A framework for authorship identification of online messages: Writing‐style features and classification techniques

TLDR

Rapid growth of internet technologies has led to widespread misuse of online messages, and the anonymity of these communications makes tracing authorship a critical societal concern. The authors propose a framework for authorship identification of online messages to address this identity‑tracing challenge. The framework extracts lexical, syntactic, structural, and content‑specific writing‑style features and trains inductive learning models—decision trees, backpropagation neural networks, and support vector machines—on English and Chinese newsgroup data to classify authorship. The method achieved 70–95 % accuracy, with all feature types contributing, SVM outperforming the others, and comparable high performance on both languages, demonstrating its multilingual applicability.

Abstract

Abstract With the rapid proliferation of Internet technologies and applications, misuse of online messages for inappropriate or illegal purposes has become a major concern for society. The anonymous nature of online‐message distribution makes identity tracing a critical problem. We developed a framework for authorship identification of online messages to address the identity‐tracing problem. In this framework, four types of writing‐style features (lexical, syntactic, structural, and content‐specific features) are extracted and inductive learning algorithms are used to build feature‐based classification models to identify authorship of online messages. To examine this framework, we conducted experiments on English and Chinese online‐newsgroup messages. We compared the discriminating power of the four types of features and of three classification techniques: decision trees, backpropagation neural networks, and support vector machines. The experimental results showed that the proposed approach was able to identify authors of online messages with satisfactory accuracy of 70 to 95%. All four types of message features contributed to discriminating authors of online messages. Support vector machines outperformed the other two classification techniques in our experiments. The high performance we achieved for both the English and Chinese datasets showed the potential of this approach in a multiple‐language context.