The d-wise tool suite: Multi-modal machine-learning-powered tools supporting and enhancing digital discourse analysis

Abstract

This work introduces the D-WISE Tool Suite (DWTS), a novel working environment for digital qualitative discourse analysis in the Digital Humanities (DH). The DWTS addresses limitations of current DH tools induced by the ever-increasing amount of heterogeneous, unstructured, and multi-modal data in which the discourses of contemporary societies are encoded. To provide meaningful insights from such data, our system leverages and combines state-of-The-Art machine learning technologies from Natural Language Processing and Computer Vision. Further, the DWTS is conceived and developed by an interdisciplinary team of cultural anthropologists and computer scientists to ensure the tool s usability for modern DH research. Central features of the DWTS are: A) import of multi-modal data like text, image, audio, and video b) preprocessing pipelines for automatic annotations c) lexical and semantic search of documents d) manual span, bounding box, time-span, and frame annotations e) documentation of the research process.