Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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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.
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): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
The methods for personal identification and authentication are no exception.
Total Score: 105
Country: Bangladesh
Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering
Authors: Md. Ziaur Rahman Shamim, Shaheena Sultana, Anika Tabassum, Israt Tabassum, Sarkar Binoyee Farha (PhD/Dr. count: 0)
View Count (all-time): 265
Total Views (Real + Logic): 2651
Total Downloads (simulated): 18
Publish Date: 2026 01, Fri
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Neural Networks and Rules-based Systems used to Find Rational and
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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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