Character Centrality in Present-Day Dutch
Literary Fiction
Roel Smeets
1
, Eric Sanders
2
, and Antal van den Bosch
3
1
Radboud University, Department of Literary and Cultural
Studies
2
Radboud University, Centre for Language and Speech
Technology
3
KNAW Meertens Institute
In the critique of literary representation, the depiction of literary char-
acters has been studied from ideological perspectives. Hierarchies can
be exposed by determining the centrality of a character relative to other
characters. As an addition to such close reading methods, the present
paper proposes an approach to character centrality that combines network
analysis with narratology. This explorative study is based on a dataset of
demographic metadata on 2,137 characters from a corpus of 170 contem-
porary Dutch novels. We extract social networks of characters from each
novel, and rank all characters according to five centrality metrics. Then,
we perform a multiple linear regression to test which of the demographic
variables predicts a character’s position in the rankings. Our results sug-
gest that immigrant and female characters score higher on two of the five
centrality metrics. As a narratological evaluation, we contextualise this
observed pattern in relation to a close reading of Özcan Akyol’s Eus (2012),
a novel from the corpus that thematises both descent and gender. We
demonstrate that our data-driven and empirically informed approach to
character centrality lays bare surprising patterns of representation which
only gain relevance in light of close readings of specific cases.
Keywords: Dutch literature, character representation, social network analysis
1 Quantifying the Critique of Literary Representation
Literary studies have a rich tradition of critically analysing hierarchies in literary texts
from an ideological perspective. In the wake of the poststructuralist turn (J.Culler
(1983)), the critique of literary and cultural representation has evolved into an engaging
field with roots in various ideological strands, such as Marxism, postcolonialism and
feminism. These so-called ‘hermeneutics of suspicion’ (Ricoeur (1979), Felski (2009))
have focused on a variety of topics, including the hierarchical representation of story
71
characters, the inhabitants of fictional story worlds. In Dutch literary studies, this
is illustrated by a range of studies that critique the representation of characters of a
certain gender, descent or class (e.g. Pattynama (1994), Meijer (1996a), Meijer (1996b),
Pattynama (1998), Minnaard (2010), Meijer (2011)). For the sake of the present research,
it suffices to say that this field of study is concerned with hierarchies between characters
and their identities, although this is of course not their only focus. These ideological
approaches to character representation are commonly (and/or implicitly) concerned
with how important, influential, dominant or central a character in a narrative is as
opposed to other characters. ‘Centrality’ will be used in this article as an umbrella
term to refer to abstract notions such as importance, dominance, influence and power.
When a character is central, it means that he or she is important, dominant, influential
or powerful in a specific way.
A number of quantitative studies on character representation in Dutch literature have
been conducted only recently (van der Deijl et al. (2016), Van der Deijl and Smeets (2018),
Koolen (2018): 162-244). These studies make use of new applications of social network
analysis and other quantitative methods to reconstruct and analyse story worlds in
a data-driven way, following earlier research on non-Dutch texts (e.g. Alberich et al.
(2002), Stiller et al. (2003), Elson et al. (2010), Lee and Yeung (2012), Karsdorp et al.
(2012), Agarwal et al. (2013), Jayannavar et al. (2015), Karsdorp et al. (2015b), Lee
and Wong (2016)). For the study of hierarchies between characters, these methods
provide the means for a formalisation and quantification of the concept of ‘character
centrality’. This is potentially interesting for the study of character representation as
practised in the critique of literary representation. Except for these recent examples,
studies on character representation mainly use close reading methods. This can lead
to powerful interpretations for one or a few cases, but such qualitative readings do not
result in general insights into the centrality of characters at a larger scale. However, the
importance of a character in a narrative might very well be expressed by the numerical
frequency with which he/she features in the narrative. While being fully aware that
data-driven approaches are not ideologically neutral either, the present study aims to
bridge this gap by considering the centrality of characters in both narratological and
statistical terms.
In this contribution, we try to answer the following question: To what extent can a
data-driven and empirically informed approach to character centrality contribute to the
ideological critique of literary representation? First, we provide a succinct overview
of how character centrality has been understood in the narratological tradition of
analysing literary texts. Second, we confront these narratological considerations
with a computational approach to identifying networks of characters. Third, we
describe our data and the method we used to rank all 2,137 characters in a corpus
of 170 contemporary Dutch novels on the basis of five statistical metrics. Fourth, we
analyse and interpret the results of a multiple regression analysis that tested which
demographic feature (gender, descent) was the best predictor for a character’s place
in the rankings. Based on simple descriptive statistics of the gender and descent
distributions among characters in the corpus, we hypothesise that male and non-
immigrant characters will score higher than female and immigrant characters. Fifth,
in order to evaluate the output of the statistical model narratologically, we confront
these findings with a close reading of one novel from the corpus. We conclude with
the argument that our approach to character centrality lays bare surprising patterns of
representation, although they only start to make sense when contextualised through a
qualitative reading of a specific case.
