Nov 28

Text As Networks – Annotated Bibliography

Exploring texts as networks allows connections within and between texts to be visualised in a simplified manner, enabling researchers to gain insight into complex relationships, which may otherwise remain hidden.

Network analysis of texts is at the intersection of the humanities and science, and therefore has the potential to be truly interdisciplinary. This annotated bibliography covers a period of approximately ten years, and aims to highlight texts which apply network theories to texts, and the methods used to extract networks.


Drieger, Philipp. “Semantic Network Analysis as a Method for Visual Text Analytics.” Procedia – Social and Behavioral Sciences 79 (2013): 4–17. Web. 26 Oct. 2014.

Drieger’s article explores the use of semantic network analysis to gain insights into texts. Building “partially” on text mining, Drieger gives a clear and detailed overview of the field. The article outlines the assumptions and theories used, and emphasises the importance of a combined quantitative and qualitative approach allowing a researcher to “visually explore unknown text sources” (15). Although Drieger presents a detailed description and evaluation of the method, the specific tools utilised have been omitted.

Elson, D.K., Nicholas Dames, and K.R. McKeown. “Extracting Social Networks from Literary Fiction.” Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2010. 138–147. Web. 13 Nov. 2014.

This article outlines a method for the extraction of social networks from 19th Century novels and serials. The authors’ method relies upon the extraction of conversations from a corpus of 60 novels and serials, spanning the work of 31 authors. The aim is to test the “validity of some core theories about social interaction” (139), specifically those of Bakhtin, Moretti and Eagleton. Elson et al. argue that their method disproves these theories, however they do not fully investigate the limitations of their chosen method.

Gillam, Lee, and Khurshid Ahmad. “Pattern Mining across Domain-Specific Text Collections.” Machine Learning and Data Mining in Pattern Recognition: 4th International Conference, MLDM. Ed. P Perner and A Imiya. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. 570–579. Web. 19 Nov. 2014.

This article explores the use of statistical measures from corpus linguistics in order to visualise a domain via its collocational network. Using the British National Corpus as a reference text, the authors statistically compare this to domain-specific corpora drawn from science and engineering. Gillam and Ahmad identify a number of differences between ‘general language’ and domain-specific language, especially in lower frequency words, using the concept of ‘weirdness’ to explore this.

Hunter, Starling. “A Novel Method of Network Text Analysis.” Open Journal of Modern Linguistics 4.June (2014): 350–366. Web. 26 Oct. 2014.

The article, published in a peer reviewed journal, presents an unusual method of creating a network text analysis. Hunter provides a detailed overview of network text analysis, including a comparison of existing methods for generating and analysing text networks. His method proposes the use of morphology to filter words, which are mapped onto concepts “on the basis of [Indo-European] etymology” (356).  Hunter’s article is comprehensive, however it neglects to consider the issues of validity of a method reliant upon a theoretical language.

Kok, Stanley, and Pedro Domingos. “Extracting Semantic Networks from Text Via Relational Clustering.” Machine Learning and Knowledge Discovery in Databases. Ed. Walter Daelemans, Bart Goethals, and Katharina Morik. Vol. 5211. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. 624–639. Web. 13 Nov. 2014. Lecture Notes in Computer Science.

Kok and Domingos’ article explores a method for extracting semantic networks from large scale corpora using “a scalable, unsupervised, and domain-independent system” (625). The article, which has complex mathematical content, provides an overview of existing tools and current research in the field. They trial their Semantic Network Extractor alongside a number of existing tools, using a large scale web corpus, and conclude that their method is a promising approach.

Long, Seth. “Editors’ Choice: Text Network Analysis 2: Meaning Circulation in Lolita.” Digital Humanities Now. N.p., 2012. Web. 26 Oct. 2014.

This blog post provides a example of network analysis which traces meaning circulation in the first ten chapters of Nabokov’s Lolita. Long uses his analysis to explore the ‘lexical connections’, using betweenness centrality highlighted by node size. The embedded videos, which illustrate the Gephi networks, are effective, and Long concludes that this method indicates a “connection between form and function, style and plot”.

MacCarron, Pádraig, and Ralph Kenna. “Universal Properties of Mythological Networks.” EPL (Europhysics Letters) 99.2 (2012): 1–6. Web. 13 Nov. 2014.

This peer reviewed article presents an application of social network theory to three mythological texts, Beowulf, Iliad and Táin Bó Cuailnge. MacCarron and Kenna aim to “place mythological narratives on the spectrum from the real to the imaginary” (1). Characters are extracted from each text and linked via a ‘friendly’ or ‘hostile’ relationship. The resulting social network is then compared to real and fictional networks. The methodology is clearly explained, although the mathematics is complex. The authors conclude that there are indications that all three texts have a level of historicity, including Táin Bó Cuailnge.

Magnusson, Camilla, and Hannu Vanharanta. “Visualizing Sequences of Texts Using Collocational Networks.” Machine Learning and Data Mining in Pattern Recognition: 3rd International Conference MLDM. Ed. Petra Perner and Azriel Rosenfeld. Berlin, Heidelberg: Springer, 2003. 276–283. Web. 26 Oct. 2014.

This article offers a “non-technical” (276) exploration of the use of collocational networks to visualise changes in a series of financial reports. Magnusson and Vanharanta use Mutual Information and frequency, tools borrowed from corpus linguistics, to create their network manually. The article illustrates how a comparison of collocational networks can aid the identification of changes within a corpus.

Moretti, Franco. “Maps.” Graphs, Maps, Trees: Abstract Models for a Literary History. London: Verso, 2005. Print.

This chapter demonstrates the use of visual diagrams to indicate relationships between narrative elements and the physical world. Moretti explores the use of traditional geographical maps, before moving on to consider stylised geometric’ maps, to “abstract [details]….from the narrative flow” (53). He uses a series of ‘Village’ stories (Mitford, Galt and Auerbach) to demonstrate how this method of exploring a text enables the researcher to see changes over time, as well as conceptual elements which may not be apparent via close reading.

Paranyushkin, Dmitry. “Identifying the Pathways for Meaning Circulation Using Text Network Analysis.” Nodus Labs. N.p., 2011. Web. 10 Oct. 2014.

This article, presents a method for the extraction and visualisation of a text network. Paranyushkin outlines the problem of subjectivity in the creation of semantic networks and aims to “avoid as much subjective and cultural influence as possible” (3) in his own method. A clear and detailed methodology, which includes raw data and commands, mean that the reader can follow and replicate the research.

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