Title: The impact on society of false news spreading on social media with the help of predictive modelling

Authors: Riktesh Srivastava; Jitendra Singh Rathore; Sachin Kumar Srivastava; Khushboo Agnihotri

Addresses: City University College of Ajman, Sheikh Ammar Road – Al Tallah 2, Ajman, UAE ' Faculty of Management Studies, Banasthali University, Vanasthali Road, Dist., Vanasthali, Rajasthan 304022, India ' IILM Academy of Higher Learning, Lucknow, India ' Amity Business School, Amity University, Amity Rd., Sector 125, Noida, Uttar Pradesh 201301, India

Abstract: Nowadays, the interaction on social media for the latest news is an excellent source of information. Most of the time we read online news that may primarily appear authentic, but we cannot assure it because it does not happen every time. According to Gartner's published report, by 2022, most mature economies will get fake information than the correct information, mainly through social media. Fake news is one of the prevalent threats in our digitally linked world. This paper proposes a model for recognising fake news through the dataset from the Kaggle. There was 3,000 news collected from various social media sources in the dataset, of which 2,725 news is a training dataset and 275 for the test dataset. The fake and real news is classified and compared using five machine learning classification algorithms and analysed accordingly. The five classification algorithms are support vector machine (SVM), naïve Bayes, logistic regression, random forest, and neural networks.

Keywords: support vector machine; SVM; naïve Bayes; logistic regression; random forest; neural networks; classification accuracy; CA; precision; recall; F-1 score.

DOI: 10.1504/IJKL.2022.126266

International Journal of Knowledge and Learning, 2022 Vol.15 No.4, pp.307 - 318

Received: 26 Nov 2021
Accepted: 15 Jan 2022
Published online: 18 Oct 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article