# The one comparing narrative social network extraction techniques

arXiv: https://arxiv.org/abs/1811.01467 · HTML: https://mlsanity.com/paper/1811.01467/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [Understanding the Effect of Knowledge Graph Extraction Error on Downstream Graph Analyses: A Case Study on Affiliation Graphs](https://mlsanity.com/paper/2506.12367/) (2025)
- [Relation Extraction Across Entire Books to Reconstruct Community Networks: The AffilKG Datasets](https://mlsanity.com/paper/2505.10798/) (2025)
- [Time Series Analysis of Key Societal Events as Reflected in Complex Social Media Data Streams](https://mlsanity.com/paper/2403.07090/) (2024)

## Abstract (arXiv)

Analysing narratives through their social networks is an expanding field in quantitative literary studies. Manually extracting a social network from any narrative can be time consuming, so automatic extraction methods of varying complexity have been developed. However, the effect of different extraction methods on the analysis is unknown. Here we model and compare three extraction methods for social networks in narratives: manual extraction, co-occurrence automated extraction and automated extraction using machine learning. Although the manual extraction method produces more precise results in the network analysis, it is much more time consuming and the automatic extraction methods yield comparable conclusions for density, centrality measures and edge weights. Our results provide evidence that social networks extracted automatically are reliable for many analyses. We also describe which aspects of analysis are not reliable with such a social network. We anticipate that our findings will make it easier to analyse more narratives, which help us improve our understanding of how stories are written and evolve, and how people interact with each other.