research-article

Multi-Label Fake News Detection using Multi-layered Supervised Learning

Authors:

Tayyaba Rasool,

Wasi Haider Butt,

Arslan Shaukat,

M. Usman AkramAuthors Info & Claims

ICCAE 2019: Proceedings of the 2019 11th International Conference on Computer and Automation Engineering

Pages 73 - 77

https://doi.org/10.1145/3313991.3314008

Published: 23 February 2019 Publication History

Abstract

Rapid spreading of misinformation is a growing worldwide concern as it has the capacity to greatly influence individual reputation and societal behavior. The consequences of unchecked spreading of misinformation can not only vary from political to financial but also effect global opinion for a long time. Thus, detecting fake news is important but challenging as the ability to accurately categorize certain information as true or fake is limited even in human. Moreover, fake news are a blend of correct news and false information making accurate classification even more confusing. In this paper, we propose a novel method of multilevel multiclass fake news detection based on relabeling of the dataset and learning iteratively. The proposed method outperforms the benchmark and our experiments indicate that profile of the source of information contributes the most in fake news detection.

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Cited By

Nawaz MNawaz MFournier-Viger PHe Y(2024)Analysis and Classification of Fake News Using Sequential Pattern MiningBig Data Mining and Analytics10.26599/BDMA.2024.90200157:3(942-963)Online publication date: Sep-2024
https://doi.org/10.26599/BDMA.2024.9020015
Tummala SPriya SMadeswaran D(2024)Advanced Fake News Detection in Social Media Using Machine Learning Models and Natural Language ProceessingSSRN Electronic Journal10.2139/ssrn.4763161Online publication date: 2024
https://doi.org/10.2139/ssrn.4763161
Gunasekaran VKarthikeyan D(2024)Real Time Fake news Detection Web App Enhanced by Machine Learning Algorithms2023 4th International Conference on Intelligent Technologies (CONIT)10.1109/CONIT61985.2024.10627483(1-7)Online publication date: 21-Jun-2024
https://doi.org/10.1109/CONIT61985.2024.10627483
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Index Terms

Multi-Label Fake News Detection using Multi-layered Supervised Learning
1. Computing methodologies
  1. Artificial intelligence

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cover image ACM Other conferences

ICCAE 2019: Proceedings of the 2019 11th International Conference on Computer and Automation Engineering

February 2019

160 pages

ISBN:9781450362870

DOI:10.1145/3313991

Copyright © 2019 ACM.

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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The University of Western Australia, Department of Electronic Engineering, University of Western Australia
University of Melbourne: University of Melbourne
Macquarie University-Sydney

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 February 2019

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ICCAE 2019

ICCAE 2019: 2019 11th International Conference on Computer and Automation Engineering

February 23 - 25, 2019

WN, Perth, Australia

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Cited By

Nawaz MNawaz MFournier-Viger PHe Y(2024)Analysis and Classification of Fake News Using Sequential Pattern MiningBig Data Mining and Analytics10.26599/BDMA.2024.90200157:3(942-963)Online publication date: Sep-2024
https://doi.org/10.26599/BDMA.2024.9020015
Tummala SPriya SMadeswaran D(2024)Advanced Fake News Detection in Social Media Using Machine Learning Models and Natural Language ProceessingSSRN Electronic Journal10.2139/ssrn.4763161Online publication date: 2024
https://doi.org/10.2139/ssrn.4763161
Gunasekaran VKarthikeyan D(2024)Real Time Fake news Detection Web App Enhanced by Machine Learning Algorithms2023 4th International Conference on Intelligent Technologies (CONIT)10.1109/CONIT61985.2024.10627483(1-7)Online publication date: 21-Jun-2024
https://doi.org/10.1109/CONIT61985.2024.10627483
Aljrees T(2024)Improving Prediction of Arabic Fake News Using ELMO’s Features-Based Tri-Ensemble Model and LIME XAIIEEE Access10.1109/ACCESS.2024.339229712(63066-63076)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3392297
kh. Alnabrisi Ikh. Saad M(2024)Detect Arabic fake news through deep learning models and TransformersExpert Systems with Applications10.1016/j.eswa.2024.123997251(123997)Online publication date: Oct-2024
https://doi.org/10.1016/j.eswa.2024.123997
Varalakshmi KKumar P(2024)A late fusion framework using whale optimization technique and attention-BiLSTM for fake news detectionInternational Journal of Data Science and Analytics10.1007/s41060-024-00515-y18:3(275-294)Online publication date: 18-Mar-2024
https://doi.org/10.1007/s41060-024-00515-y
Ramesh Reddy BTejaswi TNaveen PGiresh JVamsi B(2023)Optimising the Detection of Fake News in Multilingual Exposition using Machine Learning Techniques2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS)10.1109/ICICCS56967.2023.10142901(158-165)Online publication date: 17-May-2023
https://doi.org/10.1109/ICICCS56967.2023.10142901
Singh MAhmed JAlam MRaghuvanshi KKumar S(2023)A comprehensive review on automatic detection of fake news on social mediaMultimedia Tools and Applications10.1007/s11042-023-17377-483:16(47319-47352)Online publication date: 26-Oct-2023
https://doi.org/10.1007/s11042-023-17377-4
Suryawanshi SGoswami APatil P(2023)FakeIDCA: Fake news detection with incremental deep learning based concept drift adaptionMultimedia Tools and Applications10.1007/s11042-023-16588-z83:10(28579-28594)Online publication date: 6-Sep-2023
https://doi.org/10.1007/s11042-023-16588-z
Narang PSingh AMonga H(2023)Opinion-Based Machine Learning Approach for Fake News ClassificationProceedings of International Conference on Intelligent Vision and Computing (ICIVC 2022)10.1007/978-3-031-31164-2_4(33-42)Online publication date: 1-May-2023
https://doi.org/10.1007/978-3-031-31164-2_4
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