Text Visualization and Close Reading for Journalism with Storifier

Nicole Sultanum (1) , Anastasia Bezerianos (2) , Fanny Chevalier (1)

1 DCS - Department of Computer Science [University of Toronto]
2 ILDA - Interacting with Large Data - LISN

Journalistic inquiry often requires analysis and close study of large text collections around a particular topic. We argue that this practice could benefit from a more text- and reading-centered approach to journalistic text analysis, one that allows for a fluid transition between overview of entities of interest, the context of these entities in the text, down to the detailed documents they are extracted from. In this context, we present the design and development of Storyfier, a text visualization tool created in close collaboration with a large francophone news office. We also discuss a case study on how our tool was used to analyze a text collection and helped publish a story.

Domaines: Interface homme-machine [cs.HC]

Nicole Sultanum, Anastasia Bezerianos, Fanny Chevalier. Text Visualization and Close Reading for Journalism with Storifier. 2021 IEEE Visualization Conference (VIS), Oct 2021, New Orleans, United States. ⟨10.1109/VIS49827.2021.9623264⟩. ⟨hal-03423931⟩