Abstract
In this article we report about our efforts to develop and evaluate computational support tools for literary studies. We present a novel method and tool that allows interactive visual analytics of character occurrences in Victorian novels, and has been handed to humanities scholars and students for work with a number of novels from different authors. Our user study reveals insights about Victorian novels that are valuable for scholars in the digital humanities field, and informs UI as well as UX designers about how these domain experts interact with tools that leverage network science.
Funding statement: This work was supported by the Spearheading Digital Futures Steering Group at Victoria University of Wellington and Victoria Business School.
About the authors
Markus Luczak-Roesch is a Senior Lecturer in Information Systems at the School for Information Management, Victoria Business School, Victoria University of Wellington. Before joining Victoria Markus worked as a Senior Research Fellow on the prestigious EPSRC programme grant SOCIAM - The Theory and Practice of Social Machines at the University of Southampton, Electronics and Computer Science (UK, 2013–2016). A computer scientist by education, Markus investigates formal properties of information in socio-technical systems and human factors of information and computing systems. More information: http://markus-luczak.de
Adam Grener is Lecturer in the English Programme at Victoria University of Wellington. His main area of research is the nineteenth-century British novel, though he also has interest in the history of the novel, narrative theory, and computational approaches to literature. His work has appeared in the journals Genre, Narrative, and Modern Philology, and he is the co-editor of a special issue of Genre, “Narrative Against Data in the Victorian Novel”. He is completing a book on realist aesthetics and the history of probabilistic thought. More information: http://www.victoria.ac.nz/seftms/about/staff/adam-grener
Emma Fenton is an honours student in English Literature at Victoria University of Wellington. She is interested in the aesthetics of literature, and how text might be represented in visual forms.
Acknowledgment
The authors want to thank Tom Goldfinch for his invaluable contributions to the development of the tool prototype. We also thank Yevgeniya Li and Kingsley Ihejirika for their support in producing the demonstration material for our tool prototype. Finally, we thank the anonymous reviewers for their critical comments and suggestions for improving this article.
References
1. A. Agarwal, A. Corvalan, J. Jensen, and O. Rambow. Social network analysis of Alice in wonderland. In Workshop on Computational Linguistics for Literature, pages 88–96. Association for Computational Linguistics, 2012.Search in Google Scholar
2. R. Alberich, J. Miro-Julia, and F. Rossello. Marvel universe looks almost like a real social network. arXiv:cond-mat/0202174v1, 2002.Search in Google Scholar
3. D. Bamman, T. Underwood, and N. A. Smith. A Bayesian mixed effects model of literary character. In ACL (1), pages 370–379, 2014.10.3115/v1/P14-1035Search in Google Scholar
4. D. Berry. The computational turn: Thinking about the digital humanities. Culture Machine, 12, 2011.Search in Google Scholar
5. A. Bonato, D. R. D’Angelo, E. R. Elenberg, D. F. Gleich, and Y. Hou. Mining and modeling character networks. In Algorithms and Models for the Web Graph: 13th International Workshop, WAW 2016, Montreal, QC, Canada, December 14–15, 2016, Proceedings 13, pages 100–114. Springer, 2016.10.1007/978-3-319-49787-7_9Search in Google Scholar
6. N. C. Burbules. Rhetorics of the web: Hyperreading and critical literacy. In Page to screen: Taking literacy into the electronic era, pages 102–122, 1998.10.4324/9780203201220_chapter_5Search in Google Scholar
7. T. E. Clement. ’a thing not beginning and not ending’: using digital tools to distant-read Gertrude Stein’s the making of Americans. Literary and Linguistic Computing, 23, 2008.10.1093/llc/fqn020Search in Google Scholar
8. C. Collins, G. Penn, and S. Carpendale. Bubble sets: Revealing set relations with isocontours over existing visualizations. In Visualization and Computer Graphics, 2009. IEEE Transactions on, pages 1009–1016. IEEE, 2009.10.1109/TVCG.2009.122Search in Google Scholar PubMed
9. C. Dickens. Martin Chuzzlewit. Penguin Books, 1999, 1844.10.1093/oseo/instance.00121330Search in Google Scholar
10. A. Don, E. Zheleva, M. Gregory, S. Tarkan, L. Auvil, T. Clement, B. Shneiderman, and C. Plaisant. Discovering interesting usage patterns in text collections: integrating text mining with visualization. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, pages 213–222. ACM, 2007.10.1145/1321440.1321473Search in Google Scholar
11. J. Drouin. Close-and distant-reading modernism: Network analysis, text mining, and teaching the little review. The Journal of Modern Periodical Studies, 5(1):110–135, 2014.10.5325/jmodeperistud.5.1.0110Search in Google Scholar