72
2 Centrality in Narratology
Narratology is one of the traditional methodological toolkits for the study of narra-
tives. This toolkit offers various instruments to analyse the centrality of characters in
literature, of which we will mention two of the most straightforward. A character’s
position in the story world is already predetermined by some basic structural features
of a literary text. The mode in which a novel is narrated is commonly a first indicator
of how important a character is in the storyline. Some narrative layers in a text are em-
bedded in others, which is particularly relevant for the position of narrating characters
in first-person novels. As narrating characters belong to the highest narrative layer,
they consequently ‘produce text that is not perceived by the characters’ that belong to
more embedded narrative layers (Van Boven and Dorleijn (2013): 33).
1
As such, ‘the
narrating instance is located on a higher textual level’ and is ‘above the world of the
characters’ who do not have a narrating role (ibid.). In this sense, it is logical to ascribe
a more central role to a narrating character in a first-person novel than to the other
characters, as the narrator is the one who is in the best position to control the flow of
information.
Focalisation is a narratological concept that is also applicable to the centrality of
characters. It was coined by the French structuralist Gerard Genette to distinguish
between who narrates and who perceives in a text (Genette (1972)). Others have
suggested revisions of the concept (e.g. Bal (1977), Nelles (1990), Jahn (1996)); the
revision that has become most popular is that of the Dutch scholar Mieke Bal. She
defined focalisation as ‘the relation between the vision and that which is “seen”,
perceived’ (Bal (2009): 145-146), which made it possible to discern hierarchical relations
between characters who occupy active focalising roles and characters who are mainly
in a passive position in which they are being focalised by other characters. The extent
to which a character features in active focalising roles is thus another indicator of
his/her place in the character hierarchy.
3 Centrality in Network Theory
Network theory has been occupied with the question of how to measure the centrality
of nodes in a network. Relational, networked structures are interesting in this respect
as they can yield insights into the centrality of certain actors as opposed to others.
The centrality of a node can be measured in a number of ways to consider different
aspects of the network structure. In 1978, the American sociologist Linton Freeman
observed that there is ‘certainly no unanimity on exactly what centrality is or on its
conceptual foundations, and there is very little agreement on the proper procedure
for its measurement’ (Freeman (1978): 217). He conceptualised three basic centrality
measures – degree, betweenness and closeness – which are still being used today, albeit
frequently in revised form, and which are thought to ‘cover the intuitive range of the
concept of centrality’ (idem: 237). It is worth mentioning that Freeman’s intent was
not ‘to “lock in” to any sort of ultimate centrality measure’ (idem: 217), as centrality is
a rather abstract concept and therefore hard to pinpoint statistically. Existing measures
as those used by Freeman at best help to clarify what might be understood as central,
but they do not necessarily give any definitive answers on which actors are most
important in a network.
Before Freeman’s innovative proposition, centrality was mainly viewed in terms of
1
Unless otherwise indicated, all translations are the authors’ own.
73
Figure 1: Adapted from Opsahl and Others (2010)).
degree, which is the most straightforward measure of centrality. In Figure 1, node
A has an advantage over B, C, D and E because it has more relations to others in the
network: A has a degree of 4, whereas B, C, D and E each have a degree of 1. The main
limitation of degree centrality, however, is that it does not take into account the overall
structure of the network. A node can be related to many other nodes but located in the
periphery of the network, which results in a situation where the node is far removed
from the opposite side of the network.
As an alternative to degree centrality, closeness centrality is defined as the sum of
distances to all others nodes in the network. An advantage of closeness is that it takes
into account the relative access that a node has to other nodes in the network. In
Figure 1, node A has a higher closeness than B, C, D and E, as it is directly connected
with its neighbours, whereas B, C, D, and E need to cross through A to reach a node
other than A. The disadvantage of closeness centrality, however, is that it cannot
properly be applied to networks that are not fully connected. By definition, nodes
in two disconnected components of a network are unable to reach one another, and
therefore closeness cannot be computed for the overall structure of a network with
disconnected components.
2
Freeman was the first to propose betweenness centrality, which computes the extent
to which a node lies on the shortest path between two other nodes. In Figure 1, node
A has a high betweenness centrality because it connects all four nodes with each other.
