An Explainable Fake News Detector Based on Named Entity Recognition and Stance Classification Applied to COVID-19

Giorgio De Magistris,Samuele Russo,Janusz T Starczewski,Paolo Roma,Christian Napoli

doi:10.3390/info13030137

Giorgio De Magistris, Samuele Russo + Show 3 more

Open Access

https://doi.org/10.3390/info13030137

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Abstract

Over the last few years, the phenomenon of fake news has become an important issue, especially during the worldwide COVID-19 pandemic, and also a serious risk for the public health. Due to the huge amount of information that is produced by the social media such as Facebook and Twitter it is becoming difficult to check the produced contents manually. This study proposes an automatic fake news detection system that supports or disproves the dubious claims while returning a set of documents from verified sources. The system is composed of multiple modules and it makes use of different techniques from machine learning, deep learning and natural language processing. Such techniques are used for the selection of relevant documents, to find among those, the ones that are similar to the tested claim and their stances. The proposed system will be used to check medical news and, in particular, the trustworthiness of posts related to the COVID-19 pandemic, vaccine and cure.

Highlights

Nowadays social media has reshaped the mass communication ecosystem
During the still ongoing worldwide COVID-19 pandemic, fake news have become a serious risk for the public health, especially when false or misleading information is spread regarding the nature of this illness and its cure
We used deep language models in order to encode the documents into a feature vector space, a deep convolutional neural network is employed to classify the stances of two documents

Summary

Introduction

Nowadays social media has reshaped the mass communication ecosystem. Individual news creators with no confirmed reputation can reach wide audiences on news networks because of the absence of the verification of data, such as third-party filtering [1]. Fake news can be defined as the deliberate presentation of false and misleading claims as real facts [2]. They may cause serious impact and even damage to society since they may be intentionally forged to manipulate the orientation of people regarding important themes. We believe that explanations are needed in order to convince people about the mendacity of a claim For this reason, we developed a system that leaves the final judgment to the user, presenting them all the evidence, rather than being a straightforward binary classifier. We used deep language models in order to encode the documents into a feature vector space, a deep convolutional neural network is employed to classify the stances of two documents

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