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Erschienen in:
A Fuzzy-Based Peer Coordination Quality System in Mobile P2P Networks: Effect of Time for Finishing Required Task (TFRT) Parameter Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

A Novel Approach for Selecting Hybrid Features from Online News Textual Metadata for Fake News Detection

verfasst von : Mohamed K. Elhadad, Kin Fun Li, Fayez Gebali

Erschienen in: Advances on P2P, Parallel, Grid, Cloud and Internet Computing

Verlag: Springer International Publishing

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Abstract

Nowadays, online news platforms have become the main sources of news for many users. Hence, an urgent need arises to find a way to classify this news automatically and measure its validity to avoid spreading fake news. In this paper, we tried to simulate how humans, in real life, are dealing with news documents. We introduced a new way in which we can deal with the whole textual content of the news documents by extracting a number of characteristics of those texts and extracting a complex set of other metadata related features without segmenting the news documents into parts (title, content, date, source, etc.). Performances of nine machine learning algorithms in terms of Accuracies, Precision, Recall and F1-score are compared when using three different datasets obtaining much better result than the results in [1] and [2].

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Vorheriges Kapitel An Architecture for System Recovery Based on Solution Records on Different Servers
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Metadaten
Titel
A Novel Approach for Selecting Hybrid Features from Online News Textual Metadata for Fake News Detection
verfasst von
Mohamed K. Elhadad
Kin Fun Li
Fayez Gebali
Copyright-Jahr
2020
Buch
Advances on P2P, Parallel, Grid, Cloud and Internet Computing

Print ISBN: 978-3-030-33508-3

Electronic ISBN: 978-3-030-33509-0

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-33509-0_86