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 – ten – fifteen – twenty - thirty and thirty four of these 34 authors are selected each time. Since recurrent artificial neural networks are more stable and iterations converge more quickly, in this work this architecture is preferred. In general, ANNs are more successful in distinguishing two classes, therefore for N authors, N×N neural networks are trained for pair wise classification. These N×N experts then organized as N special teams (CANNT) to aggregate decisions of these N×N experts. 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
The results offered for the necessity of a plenary method which allows the application of the data context and process it irrespective of its multimodality and further a system which tolerates the lack regarding availability for all author data during training
To classify tweets authored by five authors, 25 pairwise recurrent artificial neural networks are trained, five of which are dummy that are indifferent between +1, and -1
To classify tweets authored by twenty-twenty tive-Thirtythirty four authors, for N is the number of authors,N×N recurrent artificial neural networks are trained, N of which are dummy that are indifferent between votes +1, and -1
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 rates. They compared style makers (SM) feature sets and bag-of-words (BOW) feature sets and informed that SM features are more effective that 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.
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