Lies Kill, Facts Save: Detecting COVID-19 Misinformation in Twitter

被引:87
作者
Al-Rakhami, Mabrook S. [1 ,2 ]
Al-Amri, Atif M. [1 ,3 ]
机构
[1] King Saud Univ, Res Chair Pervas & Mobile Comp, Riyadh 11543, Saudi Arabia
[2] King Saud Univ, Coll Comp & Informat Sci, Informat Syst Dept, Riyadh 11543, Saudi Arabia
[3] King Saud Univ, Coll Comp & Informat Sci, Software Engn Dept, Riyadh 11543, Saudi Arabia
关键词
Twitter; Diseases; Feature extraction; Tools; Machine learning; Organizations; Classification; COVID-19; machine learning; misinformation; SOCIAL MEDIA; AGREEMENT; HEALTH;
D O I
10.1109/ACCESS.2020.3019600
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Online social networks (ONSs) such as Twitter have grown to be very useful tools for the dissemination of information. However, they have also become a fertile ground for the spread of false information, particularly regarding the ongoing coronavirus disease 2019 (COVID-19) pandemic. Best described as an infodemic, there is a great need, now more than ever, for scientific fact-checking and misinformation detection regarding the dangers posed by these tools with regards to COVID-19. In this article, we analyze the credibility of information shared on Twitter pertaining the COVID-19 pandemic. For our analysis, we propose an ensemble-learning-based framework for verifying the credibility of a vast number of tweets. In particular, we carry out analyses of a large dataset of tweets conveying information regarding COVID-19. In our approach, we classify the information into two categories: credible or non-credible. Our classifications of tweet credibility are based on various features, including tweet- and user-level features. We conduct multiple experiments on the collected and labeled dataset. The results obtained with the proposed framework reveal high accuracy in detecting credible and non-credible tweets containing COVID-19 information.
引用
收藏
页码:155961 / 155970
页数:10
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