Fake News Detection: Covid-19 Perspective

Fake News Detection: Covid-19 Perspective

Md. Ziaur Rahman Shamim

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Shaheena Sultana

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Anika Tabassum

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Israt Tabassum

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Sarkar Binoyee Farha

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Notre Dame University Bangladesh

Fake News Detection: Covid-19 Perspective

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Abstract

The development of social media has contributed to a remarkable rise in the spread of fake news. Today people rely more on online news outlets. The chance of receiving fake news on an online platform is high. As we went through a pandemic and the Covid-19 was the most absorbing topic of 2020, much news on Covid-19 was published every day in traditional media and social media. Among that news, some are fake. In this work, we have collected a new dataset for detecting fake news from traditional media on Covid-19. We have gathered more than 3000 pieces of news from traditional media out of the 170 are fake ones that were collected from fact-checking sites. Then we have tested the existing four classification algorithms with our dataset using Count Vectorizer and TF-IDF. We have merged 170 fake news with four scales of true news and analyzed the outcome.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Md. Ziaur Rahman Shamim. 2026. \u201cFake News Detection: Covid-19 Perspective\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 22 (GJCST Volume 22 Issue C2).

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GJCST Volume 22 Issue C2

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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GJCST-C Classification DDC Code: 004.678 LCC Code: TK5105.875.I57

Submission ReceivedFebruary 2, 2022
Peer Review Double Blind
Handling Editor
Accepted February 8, 2022
Published July 19, 2022

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July 19, 2022

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