Asia Pacific Journal of Information Systems (APJIS)

menu05_sub01_ov.gif

Share this page

Past Issue

Date	December 2022
Vol. No.	Vol. 32 No. 4
DOI	https://doi.org/10.14329/apjis.2022.32.4.945
Page	945~963
Title	Detecting Fake News about COVID-19 Infodemic Using Deep Learning and Content Analysis
Author	Olga Chernyaeva, Taeho Hong, YongHee Kim, YoungKi Park, Gang Ren, Jisoo Ock
Keyword	COVID-19, Infodemic, Fake News Detection, Machine Learning, Topic Modeling, Linguistic Analysis
Abstract	With the widespread use of social media, online social platforms like Twitter have become a place of rapid dissemination of information―both accurate and inaccurate. After the COVID-19 outbreak, the overabundance of fake information and rumours on online social platforms about the COVID-19 pandemic has spread over society as quickly as the virus itself. As a result, fake news poses a significant threat to effective virus response by negatively affecting people’s willingness to follow the proper public health guidelines and protocols, which makes it important to identify fake information from online platforms for the public interest. In this research, we introduce an approach to detect fake news using deep learning techniques, which outperform traditional machine learning techniques with a 93.1% accuracy. We then investigate the content differences between real and fake news by applying topic modeling and linguistic analysis. Our results show that topics on Politics and Government services are most common in fake news. In addition, we found that fake news has lower analytic and authenticity scores than real news. With the findings, we discuss important academic and practical implications of the study.

Home l Site Map l Abstracting/Indexing l FAQ l Publisher l Contact Us l Admin Login

© 2013 The Korean Society of Management Information Systems. All rights reserved.