Fake News Detection system using Decision Tree algorithm and compare textual property with Support Vector Machine algorithm

Krishna, N. Leela Siva Rama; Adimoolam, M.

doi:10.1109/icbats54253.2022.9758999

Cited by 8 publications

(1 citation statement)

References 24 publications

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“…Therefore, this study reviews approaches for fake news classification based on machine learning classifiers, such as decision trees, support vector machines (SVM), and Naïve Bayes. In [16] , the researchers propose a fake news detection system that uses the www.ijacsa.thesai.org decision tree algorithm to classify the news from two sources. Then, they compare the results against the result obtained with the SVM algorithm showing that the results obtained with the decision tree are more accurate than with SVM, with an accuracy of 97.67% and a precision of 94.60% against the SVM results of 91.74% in accuracy and 90.12% in precision.…”

Section: Related Workmentioning

confidence: 99%

Fake News Classification Web Service for Spanish News by using Artificial Neural Networks

Moreno-Vallejo¹,

Bastidas-Guacho²,

Moreno-Costales³

et al. 2023

IJACSA

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The use of digital media, such as social networks, has promoted the spreading of fake news on a large scale. Therefore, several Machine Learning techniques, such as artificial neural networks, have been used for fake news detection and classification. These techniques are widely used due to their learning capabilities. Besides, models based on artificial neural networks can be easily integrated into social media and websites to spot fake news early and avoid their propagation. Nevertheless, most fake news classification models are available only for English news, limiting the possibility of detecting fake news in other languages, such as Spanish. For this reason, this study proposes implementing a web service that integrates a deep learning model for the classification of fake news in Spanish. To determine the best model, the performance of several neural network architectures, including MLP, CNN, and LSTM, was evaluated using the F1 score., and LSTM using the F1 score. The LSTM architecture was the best, with an F1 score of 0.746. Finally, the efficiency of web service was evaluated, applying temporal behavior as a metric, resulting in an average response time of 1.08 seconds.

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Section: Related Workmentioning

confidence: 99%