Abstract
500 tweets from Twitterare collectedby using the software Nvivo, fromeach of 34 authorsthat meet certain criteria. Dataset consists of 17000 tweets is preprocessed to extract frequencies of 72 features. Since artificial neural networks are more successful distinguishing two classes, for N authors, N×N neural networks are trained for pair wise classification. These experts then organized as N special competing teams (CANNT) to aggregate decisions of these NXN experts. Then to improve the accuracy of author authentication, a novel technique, batch identification is used and up to100% accuracy is achieved.
Highlights
INTRODUCTIONGreen, and Sheppard(2013) focused on messages collected from Twitter to analyze most effective feature sets for authorship verification
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
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
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. Ada boost classifier received the best results with 84% accuracy for 5 authors
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
