<abstract xmlns="http://www.w3.org/1999/xhtml"><bold>Subject and purpose of work:</bold> 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.<bold>Materials and methods:</bold> 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.<bold>Results:</bold> 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.<bold>Conclusions:</bold> 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.</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.

Weighted Accuracy Algorithmic Approach in Counteracting Fake News and Disinformation

Economic and Regional Studies / Studia Ekonomiczne i Regionalne

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Subject and purpose of work: Fake news and disinformation are polluting information environment. Hence, this paper proposes a methodology for fake news...

Weighted Accuracy Algorithmic Approach in Counteracting Fake News and Disinformation

Online veröffentlicht: 31. März 2021

Seitenbereich: 99 - 107

Eingereicht: 01. Aug. 2020

Akzeptiert: 01. Feb. 2021

DOI: https://doi.org/10.2478/ers-2021-0007

Schlüsselwörter
artificial intelligence, natural language processing, machine learning algorithm, fake news, disinformation, digital revolution

© 2021 Kwadwo Osei Bonsu, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Weighted Accuracy Algorithmic Approach in Counteracting Fake News and Disinformation

Online veröffentlicht: 31. März 2021

Seitenbereich: 99 - 107

Eingereicht: 01. Aug. 2020

Akzeptiert: 01. Feb. 2021

DOI: https://doi.org/10.2478/ers-2021-0007

Schlüsselwörterartificial intelligence, natural language processing, machine learning algorithm, fake news, disinformation, digital revolution

© 2021 Kwadwo Osei Bonsu, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
artificial intelligence, natural language processing, machine learning algorithm, fake news, disinformation, digital revolution