As it is applicable to networks with disconnected components, betweenness has an
advantage over closeness. However, as a metric, it is limited because nodes are often
not located on the shortest path between two others nodes. Because of that, B, C, D
and E, in Figure 1, all have a betweenness centrality of 0.
Network theory makes a distinction between unweighted and weighted graphs.
In a weighted graph, the edges represent the intensity with which two nodes are
connected. As the basic centrality measures of degree, closeness and betweenness are
devised for application to unweighted, binary networks, alternative metrics have been
2
In case a network has a lot of disconnected components, a convenient approach would be to
only compute closeness centrality for the largest component of the network.
74
proposed. Degree centrality has been redefined for weighted graphs by not focusing
on the number of relations but on the sum of the weights of those relations (Barrat et al.
(2004)). Dijkstra’s algorithm (Dijkstra (1959)), named after the Dutch computer scientist
Edsger W. Dijkstra, has been used to redefine closeness and betweenness centrality by
looking at the shortest paths in terms of distances (Newman (2001), Brandes (2001)).
As these new proposed metrics target primarily the weights and are less reliant on
the number of relations, a second redefinition was needed to take into account both
weight and number of relations (Opsahl and Others (2010)).
Every network thus demands a specific approach; there is no general method that
applies to every network. The first question should be which elements constitute the
network, the second how those elements are related. Then, it should be decided if
the network is binary and unweighted, or if the elements are gradually related to one
another. The appropriate centrality measures should be derived from the specific
nature of the network (weighted/unweighted, unipartite/bipartite
3
) and the question
through which is it approached, as not every centrality measure is relevant in all
possible instances.
In the following we will explore to what extent it is useful to make a synthesis
between the narratological and the network analytic approach to centrality. We do
so by considering literary texts as social networks made up of characters which can
be ranked according to the centrality measures described above. The structure of
these character networks will be adjusted to the mode of narration and focalisation
of the novels. In the context of character representation, narratology thus informs
a quantitative and statistical conceptualisation of centrality. Conversely, the study
of social networks of fictional characters is informed by some basic narratological
insights.
4 Data & Method
In order to test how a data-driven approach to character centrality might contribute to
the critique of literary representation, we devised a computational model that takes
into account both network theoretical and narratological considerations on mode of
narration and focalisation. There is an emerging branch of studies that apply network
analysis to fictional populations of characters (e.g. Alberich et al. (2002), Stiller et al.
(2003), Elson et al. (2010), Lee and Yeung (2012), Agarwal et al. (2013), Jayannavar et al.
(2015), Karsdorp et al. (2015b), Lee and Wong (2016), Moretti (2013):211-240,Rydberg-
Cox (2011), MacCarron and Kenna (2012)), though all use different methods for their
purposes. One of the main challenges for this type of analysis is the conceptual issue
of how to define and automatically identify characters in texts (Dekker et al. (2019)).
This can be done manually (e.g. Moretti (2013):211-240) or automatically (e.g. Elson
et al. (2010)). Vala et al. (2015) have shown that automatic detection is a difficult
task due to the poor performance of existing pronominal and coreference resolution
techniques.
4
Because of this poor performance, we do not aim for full coreference
resolution, but instead use a semi-automatic method that departs from a predefined
3
Unipartite networks exclusively consist of elements from the same category, e.g. people
connected to people. Bipartite networks consist of elements from different categories, e.g. people
connected to organisations. The number of elements in multipartite networks can be extended endlessly
in theory, but it is usually restricted to three different categories (tripartite).
4
An alternative approach to character detection is automatically classifying animacy in in texts
(Karsdorp et al. (2015a)).
75
set of characters. Inspired by narratological theories on what constitutes a character
(Herman and Vervaeck (2005): 60-61; Van Boven and Dorleijn (2013): 335), we define
characters as people or creatures which to a greater or lesser extent are presented as human,
existing of not more than a few linguistic features including one or more names. For each
novel, a list of names is created with Named Entity Recognition (NER); characters
whose name frequency is above a normalised threshold value (based on the number
of words of the text) will be regarded as characters.
5
The other challenge is how to define and automatically identify relational ties be-
tween those characters. One of the most used definitions of character relation frames
connections between characters in terms of conversations or dialogues (Stiller et al.
(2003), Elson et al. (2010), Lee and Yeung (2012), Moretti (2013): 211-240, Jayannavar
et al. (2015), Lee and Wong (2016)). The quantifiable unit of the conversation is, how-
ever, not the best indication for character interactions, as there are plenty of characters
that do not enter into a conversation but are related to one another in some other way.
