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The Sociability of Detection

The Sociability of Detection. Andrew Piper, Derek Ruths , Syed Ahmed, Faiyaz Al Zamal. The History of Character Theory. The History of Character Theory. Vladimir Propp , The Morphology of the Folktale. The History of Character Theory. Vladimir Propp , The Morphology of the Folktale

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The Sociability of Detection

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  1. The Sociability of Detection Andrew Piper, Derek Ruths, Syed Ahmed, Faiyaz Al Zamal

  2. The History of Character Theory

  3. The History of Character Theory • Vladimir Propp, The Morphology of the Folktale

  4. The History of Character Theory • Vladimir Propp, The Morphology of the Folktale • James Phelan, Reading People, Reading Plots: Character, Progression, and the Interpretation of Narrative (Chicago, 1989)

  5. The History of Character Theory • Vladimir Propp, The Morphology of the Folktale • James Phelan, Reading People, Reading Plots: Character, Progression, and the Interpretation of Narrative (Chicago, 1989) • David A. Brewer, The Afterlife of Character, 1726-1825 (Penn, 2005)

  6. The History of Character Theory • Vladimir Propp, The Morphology of the Folktale • James Phelan, Reading People, Reading Plots: Character, Progression, and the Interpretation of Narrative (Chicago, 1989) • David A. Brewer, The Afterlife of Character, 1726-1825 (Penn, 2005) • Deidre Shauna Lynch, The Economy of character: Novels, Market culture, and the Business of Inner Meaning (Chicago, 1998)

  7. The History of Character Theory • Vladimir Propp, The Morphology of the Folktale • James Phelan, Reading People, Reading Plots: Character, Progression, and the Interpretation of Narrative (Chicago, 1989) • David A. Brewer, The Afterlife of Character, 1726-1825 (Penn, 2005) • Deidre Shauna Lynch, The Economy of character: Novels, Market culture, and the Business of Inner Meaning (Chicago, 1998) • Lisa Zunshine, Why We Read Fiction: Theory of Mind and the Novel (Columbus: Ohio State UP, 2006)

  8. The History of Character Theory • Vladimir Propp, The Morphology of the Folktale • James Phelan, Reading People, Reading Plots: Character, Progression, and the Interpretation of Narrative (Chicago, 1989) • David A. Brewer, The Afterlife of Character, 1726-1825 (Penn, 2005) • Deidre Shauna Lynch, The Economy of character: Novels, Market culture, and the Business of Inner Meaning (Chicago, 1998) • Lisa Zunshine, Why We Read Fiction: Theory of Mind and the Novel (Columbus: Ohio State UP, 2006) • BlakeyVermeule, Why do we care about literary characters? (JHU, 2010)

  9. SNA and Literary Theory

  10. SNA and Literary Theory • Franco Moretti, “Network Theory, Plot Analysis,” New Left Review 68 (2011) • Franco Moretti, “Operationalizing,” New Left Review 84 (2013)

  11. SNA and Literary Theory • Franco Moretti, “Network Theory, Plot Analysis,” New Left Review 68 (2011) • Franco Moretti, “Operationalizing,” New Left Review 84 (2013) • Padraig MacCarron & Ralph Kenna, “Universal properties of mythological networks,” EPL, 99 (2012) 28002

  12. SNA and Literary Theory • Franco Moretti, “Network Theory, Plot Analysis,” New Left Review 68 (2011) • Franco Moretti, “Operationalizing,” New Left Review 84 (2013) • Padraig MacCarron & Ralph Kenna, “Universal properties of mythological networks,” EPL, 99 (2012) 28002 • ApoorvAgarwal, AnupKotalwar and Owen Rambow, “Automatic Extraction of Social Networks from Literary Text: A Case Study on Alice in Wonderland,” Proceedings of the 6th International Joint Conference on Natural Language Processing (IJCNLP 2013)

  13. SNA and Literary Theory • Franco Moretti, “Network Theory, Plot Analysis,” New Left Review 68 (2011) • Franco Moretti, “Operationalizing,” New Left Review 84 (2013) • Padraig MacCarron & Ralph Kenna, “Universal properties of mythological networks,” EPL, 99 (2012) 28002 • ApoorvAgarwal, AnupKotalwar and Owen Rambow, “Automatic Extraction of Social Networks from Literary Text: A Case Study on Alice in Wonderland,” Proceedings of the 6th International Joint Conference on Natural Language Processing (IJCNLP 2013) • 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.

