Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques

被引：248

作者：

Ahmed, Hadeer ^{[1
]}

Traore, Issa ^{[1
]}

Saad, Sherif ^{[2
]}

机构：

[1] Univ Victoria, ECE Dept, Victoria, BC, Canada

[2] Univ Windsor, Sch Comp Sci, Windsor, ON, Canada

来源：

INTELLIGENT, SECURE, AND DEPENDABLE SYSTEMS IN DISTRIBUTED AND CLOUD ENVIRONMENTS (ISDDC 2017) | 2017年 / 10618卷

关键词：

Online fake news; Text classification; Online social network security; Fake news detection; N-gram analysis;

D O I：

10.1007/978-3-319-69155-8_9

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Fake news is a phenomenon which is having a significant impact on our social life, in particular in the political world. Fake news detection is an emerging research area which is gaining interest but involved some challenges due to the limited amount of resources (i.e., datasets, published literature) available. We propose in this paper, a fake news detection model that use n-gram analysis and machine learning techniques. We investigate and compare two different features extraction techniques and six different machine classification techniques. Experimental evaluation yields the best performance using Term Frequency-Inverted Document Frequency (TF-IDF) as feature extraction technique, and Linear Support Vector Machine (LSVM) as a classifier, with an accuracy of 92%.

引用

页码：127 / 138

页数：12