A literary work may be political in many ways. Themes, motifs, characterization, style, tone, and genre may all carry political meaning, and so may various aspects of literary creation and reception. This means that a number of different quantitative approaches may be useful in the study of the politics of literature, depending on your particular interests.

One way to begin to explore the social world of a given literary work is by asking, “What kind of characters and social groups exist in this text, and how do they interact?”.

Social network analysis may help you to explore these questions. It is particularly suited to drama, because character interactions may be easily quantified as the number of verbal exchanges between characters.

Studying character networks allows you to consider not only what a play says about social communities, but also how it builds them and centralizes particular types of characters (and not others).

The field of corpus linguistics has also developed many useful approaches. For example, collocation analysis allows you to analyze the verbal contexts in which a particular word appears in a text, and to compare that to the way the word is used in other texts. This gives a means of grasping the clusters of associations that shape the complex meanings of a given politically significant concept in one or several texts. Usage fluctuation analysis is a recent, advanced method that builds on collocation analysis to study the historical changes in the usage of words.



An example of a free software useful as a tool for studying politics in literature is AntConc, a free software for corpus linguistic analysis, including collocation analysis. Collocation analysis is an approach to corpus analysis aimed at detecting significant regularities in the collocations of words, i.e. exploring how certain words or a particular combination of words appear together with a frequency greater than chance. A collocation analysis thus allows you to analyze the verbal contexts in which a particular word appears in a text and compare that to the way the word is used in other texts. This gives a way of grasping the clusters of associations that shape the complex meanings of a given politically significant concept as well as the discoursal representation of a social group in one or several texts. 

Surrounding politics can affect novels, but novels can also affect our political views by introducing words, concepts and metaphors that might have political significance. Think, for example, of George Orwell’s 1984 and how terms such as “big brother” or “thoughtcrime” became part of everyday language. Trace the usage of such words and expressions on Google Ngram Viewer or Google Trends to contextualize politics and literature.


Politics is always embedded in a complex social context which is impossible to fully capture by a computational model. However, computational methods can help to analyze underlying aspects of the social complexity and thus help to understand the political reality. One approach to the social dimension is conducting a social network analysis (SNA). It has found many uses in social and human sciences to analyze interactions in social groups and communities. The networked presentation of connections can reflect changes and dynamics in the society, and the analysis of the social structure can become more tangible by the network visualization.

In a literary work, SNA can help to detect influential characters, subgroups and community structure that mirror the social reality. For instance, Elson, Dames, and McKeown (2010) compared rural and urban social dynamics in 19th-century British literature, having hypotheses about different complexity and amount of interaction in the two settings. Network analysis is especially fitting for plays where dialogue can be translated to a network of edges representing interactions between the character nodes (“speaking-in-turn” principle). Select two plays you find interesting to compare from Gutenberg. In Python, using the NetworkX package (see e.g. this tutorial for help) you can generate a network that you can then visualize and analyze with Gephi. Compare the results of the selected plays. What kind of similarities and differences can you observe in their social hierarchy? Do the dynamics change if you split the analysis into the play's acts?

Another approach to study politics in literature is through language processing, by creating a Usage Fluctuation Analysis. UFA is designed to trace shifts in historical discourse and word usage. The method lies on two assumptions. The first is called collocation principle: a word co-occurring together in the vicinity of other words gives insights into the word’s usage, such as its semantics and implicit associations. Second, changes in the co-occurrence pattern can help to detect change in that word’s usage and thereby in its meaning. This way, if you select words that are important for social hierarchy (such as 'boss', 'master' or 'immigrant') it is possible to make social change visible from the linguistic point of view. Read more about UFA and see an example on the usage of the words ‘whore’ and ‘harlot’ over time in the article by McEnery, Brezina, and Baker (2019). Produce your own usage plot with Lancaster Stats Online Tools.


Scripts and sites


  • Brezina, V. (2018). Statistics in corpus linguistics: A practical guide. Cambridge University Press. https://doi.org/10.1017/9781316410899
  • Elson, D., Dames, N., & McKeown, K. (2010, July). Extracting social networks from literary fiction. In Proceedings of the 48th annual meeting of the association for computational linguistics (pp. 138-147). https://www.aclweb.org/anthology/P10-1015
  • Erlin, M., Piper, A., Knox, D., Pentecost, S., Drouillard, M., Powell, B., & Townson, C. (2021). Cultural capitals: Modeling ‘minor’ european literature. Journal of Cultural Analytics. https://doi.org/10.22148/001c.21182
  • Ladegaard, J., & Kristensen-McLachlan, R. D. (2021). Prodigal Heirs and Their Social Networks in Early Modern English Drama, 1590–1640. Law & Literature, 1-22. https://doi.org/10.1080/1535685X.2021.1902635 
  • McEnery, T., Brezina, V., & Baker, H. (2019). Usage Fluctuation Analysis: A new way of analysing shifts in historical discourse. International Journal of Corpus Linguistics, 24(4), 413-444. https://doi.org/10.1075/ijcl.18096.mce

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