Seybold Report ISSN: 1533-9211
Prajakta Vilas Chiddarwar
Bharati Vidyapeeth (Deemed to be University), College of Engineering, Pune, chiddarwarprajakta711@gmail.com
Dr. Sunita Dhotre
Associate Professor, Department of Computer Engineering, Bharati Vidyapeeth (Deemed to be University), College of Engineering, Pune
Vol 17, No 08 ( 2022 ) | DOI: 10.5281/zenodo.7012733 | Licensing: CC 4.0 | Pg no: 552-564 | Published on: 20-08-2022
Abstract
Today’s generation is living in the 21’st century where social media platform is growing rapidly. Increase in the involvement of social media, the number of gateways is getting open to entertaining the user. Many online websites, web applications, and android applications are available over the online platform. Because of these, the credibility of information is introduced on the social media platform. Also fake news, Misinformation, and irrelevant information are critical issues raised on the social platform. Because of these, there is rapid growth in using social platforms like Facebook, Whatsapp, Twitter, Blogs, and many more. Due to this ease of access, people nowadays getting interested in the platform, due to this dissemination of information leads people to spread and search more information.
The dissemination of information may have many ways like fake news, irrelevant data, etc . the impact of this information vary from very low to high, according to the importance of information or search ratio of information. The fake news may contain low-quality data or fake links that spread over a wide or any message to one another. This news can be spread intensively to create an attackable environment to affect the end-user or to damage the status of politicians. Online social media has several fake accounts and that leads to the spread of fake news on the platform. The end-user who is using social media is unaware of the validation and credibility of information which leads to spreading of the fake news from one end to another user.
Keywords:
fake news, trust analysis, the credibility of information, machine learning