Estimating the Identity of Satoshi Nakamoto using Multimodal Stylometry

Multimodal Stylometry is a novel approach that utilizes multiple data modes to build a machine learning model and make predictions. We compare multimodal stylometry with unimodal stylometry and show that multimodality increases the classification accuracy for authorship identification. The Satoshi Nakamoto authorship identification was used as a case study