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The rapid growth of social networks has produced an unprecedented amount of user-generated data, which provides an excellent opportunity for text mining. Authorship analysis, an important part of text mining, attempts to learn about the author of the text through subtle variations in the writing styles that occur between gender, age and social groups. Such information has a variety of applications including advertising and law enforcement. One of the most accessible sources of user-generated data is Twitter, which makes the majority of its user data freely available through its data access API. In this study we seek to identify the gender of users on Twitter using Perceptron and Naïve Bayes with selected 1 through 5-gram features from tweet text. Stream applications of these algorithms were employed for gender prediction to handle the speed and volume of tweet traffic. Because informal text, such as tweets, cannot be easily evaluated using traditional dictionary methods, n-gram features were implemented in this study to represent streaming tweets. The large number of 1 through 5-grams requires that only a subset of them be used in gender classification, for this reason informative n-gram features were chosen using multiple selection algorithms. In the best case the Naïve Bayes and Perceptron algorithms produced accuracy, balanced accuracy, and F-measure above 99%.

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With the rapid growth of web-based social networking technologies in recent years, author identification and analysis have proven increasingly useful. Authorship analysis provides information about a document's author, often including the author's gender. Men and women are known to write in distinctly different ways, and these differences can be successfully used to make a gender prediction. Making use of these distinctions between male and female authors, this study demonstrates the use of a simple stream-based neural network to automatically discriminate gender on manually labeled tweets from the Twitter social network. This neural network, the Modified Balanced Winnow, was employed in two ways; the effectiveness of data stream mining was initially examined with an extensive list of n-gram features. Feature selection techniques were then evaluated by drastically reducing the feature list using WEKA's attribute selection algorithms. This study demonstrates the effectiveness of the stream mining approach, achieving an accuracy of 82.48%, a 20.81% increase above the baseline prediction. Using feature selection methods improved the results by an additional 16.03%, to an accuracy of 98.51%.

With the rapid growth of the Internet in recent years, the ability to analyze and identify its users has become increasingly important. Authorship analysis provides a means to glean information about the author of a document originating from the internet or elsewhere, including but not limited to the author’s gender. There are well-known linguistic differences between the writing of men and women, and these differences can be effectively used to predict the gender of a document’s author. Capitalizing on these linguistic nuances, this study uses a set of stylometric features and a set of word count features to facilitate automatic gender discrimination on emails from the popular Enron email dataset. These features are used in conjunction with the Modified Balanced Winnow Neural Network proposed by Carvalho and Cohen, an improvement on the original Balanced Winnow created by Littlestone. Experiments with the Modified Balanced Winnow show that it is effectively able to discriminate gender using both stylometric and word count features, with the word count features providing superior results