Belief Index for Fake COVID19 Text Detection

Abstract

An increase in news articles on various communication platforms or social media has resulted in higher possibilities of spread of non-factual or fake information. The overall volume and veracity of news makes it even more impossible to manually fact check each data and label them as true or false. Under such circumstances, we propose a belief index generator model that quantifies the belief to be associated with any random information making use of text analytic proximity measures. In the initial feature engineering, we use a modified TF-IDF algorithm. Post generation of word embeddings, various distance measures have been proposed and compared as possible belief scores. The analysis has been carried out using 50K research articles on CoVid-19 to validate truths and The CoronaVirusFacts/DatosCoronaVirus Alliance Database to validate falsities in random CoVid related information.