Authorship Authentication of Short Messages from Social Networks Machines

Nesibe Merve Demir, Mehmet Can


Dataset consists of 17000 tweets collected from Twitter, as 500 tweets for each of 34 authors that meet certain criteria. Raw data is collected by using the software Nvivo. The collected raw data is preprocessed to extract frequencies of 200 features. In the data analysis 128 of features are eliminated since they are rare in tweets. As a progressive presentation, five – fifteen – twenty – twenty five – thirty and thirty four of these authors are selected each time. Since recurrent artificial neural networks are more stable and in general ANNs are more successful distinguishing two classes, for N authors, N×N neural networks are trained for pair wise classification. These experts then organized in N competing teams (CANNT) to aggregate decisions of these NXN experts. Then this procedure is repeated seven times and committees with seven members voted for final decision. By a commonest type voting, the accuracy is boosted around ten percent. Number of authors is seen not so effective on the accuracy of the authentication, and around 80% accuracy is achieved for any number of authors.


Authorship Authentication; short massages; committee machines; recurrent neural network

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Copyright (c) 2018 Nesibe Merve Demir, Mehmet Can

ISSN 2233 -1859

Digital Object Identifier DOI: 10.21533/scjournal

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This work is licensed under a Creative Commons Attribution 4.0 International License