Supervised Learning for Fake News Detection

被引：198

作者：

Reis, Julio C. S. ^{[1
]}

Correia, Andre ^{[2
]}

Murai, Fabricio ^{[3
]}

Veloso, Adriano ^{[1
]}

Benevenuto, Fabricio ^{[3
]}

机构：

[1] Univ Fed Minas Gerais, Comp Sci, Belo Horizonte, MG, Brazil

[2] Univ Fed Minas Gerais, Informat Syst, Belo Horizonte, MG, Brazil

[3] Univ Fed Minas Gerais, Comp Sci Dept, Belo Horizonte, MG, Brazil

来源：

IEEE INTELLIGENT SYSTEMS | 2019年 / 34卷 / 02期

关键词：

SENTIMENT ANALYSIS;

D O I：

10.1109/MIS.2019.2899143

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A large body of recent works has focused on understanding and detecting fake news stories that are disseminated on social media. To accomplish this goal, these works explore several types of features extracted from news stories, including source and posts from social media. In addition to exploring the main features proposed in the literature for fake news detection, we present a new set of features and measure the prediction performance of current approaches and features for automatic detection of fake news. Our results reveal interesting findings on the usefulness and importance of features for detecting false news. Finally, we discuss how fake news detection approaches can be used in the practice, highlighting challenges and opportunities.

引用

页码：76 / 81

页数：6

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