INFORMATION CHANGE THE WORLD

International Journal of Education and Management Engineering(IJEME)

ISSN: 2305-3623 (Print), ISSN: 2305-8463 (Online)

Published By: MECS Press

IJEME Vol.12, No.4, Aug. 2022

K-Nearest Neighbors Bayesian Approach to False News Detection from Text on Social Media

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Author(s)

Ogunsuyi Opeyemi J., Adebola K. OJO

Index Terms

False News/Information Detection, K-Nearest Neighbours, Bayesian, Word2Vector, Term Frequency-Inverse Document Frequency.

Abstract

Social media usage has increased due to the rate at which technologies are emerging and it is less likely to detect false news/information manually as it aims to capture the human mind. The spread of false news can cause havoc; therefore, detection of false news becomes paramount where almost everyone has access to social media. Our proposed system optimizes the false news detection process. The system combines advantages of two textual feature extraction methods and two machine learning algorithms for text classification. Basic pre-processing methods were employed. Feature extraction was carried out using Term Frequency-Inverse Document Frequency with Word2Vector. K-Nearest Neighbour (KNN) and Naïve Bayes (NB) algorithms are combined to give KNN Bayesian. The most available systems made use of a single feature extraction method but in our system, two feature extraction methods are combined. The evaluation metrics used were accuracy, precision, recall, f1score and KNN Bayesian performed better than KNN. To further evaluate our model, the Area under the Curve-Receiver Operator Characteristics (AUC-ROC) revealed that AUC of KNN Bayesian ROC curve is higher than that of KNN.

Cite This Paper

Ogunsuyi Opeyemi J., Adebola K. OJO, "K-Nearest Neighbors Bayesian Approach to False News Detection from Text on Social Media", International Journal of Education and Management Engineering (IJEME), Vol.12, No.4, pp. 22-32, 2022. DOI:10.5815/ijeme.2022.04.03

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