Abstract
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.
Highlights
Normalization was done by dividing each value by the total word count of the corresponding text, in order to remove the influence of different overall text size.Feature vectors, created by extracting from Twitter messages, were used as input for modeling artificial neural network (ANN)
Since ANNs are more successful distinguishing two classes, for N authors, N×N neural networks are trained for pair wise classification
These experts organized as N special teams (CANNT) with N experts to aggregate decisions
Summary
Green, and Sheppard (2013) focused on messages collected from Twitter to analyze most effective feature sets for authorship verification They used sequential minimal optimization (SMO) algorithm included in Weka for classification 10 authors with 120 tweets from each and had 44% accuracy rate. They compared style makers (SM) feature sets and bag-of-words (BOW) feature sets and informed that SM features are more effective than BOW features for authorship verification. Obtained features were applied for training a linear SVM classifier for prediction of an unknown tweet's author Their results showed that if data number increased, better results were obtained. Ada boost classifier received the best results with 84% accuracy for 5 authors
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