Covid-19 fake news classification using deep learning | |
| Author | Koirala, Abhishek |
| Subject(s) | Machine learning Fake news |
| Note | A research study submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Computer Science |
| Publisher | Asian Institute of Technology |
| Abstract | False and deceptive news has been around for a very long time. False stories have been used to fame or defame people, destroying real facts and events, creating propaganda, and causing biases among people to create social, and economic disturbance. However, only in recent times have people fully understood the far-reaching impact of deceptive news. The impact of fake news on the 2016 US presidential election, and BREXIT has been widely discussed by many researchers. Researchers have thus turned towards finding the best approaches to detect and classify fake news in real time before it goes to the general public. Researchers generally focus on one particular domain at a time, because natural language is complex system and generalizing over multiple domains to obtain a common approach is not the best way to begin with. I chose COVID-19 as my domain because in the early days of the pandemic, there were a large number of false stories circulating among the public creating chaos and even discrimination in society. This research study discusses various approaches to fake news classification in the COVID-19 new domain using machine learning and deep learning |
| Year | 2020 |
| Type | Research Study Project Report (RSPR) |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Computer Science (CS) |
| Chairperson(s) | Dailey, Mathew N.; |
| Examination Committee(s) | Phan Minh Dung;Chaklam Silpasuwanchai; |
| Scholarship Donor(s) | AIT Fellowship; |
| Degree | Research Studies Project Report (M. Eng.) - Asian Institute of Technology, 2020 |