ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information

• URL: http://arxiv.org/abs/2101.05499v1
• Date: Thu, 14 Jan 2021 08:39:50 GMT
• Title: ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information
• Authors: Ipek Baris and Zeyd Boukhers
• Abstract summary: Social media platforms are vulnerable to fake news dissemination. This paper analyzes the impact of incorporating content information, prior knowledge, and credibility of sources into models for the early detection of fake news.
• Score: 1.6752182911522522
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Social media platforms are vulnerable to fake news dissemination, which causes negative consequences such as panic and wrong medication in the healthcare domain. Therefore, it is important to automatically detect fake news in an early stage before they get widely spread. This paper analyzes the impact of incorporating content information, prior knowledge, and credibility of sources into models for the early detection of fake news. We propose a framework modeling those features by using BERT language model and external sources, namely Simple English Wikipedia and source reliability tags. The conducted experiments on CONSTRAINT datasets demonstrated the benefit of integrating these features for the early detection of fake news in the healthcare domain.

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