<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In the present era of the internet and social media, the way of information dissemination has changed. However, due to rapid growth in the amount of news generated regularly and the unsupervised nature of social media, fake news turns out to be a big problem. Fake news can easily build a false positive or negative perception about a person, or an event. Fake news was also used as a tool by propagandists during the Coronavirus (COVID-19) pandemic. Thus, there is a need to use technology to tag fake news and prevent its dissemination. Previously, different algorithms were designed to detect fake news but without considering the semantic meaning and long sentence dependence. This research work proposes a new approach to the detection of fake news in the context of COVID-19. The suggested approach uses a combination of Bidirectional Encoder Representations from Transformers (BERT) for extracting context meaning from sentences, SVM for pattern identification to detect fake news in a better way from the COVID-19 dataset, and an evolutionary algorithm called Non-dominated Sorting Genetic Algorithm II (NSGA-II) to distribute text for Support Vector Machine (SVM) classification. The suggested approach improves accuracy by 5.2 % by removing a certain amount of ambiguity from sentences.</p>
</abstract>

In the present era of the internet and social media, the way of information dissemination has changed. However, due to rapid growth in the amount of news generated regularly and the unsupervised nature of social media, fake news turns out to be a big problem. Fake news can easily build a false positive or negative perception about a person, or an event. Fake news was also used as a tool by propagandists during the Coronavirus (COVID-19) pandemic. Thus, there is a need to use technology to tag fake news and prevent its dissemination. Previously, different algorithms were designed to detect fake news but without considering the semantic meaning and long sentence dependence. This research work proposes a new approach to the detection of fake news in the context of COVID-19. The suggested approach uses a combination of Bidirectional Encoder Representations from Transformers (BERT) for extracting context meaning from sentences, SVM for pattern identification to detect fake news in a better way from the COVID-19 dataset, and an evolutionary algorithm called Non-dominated Sorting Genetic Algorithm II (NSGA-II) to distribute text for Support Vector Machine (SVM) classification. The suggested approach improves accuracy by 5.2 % by removing a certain amount of ambiguity from sentences.

An Improved FakeBERT for Fake News Detection

National University of Computer and Emerging Sciences

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"An Improved FakeBERT for Fake News Detection"}

In the present era of the internet and social media, the way of information dissemination has changed. However, due to rapid growth in the amount of news...

An Improved FakeBERT for Fake News Detection

Online veröffentlicht: 29. Jan. 2024

Seitenbereich: 180 - 188

DOI: https://doi.org/10.2478/acss-2023-0018

Schlüsselwörter
Covid-19, fake news, semantic analysis

© 2023 Arshad Ali et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

An Improved FakeBERT for Fake News Detection

Online veröffentlicht: 29. Jan. 2024

Seitenbereich: 180 - 188

DOI: https://doi.org/10.2478/acss-2023-0018

SchlüsselwörterCovid-19, fake news, semantic analysis

© 2023 Arshad Ali et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Covid-19, fake news, semantic analysis