Andrew Piper, Derek Ruths, Syed Ahmed, Faiyaz Al Zamal The Sociability of Detection.
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Transcript of Andrew Piper, Derek Ruths, Syed Ahmed, Faiyaz Al Zamal The Sociability of Detection.
Andrew Piper, Derek Ruths, Syed Ahmed, Faiyaz Al Zamal
The Sociability of Detection
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 James Phelan, Reading People, Reading
Plots: Character, Progression, and the Interpretation of Narrative (Chicago, 1989)
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)
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)
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)
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)
Blakey Vermeule, Why do we care about literary characters? (JHU, 2010)
SNA and Literary Theory
SNA and Literary Theory
Franco Moretti, “Network Theory, Plot Analysis,” New Left Review 68 (2011)
Franco Moretti, “Operationalizing,” New Left Review 84 (2013)
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
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
Apoorv Agarwal, Anup Kotalwar 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)
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 Apoorv Agarwal, Anup Kotalwar 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.
AMT Interface
The performance of the AMT-based interaction mapping system when assessed on the annotated dataset.
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.
Terms
Nodes = Characters Edges = Relationships Edge Weights = Interactions
Detective Fiction has larger, sparser networks
Detective Fiction has larger, sparser networks
# Nodes DF 13.52 ± 7.76 SF 5.45 ± 2.91 P-value < 0.0001
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
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
Short Fiction
Detective Fiction
Short Fiction
Detective Fiction has fewer indirectly connected neighborhoods
Detective Fiction has fewer indirectly connected neighborhoods
Clustering Coefficient DF 0.36 ± 0.23 SF 0.36 ± 0.36 P-value 0.965
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
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
Detectives don’t invest in strong relationships
Detectives don’t invest in strong relationships
Heaviest edge fraction DF 0.26 ± 0.13 0.40 ± 0.12 P-value 0.001
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
Detectives are not the center of the social universe
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
Detective Fiction takes longer to reveal the entire network
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
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
To Do
Naming
To Do
Naming Language and other genres
To Do
Naming Language
To Do
Naming Language Other Genres
To Do
Naming Language Other Genres Random Models
To Do
Naming Language Other Genres Random Models Citizen Science