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Stories and Big Data: The Possibility of Narrative Explanations at Scale

Randall Harp

Philosophy of the Social Sciences2026https://doi.org/10.1177/00483931261418167article
ABDC B
Weight
0.50

What the paper says

It has been suggested that computational tools can enable us to identify narratives in large data sets, and that we can use those narratives to facilitate social science research at scale in the same way as distant reading techniques facilitate literary analysis at scale. In this paper I attempt to determine whether a Bag of Words approach to natural language processing can identify such narratives. I argue that a Bag of Words approach can capture some features of narratives but fails on others, especially the extent to which the meaning of the parts of narratives are dependent on the ending.

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https://doi.org/https://doi.org/10.1177/00483931261418167

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@article{randall2026,
  title        = {{Stories and Big Data: The Possibility of Narrative Explanations at Scale}},
  author       = {Randall Harp},
  journal      = {Philosophy of the Social Sciences},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/00483931261418167},
}

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Stories and Big Data: The Possibility of Narrative Explanations at Scale

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.