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

TL;DR

This paper presents ECOL, a framework that combines content analysis, prior knowledge, and source credibility using BERT and external sources to improve early fake news detection in healthcare social media.

Contribution

It introduces a novel approach integrating multiple features with BERT and external knowledge sources for early fake news detection.

Findings

Enhanced detection accuracy with combined features

Effective use of external knowledge sources

Improved early detection in healthcare domain

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.