Analyzing authorship of online texts is an important analysis task in security-related areas such as cybercrime investigation and counter-terrorism, and in any field of endeavor in which authorship may be uncertain or obfuscated. This paper presents an automated approach for authorship analysis using machine learning methods, a robust stylometric feature set, and a series of visualizations designed to facilitate analysis at the feature, author, and message levels. A testbed consisting of 506,554 forum messages, in English and Arabic, from 14,901 authors was first constructed. A prototype portal system was then developed to support feasibility analysis of the approach. A preliminary evaluation to assess the efficacy of the text visualizations was conducted. The evaluation showed that task performance with the visualization functions was more accurate and more efficient than task performance without the visualizations.