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Analysing and detecting twitter spam

Chen, Chao 2016, Analysing and detecting twitter spam, PhD thesis, School of Information Technology, Deakin University.

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Title Analysing and detecting twitter spam
Author Chen, Chao
Institution Deakin University
School School of Information Technology
Faculty Faculty of Science, Engineering and Built Environment
Degree type Research doctorate
Degree name PhD
Thesis advisor Xiang, YangORCID iD for Xiang, Yang orcid.org/0000-0001-5252-0831
Zhang, JunORCID iD for Zhang, Jun orcid.org/0000-0002-2189-7801
Date submitted 2016-09
Keyword(s) spam detection
online social networks
spamming strategies
spam drift
Summary Through in-depth data-drive analysis, we provide insights on deceptive information in Twitter spam, spammers' behaviours and emerging spamming strategies. We also firstly identify and solve the "spam drift" problem. Online social network providers can adopt our findings and proposed scheme to re-design their detection system to improve its efficiency and accuracy.
Language eng
Field of Research 080503 Networking and Communications
Socio Economic Objective 890101 Fixed Line Data Networks and Services
Description of original xvi, 168 pages : illustrations, tables, graphs, some coloured
Copyright notice ┬ęThe Author. All Rights Reserved.
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30103065

Document type: Thesis
Collections: Higher degree theses (Open Access)
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Created: Fri, 06 Oct 2017, 13:26:59 EST by Asif, Yasmin

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.