Data-Driven Authorship Feature Extraction and Comparative Analysis using Machine Learning

Sonalysts will develop the Authorship and Intent Recognition Environment (AIRE), an adaptive platform that provides automated and semi-automated analyses for Information Operations (IO) based on textual information and other data sources. AIRE will use a variety of analytical techniques, including Machine Learning (ML) and Artificial Intelligence (AI) methods, as well more explainable methods such as Qualitative Comparative Analysis (QCA). Analysts will interact with intuitive interfaces in a visual environment built around their workflows. AIRE will leverage AI/ML techniques to find trends and connect the dots among data, and then allow more explainable statistical methods to be run on data to provide more explainability to the analyst than using “black box” methods alone. These methods will be combined with the use of deep linguistic features, such as semantic information and other relevant metadata, to fuse authorship detection and intent recognition methods, rather than treating them as separate routines. This combination of analytical techniques in an intuitive interface will make AIRE a significant innovation and a valuable tool to several end users.