COVID-19 Fake News Detection on Social Media
6th International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering, IC4ME2 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1874263
ABSTRACT
Since the outbreak of COVID-19, social media plays an important role to circulate pandemic news around the world. Some malevolent users may take an advantage of this and spread fake news to attract people for business and research purposes. In this paper, we take an approach by applying existing machine learning algorithms to detect fake news in social media and show a comparison of their performances. In our study, the support vector classifier (SVC) outperforms the rest of the classifiers based on different statistical metrics. Therefore, the SVC classifier has been considered as our proposed classifier model to identify fake COVID-19 news in social media. Two word clouds have also been generated based on the appearance of words in the news that shows an insignificant difference between true and fake news. © 2021 IEEE.
COVID-19; fake news; machine learning; social media; Deep learning; Fake detection; Learning algorithms; Static Var compensators; Support vector machines; Classifier models; Machine learning algorithms; Performance; Research purpose; Support vector classifiers; Word clouds; Social networking (online)
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
6th International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering, IC4ME2 2021
Year:
2021
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS