Gender Identification on Twitter Using the Modified Balanced Winnow

• 作者: Benjamin Valyou Brian Dickinson Timothy Munson Wei Hu William Deitrick Zachary Miller
• 来源期刊: 通讯与网络(英文)
• 学科: 医学
• 关键词: GENDER IDENTIFICATION TWITTER MODIFIED BALANCED WINNOW Neural Networks Stream Data Mining Feature Selection
• 年，卷（期）: 2012,（3）
• 页码范围: 189-195
• 页数: 7页
• 分类号: R73

摘要

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%.