Fake News Detection using Machine Learning and Deep Learning Hybrid Algorithms

Title

Fake News Detection using Machine Learning and Deep Learning Hybrid Algorithms

Creator

Saha A.; Thomas K.T.

Description

Spreading misinformation or fake news for personal, political, or financial gain has become very common these days. The influence of this misinformation on peoples opinions can be significant, i.e., the 2016 presidential election in the United States was a perfect illustration of how false news may be used to deceive people. In todays fast-paced world, automatic detection of fake news has become an importantrequirement. In this paper, multiple machine learning algorithms have been implemented to perform classification. A proposition of a hybrid architecture consisting of CNN along with LSTM has also been made. The proposed model outperforms the other traditional approaches. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes in Networks and Systems, Vol-462, pp. 447-456.

Date

2022-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Convolutional neural networks; Fake news; Hybrid model; Long short-term memory

Coverage

Saha A., Department of Data Science, Christ (Deemed to be University), Bangalore, India; Thomas K.T., Department of Data Science, Christ (Deemed to be University), Bangalore, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981192210-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-19-2211-4_40

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135067536&doi=10.1007%2f978-981-19-2211-4_40&partnerID=40&md5=97dbdcbdc9db64878174301d3c1700b6