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ORIGINAL ARTICLE
WEIGHTED ACCURACY ALGORITHMIC APPROACH IN COUNTERACTING FAKE NEWS AND DISINFORMATION
1
School of Economics, School of Law and Intellectual Property, Zhejiang Gongshang University, China
Submission date: 2020-08-01
Acceptance date: 2021-02-19
Online publication date: 2021-03-31
Publication date: 2021-03-31
Corresponding author
Kwadwo Osei Bonsu
School of Economics, School of Law and Intellectual Property, Zhejiang Gongshang University, 18 Xuezheng St, Jianggan District, Hangzhou, Zheji, 310018, Hangzhou, China
School of Economics, School of Law and Intellectual Property, Zhejiang Gongshang University, 18 Xuezheng St, Jianggan District, Hangzhou, Zheji, 310018, Hangzhou, China
Economic and Regional Studies 2021;14(1):99–107
KEYWORDS
artificial intelligencenatural language processingmachine learning algorithmfake newsdisinformationdigital revolution
TOPICS
ABSTRACT
Subject and purpose of work: Fake news and disinformation are polluting information
environment. Hence, this paper proposes a methodology for fake news detection through the
combined weighted accuracies of seven machine learning algorithms. Materials and methods: This paper uses natural language processing to analyze the text content
of a list of news samples and then predicts whether they are FAKE or REAL. Results: Weighted accuracy algorithmic approach has been shown to reduce overfitting. It was
revealed that the individual performance of the different algorithms improved after the data
was extracted from the news outlet websites and 'quality' data was filtered by the constraint
mechanism developed in the experiment. Conclusions: This model is different from the existing mechanisms in the sense that it automates
the algorithm selection process and at the same time takes into account the performance of all the
algorithms used, including the less performing ones, thereby increasing the mean accuracy of all
the algorithm accuracies.
PEER REVIEW INFORMATION
Article has been screened for originality
iThenticate
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