Keywords
fake news
fake news detection
traditional
media
data set
covid-19
social media
fake news detection
traditional
media
data set
covid-19
social media
How to Cite
Md. Ziaur Rahman Shamim, Shaheena Sultana, Anika Tabassum, Israt Tabassum, & Sarkar Binoyee Farha. (2022). Fake News Detection: Covid-19 Perspective. Global Journal of Computer Science and Technology, 22(C2), 1–12. Retrieved from https://gjcst.com/index.php/gjcst/article/view/191
Abstract
The development of social media has con- tributed 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
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2022 Authors and Global Journals Private Limited