ProBlock: a novel approach for fake news detection.

Blockchain Fake news ProBit model Probabilistic model

Journal

Cluster computing
ISSN: 1386-7857
Titre abrégé: Cluster Comput
Pays: Netherlands
ID NLM: 101580721

Informations de publication

Date de publication:
2021
Historique:
received: 17 07 2020
revised: 14 05 2021
accepted: 11 07 2021
pubmed: 10 8 2021
medline: 10 8 2021
entrez: 9 8 2021
Statut: ppublish

Résumé

The world is diving deeper into the digital age, and the sources of first information are moving towards social media and online news portals. The chances of being misinformed increase multifold as our reliance on sources of information are getting ambiguous. Traditional news sources followed strict codes of practice to verify stories, whereas today, users can upload news items on social media and unverified portals without proving their veracity. The absence of any determinants of such news articles' truthfulness on the Internet calls for a novel approach to determine the realness quotient of unverified news items by leveraging technology. This study presents a dynamic model with a secure voting system, where news reviewers can provide feedback on news, and a probabilistic mathematical model is used for predicting the truthfulness of the news item based on the feedback received. A blockchain-based model, ProBlock is proposed; so that correctness of information propagated is ensured.

Identifiants

pubmed: 34366702
doi: 10.1007/s10586-021-03361-w
pii: 3361
pmc: PMC8335474
doi:

Types de publication

News

Langues

eng

Pagination

3779-3795

Informations de copyright

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.

Références

Int J Environ Res Public Health. 2020 Jan 07;17(2):
pubmed: 31936087

Auteurs

Eishvak Sengupta (E)

Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India.

Renuka Nagpal (R)

Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India.

Deepti Mehrotra (D)

Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India.

Gautam Srivastava (G)

Department of Mathematics and Computer Science, Brandon University, Brandon, R7A 6A9 Canada.
Research Center for Interneural Computing, China Medical University, Taichung, 40402 Taiwan, Republic of China.

Classifications MeSH