For instance, two characters with family ties might never speak to each other, but such
a relation should definitely be regarded as a character relation. Another way to define
relational ties is in terms of co-occurrence in the same window of N words, sentences,
paragraphs or chapters (Alberich et al. (2002), Grayson et al. (2016)). Defining character
relations in terms of adjacency in the text will be able to capture more instances of
character interaction than when it is defined in conversational terms. This is the most
bottom-up definition of character relations, as characters do not have to communicate
in a literal sense (as is the case in conversation networks) to be considered as having
some form of interaction.
Based on these considerations, we operationalise the strength of character relations
through co-occurrences of character name variants in a window of N tokens. We experi-
mented with different window units and sizes for different types of novels to find the
‘sweet spot’ where not too many and not too few character interactions are detected
(cf. Grayson et al. (2016)). However, such a sweet spot is different for every novel. In
order to be able to compare the novels, we decided to use the same window unit and
window size for every novel. As sentences are the smallest linguistic structures which
are semantically meaningful in themselves (cf. Mann and Thompson (1988)), we used
sentences as the window unit, which we tokenized using Ucto.
6
The window size was
set to two sentences, as semantic relations are known to extend over two sentences
through connectives (cf. Bluhdorn (2010)).
7
We devised a customised co-occurrence
approach for each narrative mode, which we describe in detail below.
We have used a sample corpus of 170 contemporary Dutch novels, consisting of all
submissions to the 2013 Libris Literatuur Prijs, one of the most prestigious literary
prizes in the Dutch language area. This prize is awarded to novels published in the
year before, in this case in 2012. In that year, 1,397 Dutch novels were published;
our sample of 170 novels thus makes up 12.2 percent of the total number of novels
5
There are several NER-tools available, but not all are suitable for the same task. NER-tools
have to be trained for specific languages, and their accuracy depends on the nature of the training data
(e.g. a tool trained on newspaper articles performs badly on literary fiction). For the current research
the Namescape-tagger is used, which is trained on Dutch literary fiction and which is demonstrated to
be the most accurate for the present purposes, although it is still not perfect as a F1-score of 0.72 was
reported (Smeets (2017)).
6
https://github.com/proycon/python-ucto, last accessed: 3-7-2018.
7
As the plain texts of the novels in our corpus are unstructured, we could not rule out the
possibility that characters co-occur in two sentence windows that transcend the boundaries of a paragraph
or chapter. We are aware that this creates noise, as in those cases it could be argued that there is no
meaningful interaction between characters.
76
Figure 2: Visualisation of database linkage
published in that year.
8
With a few exceptions, these novels belong to the genre of
literary fiction.
9
In earlier research (van der Deijl et al. (2016)), the following demographic information
for 1,176 characters in the corpus was manually gathered, if known: gender, age,
country of descent, city of descent, country of residence, city of residence, profession.
In more recent research (Volker and Smeets (2019)), the number of characters was
increased to a total of 2,137 characters and semi-automatically enriched with 4,459
relational ties between each character. These relational ties were grouped in one of
the following five categories: family, friend, lover, colleague, enemy.
10
These character
metadata were stored in four interrelated database tables (see Figure 2).
Lists of all variants of a character’s name were automatically generated with named-
entity recognition and were stored in a table called NAMES. BOOKS contains all
relevant metadata of the novels, such as title, the name, gender and age of the author,
the publisher and the filename of the digital version of the novel. NODES contains
all relevant metadata of the characters, such as name, gender, country of descent, city
of descent, country of residency, city of residency, education and profession. EDGES
contains all relevant metadata on the character relations, such as the specific nature of
the relation (friend, family, enemy, lover, colleague). All tables are linked to one another
through a unique book id. NAMES, NODES and EDGES are also connected through a
character id. The character networks are computed through an Object-Oriented model
written in the Python programming language, consisting of three main classes: Book,
8
This number is based on all Dutch language novels published in 2012 with NUR-code 301
(literary fiction), that is, 1,780 in total. These include 383 duplicates or reissues, which were subtracted
from the total number. Thus, the total number of ‘original’ Dutch literary fiction published in 2012 is
1,397.
9
For a list of all 170 novels, see: http://www.librisliteratuurprijs.nl/2013-groslijst.
10
We used top-down relational labels assigned to characters by two expert annotators. These
annotators based their annotations on rather narrow definitions: e.g. the label ‘enemy’ was only assigned
when the relation is clearly hostile, the label ‘friend’ when the relation is clearly friendly. Differences in
annotations between the annotators were resolved through discussion. The annotators also accounted
for changing relations between characters. In those cases double labels were assigned, such as Col-
league_Enemy. Double labels were also assigned when the nature of the relation changed over time,
such as friends becoming enemies.
