Online marketplaces and communication media such as email, web sites, forums, and chat rooms have been ubiquitously integrated into our everyday lives. Unfortunately, the anonymous nature of these channels makes them an ideal avenue for online fraud, hackers, and cybercrime. Anonymity and the sheer volume of online content make cyber identity tracing an essential yet strenuous endeavor for Internet users and human analysts. In order to address these challenges, we propose a framework for online stylometric analysis to assist in distinguishing authorship in online communities based on writing style. Our framework includes the use of a scalable identity-level similarity detection technique coupled with an extensive stylistic feature set and an identity database. The framework is intended to support stylometric authentication for Internet users as well as provide support for forensic investigations. The proposed technique and extended feature set were evaluated on a test bed encompassing thousands of feedback comments posted by 100 electronic market traders. The method outperformed benchmark stylometric techniques with an accuracy of approximately 95% when differentiating between 200 trader identities. The results indicate that the proposed stylometric analysis approach may help mitigate the effects of online anonymity abuse.