Abstract
The growing popularity of online news has prompted concerns regarding (i) the socio-political influence over news dissemination, (ii) the waning freedom of news media, (iii) and a facile news evaluation process. A piece of news having the power to capture a large audience and sow the seed of bizarre consequences on a national scale should be prudently evaluated before reaching the mass. In quest of making a substantial profit, and sometimes due to inevitable socio-political influence, news with biased heading outpours mass media, resulting in ambiguity and mass manipulation. In this paper, we suggest a blockchain, smart contract, and incremental machine learning-based news evaluation procedure for the Bengali language to overcome these challenges. Weighted synthesis of machine classification and human expert opinion in a decentralized platform are synthesized to evaluate news. With continuous data, the Natural Language Processing (NLP) model is incrementally trained, and the best version of the model is used to detect deprived fake news. During experiments, the NLP model with initial training and testing accuracy of 84.94% and 84.99% was increased to 93.75% and 93.80% after nine rounds of incremental model training. On the Ethereum test network, the protocols have been installed and tested. The simulation demonstrates successful implementation of our proposed system.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
