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ISSN 2063-5346
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TO IMPROVE ACCURACY TO DETECT FAKE NEWS IN SOCIAL MEDIA USING DECISION TREES COMPARED OVER NAIVE BAYES ALGORITHM

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V. Lakshmi Narayana, A. Gayathri
» doi: 10.31838/ecb/2023.12.sa1.404

Abstract

Aim: The Machine Learning Algorithms to Detect Fake News in Social Media to discover the best accuracy in determining which news is fake and which is true. Decision Trees and Naive Bayes (NB) are two techniques for detecting anomalies. Materials and Methods: The dataset for the false news identification was collected from kaggle. .The two groups are Novel Based Decision Trees (N=10) and Naive Bayes (N=10). As known, keeping of G-power and minimum power of the analysis is fixed as 80% and maximum accepted error is fixed as 0.5 with threshold value as 0.0805% and Confidence Interval is 95%. Results: The Novel Decision Trees Detection Algorithm has been found to be useful in detecting fake news. The accuracy of the Decision Trees algorithm is (84.00%), whereas the accuracy of the Naive Bayes technique is (72.40%). These two algorithms are used to improve the detection of fake news. Furthermore, the independent significant value p=0.0496 (p<0.05) was met, i.e. alpha is 0.01 with a 95% confidence level. Conclusion: The Novel Decision Trees Detection Algorithm looks to outperform Naive Bayes when it comes to recognising fake news on social media.

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