Role of Contextual Features in Fake News Detection: A Review

被引：3

作者：

George, Joma ^{[1
]}

Skariah, Shintu Mariam ^{[1
]}

Xavier, Aleena T. ^{[1
]}

机构：

[1] Amal Jyothi Coll Engn, Dept Comp Sci & Engn, Kanjirappally, Kerala, India

来源：

2020 INTERNATIONAL CONFERENCE ON INNOVATIVE TRENDS IN INFORMATION TECHNOLOGY (ICITIIT) | 2020年

关键词：

Fake news detection; Contextual information; Machine Learning; Deep Learning; Multi-head self-attention;

D O I：

10.1109/icitiit49094.2020.9071524

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Deceptive news has an intention to defame a person, an institution or an organization. It often has a catastrophic effect on the views of the readers. With the advancement of technology, social media and online sites are made so accessible that, it acts as a catalyst for spreading fake news. Identification of such misinformation is a challenging task even for humans. But with Deep learning and Machine learning techniques, there have been efforts to solve this problem. In this paper the influence of linguistic characteristics and contextual features in fake news detection are analyzed and certain techniques like Naive Bayes, KNN, SVM, Decision tree, Hybrid CNN, CMS etc. are compared.

引用

页数：6

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