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

Autor:	Fayez Gebali, Mohamed K. Elhadad, Kin Fun Li
Rok vydání:	2019
Předmět:	Set (abstract data type) Metadata Measure (data warehouse) Information retrieval Recall Market segmentation Computer science Content (measure theory) In real life Fake news
Zdroj:	Advances on P2P, Parallel, Grid, Cloud and Internet Computing ISBN: 9783030335083 3PGCIC
DOI:	10.1007/978-3-030-33509-0_86
Popis:	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].
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::63fbec1ad1fbd4db7329b9ff5ccb7e11 https://doi.org/10.1007/978-3-030-33509-0_86 Zobrazit plný text záznamu