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An Efficient and Accurate Detection of Fake News Using Capsule Transient Auto Encoder

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Published:16 June 2023Publication History
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Abstract

Fake news is “news reports that are deliberatively and indisputably fake.” News that uses fake information is becoming a threat. It becomes challenging for humans to distinguish between fake and actual news. It has become necessary to detect fake news, which seeks to determine whether a news document can be believed. Detection of fake news faces challenges in accurate classification, making existing detection algorithms ineffective. In these issues, this article uses a novel Adaptive Capsule Transient Auto Encoder (ACTAE) for effectively detecting fake news. ACTAE is a combined approach of a classifier named Capsule Auto Encoder and an algorithm called Adaptive Transient Search Optimization Algorithm. The overall detection process is performed in various stages, including preprocessing, feature withdrawal, feature selection, and classification and optimization of weight parameters of the classifier for better results. The overall process is executed in Python, proving that ACTAE detects fake news with higher accuracy (99%) and lower error rate.

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        cover image ACM Transactions on Asian and Low-Resource Language Information Processing
        ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 22, Issue 6
        June 2023
        635 pages
        ISSN:2375-4699
        EISSN:2375-4702
        DOI:10.1145/3604597
        Issue’s Table of Contents

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        Publication History

        • Published: 16 June 2023
        • Online AM: 1 April 2023
        • Accepted: 14 March 2023
        • Revised: 1 February 2023
        • Received: 26 August 2022
        Published in tallip Volume 22, Issue 6

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