Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/108779
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Type: Conference paper
Title: Recognising user identity in twitter social networks via text mining
Author: Keretna, S.
Hossny, A.
Creighton, D.
Citation: Proceedings: 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013, 2013 / pp.3079-3082
Publisher: IEEE
Issue Date: 2013
Series/Report no.: IEEE International Conference on Systems Man and Cybernetics Conference Proceedings
ISBN: 9780769551548
ISSN: 1062-922X
Conference Name: 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2013) (13 Oct 2013 - 16 Oct 2013 : Manchester, UK)
Statement of
Responsibility: 
Sara Keretna, Ahmad Hossny, Doug Creighton
Abstract: 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, gtraining 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.
Keywords: Text mining; identity recognition; social networks; machine learning
Rights: © 2013 IEEE
RMID: 0030046251
DOI: 10.1109/SMC.2013.525
Appears in Collections:Mathematical Sciences publications

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