Lies Kill, Facts Save: Detecting COVID-19 Misinformation in Twitter.
Lies Kill, Facts Save: Detecting COVID-19 Misinformation in Twitter.
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DOI：
10.1109/access.2020.3019600
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Al-Amri AM
中科院分区：
文献类型：
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作者：
Al-Rakhami MS;Al-Amri AM
诸如Twitter之类的在线社交网络（ONS）已经发展成为非常有用的信息传播工具。然而，它们也成为传播虚假信息的沃土，特别是关于正在进行的2019冠状病毒病（COVID-19）大流行的信息。最好的描述是信息流行病，现在比以往任何时候都更需要对这些工具对COVID-19造成的危险进行科学的事实核查和错误信息检测。在本文中，我们分析了Twitter上分享的有关COVID-19大流行的信息的可信度。对于我们的分析，我们提出了一个基于集成学习的框架来验证大量推文的可信度。特别是，我们对传达COVID-19相关信息的大型推文数据集进行了分析。在我们的方法中，我们将信息分为两类：可信或不可信。我们对推文可信度的分类基于各种特征，包括推文和用户级别的特征。我们对收集和标记的数据集进行了多次实验。使用该框架获得的结果表明，在检测包含COVID-19信息的可信和不可信推文时具有很高的准确性。
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.