIdreos SSrivastava D(2021)To Intervene or Not To Intervene: Cost based Intervention for Combating Fake NewsProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3452778(2300-2309)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3452778
Meesad P(2021)Thai Fake News Detection Based on Information Retrieval, Natural Language Processing and Machine LearningSN Computer Science10.1007/s42979-021-00775-62:6Online publication date: 23-Aug-2021
https://dl.acm.org/doi/10.1007/s42979-021-00775-6
Bagade APale ASheth SAgarwal MChakrabarti SChebrolu KSudarshan SVarma VKambhampati SBhattacharya ANatarajan SRoy R(2020)The Kauwa-Kaate Fake News Detection SystemProceedings of the 7th ACM IKDD CoDS and 25th COMAD10.1145/3371158.3371402(302-306)Online publication date: 5-Jan-2020
https://dl.acm.org/doi/10.1145/3371158.3371402

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