12. D. K. Elson, N. Dames, and K. R. McKeown. Extracting social networks from literary fiction. In Proceedings of the 48th annual meeting of the association for computational linguistics, pages 138–147. Association for Computational Linguistics, 2010.Search in Google Scholar
13. A. Gibson. Our mutual friend and network form. Novel, 48(1):63–84, 2015.10.1215/00295132-2860341Search in Google Scholar
14. J. H. Grossman. Charles Dickens’s networks: public transport and the novel. Oxford University Press, 2012.10.1093/acprof:osobl/9780199682164.001.0001Search in Google Scholar
15. J. H. Grossman. Living the global transport network in great expectations. Victorian Studies, 57(2):225–250, 2015.10.2979/victorianstudies.57.2.225Search in Google Scholar
16. D. Hawes. Who’s who in Dickens. Routledge, 2013.10.4324/9781315013091Search in Google Scholar
17. L. Hettinger, M. Becker, I. Reger, F. Jannidis, and A. Hotho. Genre classification on German novels. In Database and Expert Systems Applications (DEXA), 2015 26th International Workshop on, pages 249–253, IEEE, 2015.10.1109/DEXA.2015.62Search in Google Scholar
18. M. Jockers. Macroanalysis: Digital Methods & Literary History. University of Illinois Press, 2013.10.5406/illinois/9780252037528.001.0001Search in Google Scholar
19. R. Kleymann. Visualisation of literary narratives: How to support text analysis with visualisations? creating a narratological use case. http://threedh.net/visualisation-of-literary-narratives-how-to-support-text-analysis-with-visualisations-creating-a-narratological-use-case/.Search in Google Scholar
20. C. Levine. Forms: Whole, Rhythm, Hierarchy, Network. Princeton University Press, 2015.10.1515/9781400852604Search in Google Scholar
21. M. Luczak-Roesch, A. Grener, E. Fenton, and T. Goldfinch. Creating Transcendental Information Cascades from English Literature: An Intuitive Approach in R. https://doi.org/10.5281/zenodo.164949, Nov. 2016.Search in Google Scholar
22. M. Luczak-Roesch, R. Tinati, and K. O’Hara. What an entangled web we weave: An information-centric approach to socio-technical systems. PeerJ Preprints, 5:e2789v1.Search in Google Scholar
23. M. Luczak-Roesch, R. Tinati, K. O’Hara, and N. Shadbolt. Socio-technical computation. In Proceedings of the 18th ACM Conference Companion on Computer Supported Cooperative Work & Social Computing, pages 139–142. ACM, 2015.10.1145/2685553.2698991Search in Google Scholar
24. M. Luczak-Roesch, R. Tinati, and N. Shadbolt. When resources collide: Towards a theory of coincidence in information spaces. In Proceedings of the 24th International Conference on World Wide Web, pages 1137–1142. ACM, 2015.10.1145/2740908.2743973Search in Google Scholar
25. M. Luczak-Roesch, R. Tinati, M. Van Kleek, and N. Shadbolt. From coincidence to purposeful flow? properties of transcendental information cascades. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, pages 633–638. ACM, 2015.10.1145/2808797.2809393Search in Google Scholar
26. F. Moretti. Network theory, plot analysis. New Left Review, 2011.Search in Google Scholar
27. G. A. Pierce. The Dickens Dictionary. Courier Corporation, 2013.Search in Google Scholar
28. A. Piper. The constraints of character. introducing a character feature-space tool. https://txtlab.org/?p=611.Search in Google Scholar
29. A. Piper. Detecting literary characters. https://txtlab.org/?p=559.Search in Google Scholar
30. A. Piper. Development of a (semi-) automatic character network tool. https://novel-tm.ca/?p=472.Search in Google Scholar
31. S. Prado, S. Dahmen, A. Bazzan, P. MacCarron, and R. Kenna. Temporal network analysis of literary texts. Advances in Complex Systems, 19, 2016.10.1142/S0219525916500053Search in Google Scholar
32. J. Rosenthal. Introduction: “narrative against data”. Genre, 50(1):1–18, 2017.10.1215/00166928-3761312Search in Google Scholar
33. J. Rydberg-Cox. Social networks and the language of Greek tragedy. Journal of the Chicago Colloquium on Digital Humanities and Computer Science, 1(3), 2011.Search in Google Scholar
34. G. Sack. Character networks for narrative generation. In Intelligent Narrative Technologies: Papers from the 2012 AIIDE Workshop, AAAI Technical Report WS-12-14, pages 38–43, 2012.10.1609/aiide.v8i2.12541Search in Google Scholar
35. B. Shneiderman. The eyes have it: A task by data type taxonomy for information visualizations. In Visual Languages, 1996. Proceedings., IEEE Symposium on, pages 336–343. IEEE, 1996.10.1109/VL.1996.545307Search in Google Scholar
36. H. Stone, editor. Charles Dickens’ Working Notes for his Novels. University of Chicago Press, 1987.Search in Google Scholar
37. R. Tinati, M. Luczak-Roesch, and W. Hall. Finding structure in wikipedia edit activity: An information cascade approach. In Proceedings of the 25th International Conference Companion on World Wide Web, pages 1007–1012. International World Wide Web Conferences Steering Committee, 2016.10.1145/2872518.2891110Search in Google Scholar
38. A. Zehe, M. Becker, L. Hettinger, A. Hotho, I. Reger, and F. Jannidis. Prediction of happy endings in German novels based on sentiment information. In 3rd Workshop on Interactions between Data Mining and Natural Language Processing, Riva del Garda, Italy, 2016.Search in Google Scholar
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