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
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, 17261825 (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, 17261825 (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, 17261825 (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




# Edges




DF 13.52 ± 7.76
SF 5.45 ± 2.91
p-value < 0.0001
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




2-Clustering (Dispersion)




DF 0.36 ± 0.23
SF 0.36 ± 0.36
P-value 0.965
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