Result: Raising a Model for Fake News Detection Using Machine Learning in Python

Title:
Raising a Model for Fake News Detection Using Machine Learning in Python
Authors:
Agudelo, Gerardo, Parra, Octavio, Velandia, Julio
Contributors:
Universidad Distrital Francisco Jose de Caldas [Bogota], Department of Systems and Industrial Engineering [Bogotá], Faculty of Engineering [Bogotá], Universidad Distrital Francisco Jose de Caldas [Bogota]-Universidad Distrital Francisco Jose de Caldas [Bogota], Salah A. Al-Sharhan, Antonis C. Simintiras, Yogesh K. Dwivedi, Marijn Janssen, Matti Mäntymäki, Luay Tahat, Issam Moughrabi, Taher M. Ali, Nripendra P. Rana, TC 6, WG 6.11
Source:
17th Conference on e-Business. :596-604
Publisher Information:
HAL CCSD; Springer International Publishing, 2018.
Publication Year:
2018
Collection:
collection:IFIP-LNCS
collection:IFIP
collection:IFIP-TC
collection:IFIP-WG
collection:IFIP-TC6
collection:IFIP-WG6-11
collection:IFIP-I3E
collection:IFIP-LNCS-11195
Subject Terms:
Fake news, Machine learning, NLTK, Sklearn, [INFO]Computer Science [cs], [INFO.INFO-NI]Computer Science [cs], Networking and Internet Architecture [cs.NI]
Subject Geographic:
Kuwait City, Kuwait
Original Identifier:
HAL: hal-02274166
Document Type:
Conference conferenceObject<br />Conference papers
Language:
English
Relation:
info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-030-02131-3_52
DOI:
10.1007/978-3-030-02131-3_52
Access URL:
https://hal.inria.fr/hal-02274166
https://hal.inria.fr/hal-02274166/document
https://hal.inria.fr/hal-02274166/file/474698_1_En_52_Chapter.pdf
Rights:
info:eu-repo/semantics/OpenAccess
URL: http://creativecommons.org/licenses/by/
Accession Number:
edshal.hal.02274166v1
Database:
HAL

Further Information

Fake news has been spreading in greater numbers and has generated more and more misinformation, one of the clearest examples being the United States presidential elections of 2016, for which a lot of false information was circulated before the votes that improved the image of Donald Trump overs Hilary’s Clinton (Singh et al. n.d.). Because fake news is too much, it becomes necessary to use computational tools to detect them; this is why the use of algorithms of Machine Learning like “CountVectorizer”, “TfidfVectorizer”, a Naive Bayes Model and natural language processing for the identification of false news in public data sets is proposed.