Recognising user identity in twitter social networks via text mining

Keretna, Sara, Hossny, Ahmad and Creighton, Doug 2013, Recognising user identity in twitter social networks via text mining, in SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics, IEEE, Piscataway, N.J., pp. 3079-3082.

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Title Recognising user identity in twitter social networks via text mining
Author(s) Keretna, Sara
Hossny, Ahmad
Creighton, Doug
Conference name IEEE Systems, Man and Cybernetics. Conference (2013 : Manchester, England)
Conference location Manchester, England
Conference dates 13-16 Oct. 2013
Title of proceedings SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics
Editor(s) [Unknown]
Publication date 2013
Conference series IEEE Systems, Man and Cybernetics Conference
Start page 3079
End page 3082
Total pages 4
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) text mining
identity recognition
social networks
machine learning
Summary Social networks have become a convenient and effective means of communication in recent years. Many people use social networks to communicate, lead, and manage activities, and express their opinions in supporting or opposing different causes. This has brought forward the issue of verifying the owners of social accounts, in order to eliminate the effect of any fake accounts on the people. This study aims to authenticate the genuine accounts versus fake account using writeprint, which is the writing style biometric. We first extract a set of features using text mining techniques. Then, training of a supervised machine learning algorithm to build the knowledge base is conducted. The recognition procedure starts by extracting the relevant features and then measuring the similarity of the feature vector with respect to all feature vectors in the knowledge base. Then, the most similar vector is identified as the verified account.
ISBN 9781479906529
9780769551548
Language eng
Field of Research 080107 Natural Language Processing
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30058810

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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