research-article

FakeFinder: Twitter Fake News Detection on Mobile

Authors:

Lin Tian,

Xiuzhen Zhang,

Min PengAuthors Info & Claims

WWW '20: Companion Proceedings of the Web Conference 2020

Pages 79 - 80

https://doi.org/10.1145/3366424.3382706

Published: 20 April 2020 Publication History

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Abstract

Misinformation, or fake news, spreads quickly on the social media platform Twitter. Mobile devices are widely used to read Twitter posts. A mobile app that can detect fake news from the live Twitter stream and alert users in real time is an effective way to contain the spread of misinformation on Twitter. Towards this objective, the prediction model needs to be small to achieve fast prediction. In this paper, we design and develop a fake news detection mobile app with a device-based prediction model based on the small language model ALBERT. Experiments show that it can achieve real-time, accurate detection of fake news.

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Cited By

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Tufchi SYadav AAhmed T(2023)A comprehensive survey of multimodal fake news detection techniques: advances, challenges, and opportunitiesInternational Journal of Multimedia Information Retrieval10.1007/s13735-023-00296-312:2Online publication date: 23-Aug-2023
https://doi.org/10.1007/s13735-023-00296-3
Pirani RMohammed E(2023)Efficient Fake News Detection Method Using Feature Reduction5th International Conference on Wireless, Intelligent and Distributed Environment for Communication10.1007/978-3-031-33242-5_1(1-14)Online publication date: 30-Jul-2023
https://doi.org/10.1007/978-3-031-33242-5_1
Truică CApostol E(2022)MisRoBÆRTa: Transformers versus MisinformationMathematics10.3390/math1004056910:4(569)Online publication date: 12-Feb-2022
https://doi.org/10.3390/math10040569
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Index Terms

FakeFinder: Twitter Fake News Detection on Mobile

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

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Published In

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

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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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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Tufchi SYadav AAhmed T(2023)A comprehensive survey of multimodal fake news detection techniques: advances, challenges, and opportunitiesInternational Journal of Multimedia Information Retrieval10.1007/s13735-023-00296-312:2Online publication date: 23-Aug-2023
https://doi.org/10.1007/s13735-023-00296-3
Pirani RMohammed E(2023)Efficient Fake News Detection Method Using Feature Reduction5th International Conference on Wireless, Intelligent and Distributed Environment for Communication10.1007/978-3-031-33242-5_1(1-14)Online publication date: 30-Jul-2023
https://doi.org/10.1007/978-3-031-33242-5_1
Truică CApostol E(2022)MisRoBÆRTa: Transformers versus MisinformationMathematics10.3390/math1004056910:4(569)Online publication date: 12-Feb-2022
https://doi.org/10.3390/math10040569
Alzubi SAwaysheh F(2022)EdgeFNF: Toward Real-time Fake News Detection on Mobile Edge Computing2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC)10.1109/FMEC57183.2022.10062503(1-3)Online publication date: 12-Dec-2022
https://doi.org/10.1109/FMEC57183.2022.10062503
Jiang YPorter M(2022)Simulating Fake News Dissemination on Twitter with Multivariate Hawkes Processes2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020285(3597-3606)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020285
Kaufhold MBayer MHartung DReuter C(2021)Design and Evaluation of Deep Learning Models for Real-Time Credibility Assessment in TwitterArtificial Neural Networks and Machine Learning – ICANN 202110.1007/978-3-030-86383-8_32(396-408)Online publication date: 7-Sep-2021
https://doi.org/10.1007/978-3-030-86383-8_32
Bozarth LBudak C(undefined)Market Forces: Quantifying the Role of Top Credible Ad Servers in the Fake News EcosystemSSRN Electronic Journal10.2139/ssrn.3650585
https://doi.org/10.2139/ssrn.3650585

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User Preference-aware Fake News Detection

Multidimensional Analysis of Fake News Spreaders on Twitter

Fake News Early Detection: A Theory-driven Model

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