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

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.

Transfer learning and GRU-CRF augmentation for Covid-19 fake news detection

“…The social engagements on articles can be a significant feature for fake news detection (to find the semantic relationship between news articles and writers) [23]. In the Fake News Detection research field, many datasets can be used, such as PolitiFact [24,25], Fake News Kaggle [18,26], The Fake News Challenge (FNC-1) [27,28], and Constraint@AAAI2021 -COVID19 Fake News Detection [9,10,12,11]. Ahmad et al [18] developed Fake News Detection Using Machine Learning Ensemble Methods consists of Logistic Regression, Support Vector Machine, Random Forest (RF), etc.…”

“…Several previous studies have contributed to this Constraint @ AAAI2021 -COVID19 Fake News Detection in English shared task. Azhan et al [10] apply a Layer Differentiated training procedure for training a pre-trained ULMFiT, Kakwani et al [11] compile the IndicGLUE benchmark for language, Baris et al [12] propose a modeling framework for those features by using BERT language model and external sources. Considering the number of researches utilizing the dataset, we think it crucial to contribute to this shared-task by using another approach.…”