Statistical Twitter spam detection demystified: performance, stability and scalability

Lin, Guanjun, Sun, Nan, Nepal, Surya, Zhang, Jun, Xiang, Yang and Hassan, Houcine 2017, Statistical Twitter spam detection demystified: performance, stability and scalability, IEEE access, vol. 5, pp. 11142-11154, doi: 10.1109/ACCESS.2017.2710540.

Attached Files
Name Description MIMEType Size Downloads

Title Statistical Twitter spam detection demystified: performance, stability and scalability
Author(s) Lin, Guanjun
Sun, Nan
Nepal, Surya
Zhang, JunORCID iD for Zhang, Jun orcid.org/0000-0002-2189-7801
Xiang, YangORCID iD for Xiang, Yang orcid.org/0000-0001-5252-0831
Hassan, Houcine
Journal name IEEE access
Volume number 5
Start page 11142
End page 11154
Total pages 13
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2017-06-01
ISSN 2169-3536
Keyword(s) Computer Science
Computer Science, Information Systems
Engineering
Engineering, Electrical & Electronic
Feature extraction
Machine learning algorighms
Parallel computing
Real-time systems
Scalability
Science & Technology
Spam detection
Technology
Telecommunications
Twitter
Machine learning
Language eng
DOI 10.1109/ACCESS.2017.2710540
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2017, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30100389

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: 96 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Wed, 26 Jul 2017, 15:36:50 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.