  14. AMT Interface

  15. The performance of the AMT-based interaction mapping system when assessed on the annotated dataset.

  16. The effect of changing the number of workers who code the same text block on the sensitivity and specificity with which interactions are identified in the text.

  17. Terms • Nodes = Characters • Edges = Relationships • Edge Weights = Interactions

  18. Detective Fiction has larger, sparser networks

  19. Detective Fiction has larger, sparser networks • # Nodes • DF 13.52 ± 7.76 • SF 5.45 ± 2.91 • P-value < 0.0001

  20. Detective Fiction has larger, sparser networks • # Nodes • DF 13.52 ± 7.76 • SF 5.45 ± 2.91 • P-value < 0.0001 • # Edges • DF 9.76 ± 4.03 • SF 5.55 ± 2.50 • P-value < 0.0001

  21. Detective Fiction has larger, sparser networks • # Nodes • DF 13.52 ± 7.76 • SF 5.45 ± 2.91 • p-value < 0.0001 • # Edges • DF 9.76 ± 4.03 • SF 5.55 ± 2.50 • p-value < 0.0001 • Density • DF 0.35 ± 0.14 • SF 0.53 ± 0.25 • p-value = 0.007

  22. Short Fiction

  23. Detective Fiction Short Fiction

  24. Detective Fiction has fewer indirectly connected neighborhoods

  25. Detective Fiction has fewer indirectly connected neighborhoods • Clustering Coefficient • DF 0.36 ± 0.23 • SF 0.36 ± 0.36 • P-value 0.965

  26. Detective Fiction has fewer indirectly connected neighborhoods • Clustering Coefficient • DF 0.36 ± 0.23 • SF 0.36 ± 0.36 • P-value 0.965 • 2-Clustering (Dispersion) • DF 0.92 ± 0.06 • SF 0.97 ± 0.04 • P-value 0.003

  27. Detective Fiction has fewer indirectly connected neighborhoods • Clustering Coefficient • DF 0.36 ± 0.23 • SF 0.36 ± 0.36 • P-value 0.965 • 2-Clustering (Dispersion) • DF 0.92 ± 0.06 • SF 0.97 ± 0.04 • P-value 0.003 • 2-Clustering along heaviest edge • DF 0.83 ± 0.21 • SF 0.96 ± 0.11 • P value 0.017

  28. Detectives don’t invest in strong relationships

  29. Detectives don’t invest in strong relationships • Heaviest edge fraction • DF 0.26 ± 0.13 • 0.40 ± 0.12 • P-value 0.001

  30. Detectives don’t invest in strong relationships • Heaviest edge fraction • DF 0.26 ± 0.13 • SF 0.40 ± 0.12 • P-value 0.001 • Degree-weighted heaviest edge • DF 0.88 ± 0.11 • 0.98 ± 0.05 • P-value 0.001

  31. Detectives are not the center of the social universe

  32. Detectives are not the center of the social universe • Normalized Closeness Vitality • DF 3.14 ± 1.36 • SF 4.28 ± 1.92 • P-value 0.039

  33. Detective Fiction takes longer to reveal the entire network

  34. Detective Fiction takes longer to reveal the entire network • Time to completion – Nodes • DF 72.74 ± 15.18 • 61.99 ± 22.99 • P-value 0.091 • Time to completion – Interactions • DF 88.27 ± 11.43 • SF 80.46 ± 18.33 • P-value 0.117

  35. Detective Fiction takes longer to reveal the entire network • Time to completion – Edges • DF 87.15 ± 11.05 • SF 73.77 ± 17.09 • P-value 0.006

  36. To Do • Naming

  37. To Do • Naming • Language and other genres

  38. To Do • Naming • Language

  39. To Do • Naming • Language • Other Genres

  40. To Do • Naming • Language • Other Genres • Random Models

  41. To Do • Naming • Language • Other Genres • Random Models • Citizen Science

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