Behavioral Stylometry

Behavioral stylometry analyzes patterns in human behavior to identify individuals or groups based on their characteristic decision-making styles. Current research focuses on applying this technique to diverse domains, including authorship attribution in literature and identifying chess players from their game records, leveraging machine learning models like transformers and employing techniques such as n-gram analysis and word keyness. These advancements offer powerful tools for authentication, provenance analysis, and understanding individual differences in decision-making processes across various fields, raising important ethical considerations regarding privacy and data security.