Improving fake news detection using K-means and support vector machine approaches

Yazdi, Kasra Majbouri, Yazdi, Adel Majbouri, Khodayi, Saeid, Hou, Jingyu, Zhou, Wanlei and Saedy, Saeed 2020, Improving fake news detection using K-means and support vector machine approaches, in ICCNSIP 2020 : Proceedings of the International Conference on Communications, Networking, Signal and Image Processing, World Academy of Science, Engineering and Technology, [Melbourne, Vic.], pp. 38-42, doi: 10.5281/zenodo.3669287.

Attached Files
Name Description MIMEType Size Downloads

Title Improving fake news detection using K-means and support vector machine approaches
Author(s) Yazdi, Kasra Majbouri
Yazdi, Adel Majbouri
Khodayi, Saeid
Hou, JingyuORCID iD for Hou, Jingyu orcid.org/0000-0002-6403-9786
Zhou, WanleiORCID iD for Zhou, Wanlei orcid.org/0000-0002-1680-2521
Saedy, Saeed
Conference name Communications, Networking, Signal and Image Processing. Conference (2020 : Melbourne, Victoria)
Conference location Melbourne, Victoria
Conference dates 3-4 Feb. 2020
Title of proceedings ICCNSIP 2020 : Proceedings of the International Conference on Communications, Networking, Signal and Image Processing
Publication date 2020
Start page 38
End page 42
Total pages 5
Publisher World Academy of Science, Engineering and Technology
Place of publication [Melbourne, Vic.]
Keyword(s) Fake news detection
feature selection
support vector machine
K-means clustering
machine learning
social media
Language eng
DOI 10.5281/zenodo.3669287
Indigenous content off
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30135109

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 37 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Tue, 18 Feb 2020, 15:47:18 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.