Mapping the First World War Using Interactive Streamgraphs
Alaa Abi-Haidar, Bin Yang, Jean‐Gabriel Ganascia · Sociology and Anthropology · 2016
In this paper, we use unsupervised named entity recognition and streamgraphs in order to visualize massive amounts of unstructured textual stream data, namely, French newspapers (e.g. Le Figaro, La presse, L'humanité) from the first world war period. Such a visualization allows us to identify main characters, events and locations involved in or relevant to the first world war, according to the French press. Furthermore, our visualization technique can help visually identify correlations between major people (e.g. presidents, generals, public figures...), locations (e.g. countries, cities, towns...) and organizations and events (e.g. corporations, battles...) on multiple aligned streamgraphs. Our method can be applied to unstructured data streams of any domain or time period.