  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.
