Optimizing Authorship Profiling of Online Messages

Authorship profiling is of growing importance in the current information age, partly due to its application in digital forensics. Methodologies of profiling like any other authorship analysis consist majorly of feature extraction and application of analytical techniques. Choice of feature sets and analytical techniques may significantly affect the performance of authorship analysis. Hence, a need for methods that can help improve on the success of authorship profiling undertakings. The present study sought through experiments, the writing features, analytical technique and number of class labels that can help improve the effectiveness of profiling the country of affiliation of authors of online messages. The experiment showed that the most effective model was achieved when all feature set types in our study were used within a two-class dataset that was analysed with the Neural Network (Multilayer Perceptron) machine learning scheme. The study recommends a need for further studies in finding models that can maximize both effectiveness and efficiency in profiling the authorship of online messages. CCS Concepts • General and reference ➝ Cross-computing tools and techniques ➝ Experimentation