Bilingual Fake News Detection Algorithm Using Naïve Bayes and Support Vector Machine Models

Billones, Paolo Joshua R.; Macasaet, Dailyne; Arenas, Shearyl U.

doi:10.1109/iet-iceta56553.2022.9971596

Cited by 1 publication

(1 citation statement)

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“…Some researchers have proposed methods that work with news in other languages different than English. For instance, Billones et al [28] train ML models with a dataset of news in Filipino and compare the performance of the models with models trained with English news. The results show that the models perform better on the English dataset than on the other language dataset due to the limitation of labeled data amount in other languages.…”

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%