COVID-19 related fake news detection model
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
| Autores principales: | , , , |
|---|---|
| Otros Autores: | |
| Formato: | Tesis |
| Lenguaje: | English |
| Publicado: |
Brac University
2021
|
| Materias: | |
| Acceso en línea: | http://hdl.handle.net/10361/15432 |
| id |
10361-15432 |
|---|---|
| record_format |
dspace |
| spelling |
10361-154322022-01-26T10:19:58Z COVID-19 related fake news detection model Shondhy, Sumaiya Islam Khan, Forhad Ahmed Ibrahim, Syed Shoaib Barua, Shuvajit Parvez, Mohammad Zavid Kabir, Ashad Akhond, Mostafijur Rahman Department of Computer Science and Engineering, Brac University COVID-19 Fake news detection Dataset news article COVID-19 (Disease) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 55-56). In this era of developed information and technology, any sort of information runs faster than air. The reliability of the information can be tricky at times. Some news publishing sources can publish news that are actually misguiding. The drastic evolution of electronic media over the past couple of decades has fueled the spread of fake news causing confusion and misunderstanding among the mass regarding any topic. The main motive behind producing these fake news is to create an agenda or to spread trepidation among people. People tend to become more panicked during any kind of disaster or pandemic, this it is easier to make them believe these misinformation in these times. Likewise, COVID-19 pandemic is not out of the grasp of misinformation spreading. To tackle this, we have proposed a Fake News Prediction model that will be used to detect fake news regarding COVID-19 that are being circulated in different electronic media. Sumaiya Islam Shondhy Forhad Ahmed Khan Syed Shoaib Ibrahim Shuvajit Barua B. Computer Science 2021-10-19T06:07:51Z 2021-10-19T06:07:51Z 2021 2021-01 Thesis ID 17101532 ID 17301083 ID 17301144 ID 17301168 http://hdl.handle.net/10361/15432 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 56 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
COVID-19 Fake news detection Dataset news article COVID-19 (Disease) |
| spellingShingle |
COVID-19 Fake news detection Dataset news article COVID-19 (Disease) Shondhy, Sumaiya Islam Khan, Forhad Ahmed Ibrahim, Syed Shoaib Barua, Shuvajit COVID-19 related fake news detection model |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Parvez, Mohammad Zavid |
| author_facet |
Parvez, Mohammad Zavid Shondhy, Sumaiya Islam Khan, Forhad Ahmed Ibrahim, Syed Shoaib Barua, Shuvajit |
| format |
Thesis |
| author |
Shondhy, Sumaiya Islam Khan, Forhad Ahmed Ibrahim, Syed Shoaib Barua, Shuvajit |
| author_sort |
Shondhy, Sumaiya Islam |
| title |
COVID-19 related fake news detection model |
| title_short |
COVID-19 related fake news detection model |
| title_full |
COVID-19 related fake news detection model |
| title_fullStr |
COVID-19 related fake news detection model |
| title_full_unstemmed |
COVID-19 related fake news detection model |
| title_sort |
covid-19 related fake news detection model |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15432 |
| work_keys_str_mv |
AT shondhysumaiyaislam covid19relatedfakenewsdetectionmodel AT khanforhadahmed covid19relatedfakenewsdetectionmodel AT ibrahimsyedshoaib covid19relatedfakenewsdetectionmodel AT baruashuvajit covid19relatedfakenewsdetectionmodel |
| _version_ |
1814309049778307072 |