基于Naïve贝叶斯和支持向量机模型的双语假新闻检测算法

IF 1.3 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IET Networks Pub Date : 2022-10-14 DOI:10.1109/IET-ICETA56553.2022.9971596

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

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引用次数: 1

摘要

本研究旨在通过探索使用朴素贝叶斯和SGD分类器模型预测英语或菲律宾文章是真还是假的可行性，来减轻对虚假新闻的吸收。这是通过通过大型预处理数据集训练模型来完成的。经过评估，两种模型的准确率分别达到93%和95%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

This study aims to mitigate the absorption of fraudulent news by exploring the feasibility of using Naive Bayes and SGD classifier models in predicting whether the English or Filipino article is real or fake. This is accomplished by training the models through large pre-processed datasets. After evaluation, both models have achieved an accuracy of 93% and 95% accuracy respectively.

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来源期刊

IET Networks COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

5.00

自引率

0.00%

发文量

审稿时长

33 weeks

期刊介绍： IET Networks covers the fundamental developments and advancing methodologies to achieve higher performance, optimized and dependable future networks. IET Networks is particularly interested in new ideas and superior solutions to the known and arising technological development bottlenecks at all levels of networking such as topologies, protocols, routing, relaying and resource-allocation for more efficient and more reliable provision of network services. Topics include, but are not limited to: Network Architecture, Design and Planning, Network Protocol, Software, Analysis, Simulation and Experiment, Network Technologies, Applications and Services, Network Security, Operation and Management.