Recognising user identity in twitter social networks via text mining
Date
2013
Authors
Keretna, S.
Hossny, A.
Creighton, D.
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Conference paper
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Conference proceedings / IEEE International Conference on Systems, Man, and Cybernetics. IEEE International Conference on Systems, Man, and Cybernetics, 2013, pp.3079-3082
Statement of Responsibility
Sara Keretna, Ahmad Hossny, Doug Creighton
Conference Name
2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2013) (13 Oct 2013 - 16 Oct 2013 : Manchester, UK)
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.
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© 2013 IEEE