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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
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ROMANIA

Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

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  1/2024 - 7

Multidisciplinary Fusion Perspective Analysis Method for False Information Recognition

FAN, W. See more information about FAN, W. on SCOPUS See more information about FAN, W. on IEEExplore See more information about FAN, W. on Web of Science, WANG, Y. See more information about WANG, Y. on SCOPUS See more information about WANG, Y. on SCOPUS See more information about WANG, Y. on Web of Science
 
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Download PDF pdficon (1,551 KB) | Citation | Downloads: 307 | Views: 223

Author keywords
artificial intelligence, machine learning, support vector machines, social computing, natural language processing

References keywords
news(24), fake(18), detection(12), approach(7), social(6), networks(6), media(6), text(5), personality(5), learning(5)
No common words between the references section and the paper title.

About this article
Date of Publication: 2024-02-29
Volume 24, Issue 1, Year 2024, On page(s): 61 - 70
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2024.01007
SCOPUS ID: 85189456069

Abstract
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Combating misinformation is one of the urgent social crises. Much research has shown that disinformation can lead to social panic and adversely affect society. It is crucial to promptly detect and counteract misinformation to reduce its adverse effects. Although progress in text-based fact verification has been made, the community needs further exploration into the user-oriented results. To address this gap, we integrate theories from linguistics, journalism, psychology, and cognitive science. We propose a disinformation detection algorithm based on multidimensional content analysis. This algorithm combines human factors and user perception in text interactive media to establish six dimensions for comprehensive content analysis. We have proposed a quantitative calculation method corresponding to six dimensions to detect misinformation. The average accuracy of the proposed model test on four datasets is 95.28%. The results show that this algorithm can effectively analyze from a multidisciplinary theoretical perspective and effectively identify misinformation in Chinese and English.


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References Weight

Web of Science® Citations for all references: 5,955 TCR
SCOPUS® Citations for all references: 40,452 TCR

Web of Science® Average Citations per reference: 124 ACR
SCOPUS® Average Citations per reference: 843 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-04-25 20:41 in 269 seconds.




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