COVID-19 Fake News Detection Using Ensemble-Based Deep Learning Model
IT Professional
; 24(2):32-37, 2022.
Article
in English
| ProQuest Central | ID: covidwho-1831852
ABSTRACT
Fake news on various medicines, foods, and vaccinations relating to the COVID-19 pandemic has increased dramatically. These fake news reports lead individuals to believe in false and sometimes harmful claims and stories, and they also influence people’s vaccination opinions. Immediately detecting COVID-19 false news can help to reduce the spread of fear, confusion, and potential health risks among citizens. An ensemble-based deep learning model for detecting COVID-19-related fake news on Twitter is proposed in this article. CT-BERT, BERTweet, and roberta are three different models that are fine-tuned on COVID-19-linked text data to separate fake and authentic news. In addition, the proposed ensemble-based model is compared to a variety of standard machine learning and deep learning models. In the detection of COVID-19 fake news from Twitter, the proposed ensemble-based deep learning model achieved state-of-the-art performance with a weighted $F_1$F1-score of 0.99.
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Language:
English
Journal:
IT Professional
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS