Exploratory and explanationory features in data storytelling: untangling the interplay and associations with linearity, interactivity, and structure

Bahareh Heravi ()
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Bahareh Heravi: University of Surrey

Journal of Computational Social Science, 2025, vol. 8, issue 4, No 13, 36 pages

Abstract: Abstract In a journalism and communication landscape where data plays an increasingly important role in crafting reliable and engaging stories, data storytelling has become an integral part of the industry. Traditionally, journalism has often followed a linear, author-driven, and explanatory approach to storytelling. However, the rise of data storytelling has prompted a significant shift towards more interactive and reader-driven stories, with a higher potential for exploration. While the literature in the field has studied aspects such as interactivity in data visualisations, linearity and storytelling structures, the discourse surrounding exploratory features of data stories is relatively recent and scarce, and it remains largely unexplored. This paper addresses this gap and studies the exploratory and explanatory characteristics of data stories, and their interdependencies. It further examines the relationships of these facets with other key attributes of data stories, specifically linearity, interactivity, and storytelling structures. This is done through analysing a broad selection of data stories (N = 118) sourced from a variety of news outlets. Results uncover the complementary roles exploratory and explanatory characteristics play in data stories, and reveal the nuances in relationships with the other three facets. The findings provide both a deeper theoretical understanding of data story design and practical insights for data journalists seeking to balance explanation and exploration in their narratives.

Keywords: Data story; Storytelling structures; Data journalism; Narrative design; Data visualization; AI and journalism (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s42001-025-00407-6

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