77
Character and Network.
11
Each book in the corpus has a unique id from 1 to 170. Every character in the corpus
has a unique character id that corresponds to a book id stored in database BOOKS. For
instance, De lichtekooi van Loven by Ineke van der Aa is represented by the book id 1.
In database NODES, character ‘Louise’ is represented by this same book id followed
by character id 1 and her name (1_1_Louise). In database NAMES, this same unique
identifier is followed by every name variant of the character. The name variants for
this character are ‘Louise’, ‘Louisje’ and ‘Louiseke’, which is represented in NAMES as
1_1_Louise_Louise, 1_1_Louise_Louisje and 1_1_Louise_Louiseke. Each novel’s text
was then searched for each of these name variants, after which these variants were
replaced by the unique character identifier.
12
As such, the locations of each character
in the text were automatically identified.
The corpus was divided into three sub-corpora based on their mode of narration:
third-person, first-person, multi-perspective. Third-person novels are narrated by an
anonymous narrator who follows one main character. First-person novels are narrated
by an I-narrator. Multi-perspective novels are narrated by multiple narrators, either in
third or first person. For every subcorpus a slightly different co-occurrence approach
was used based on the specific mode of narration. For all novels, irrespective of their
mode of narration, relations between characters were pre-established when they were
annotated with one of the relational labels stored in EDGES (friend, family, lover,
enemy, colleague). In all cases, the procedure below was used to establish the weight
of the relations.
13
Third-person novels (63 novels)
For every character in the novel, a sliding window
approach was used in which co-occurrences of two characters were mapped in a
window of two sentences. Whenever two characters occur in the range of the
same two sentences, a relation between those characters was established. The
more often such co-occurrence takes place, the stronger their relation becomes.
First-person novels (73 novels)
a) As the first-person narrator has by definition high
centrality in narratological terms, the relations of the first-person narrator with
all other characters were simply defined by counting every occurrence in the
novel of characters other than the first-person narrator. As every character is
embedded in the narration of the first-person narrator, it can be argued that
every character occurrence represents a relational tie with the first-person
narrator. The more often a character occurs in the novel, the stronger its relation
with the first-person narrator.
b) For every character other than the first-person narrator, a sliding win-
dow approach was used in which co-occurrences of two characters were mapped
in a window of two sentences. Whenever two characters occur in the range of
the same two sentences, a relation between those characters was established.
11
All software and data are accessible through the following open access GitHub repository:
https://github.com/roelsmeets/character-networks. This repository does not contain the corpora
because of copyright issues.
12
A similar approach is used by Grayson et al. (2016): 4, who replace character aliases with a
character’s name.
13
In some cases, two characters have a relational label such as “family” assigned to them while
the weight of their relation is 0. This is possible as characters do not have to be adjacent in the text to have
a family tie, just as people in real-world networks can be family without being in each other’s physical
presence or without talking about each other.
78
The more often such a co-occurrence takes place, the stronger their relation
becomes.
Note that this approach will in most cases rightfully lead to relatively strong
relations between the first-person narrator and all other characters, whereas this
is not the case for the relations between and among all other characters.
Multi-perspective novels (34 novels)
For each of these novels, student assistants an-
notated where a character perspective begins and ends in the text. These an-
notations also contain information on the narrative mode and focalisation: a
first-person or third-person narration was annotated as such, and for third-person
narration the main focaliser was annotated. On the basis of those annotations,
each novel was divided into separate sections. For sections narrated in first
or third person, the first- or third-person method was applied. After that, the
co-occurrence counts between characters were aggregated for all the separate
sections.
All these relations are symmetrical, and thus undirected. This means that the charac-
ter relations are not regarded in terms of directionality, which is a logical consequence
of the co-occurrence approach, as adjacency is a priori a symmetrical issue. Further-
more, the resulting network, with characters as nodes and character relations as edges,
will both be undirected and weighted. Not every relation between any two characters
will have the same status, as the strength of a relation is increased when two characters
co-occur more often in the novel.
With Python’s software package networkx,
14
the resulting networks for each indi-
vidual novel were used to rank the characters on the basis of five centrality metrics.
Among those metrics are the above described degree, betweenness and closeness
centrality, as well as eigenvector and Katz centrality, two metrics on which Google’s
PageRank algorithm is based. PageRank is used by Google’s search engine to rank
web pages by relevance. PageRank, eigenvector and Katz are all based on the same,
seemingly circular assumption that a node in a network becomes more important
when it is connected to other important nodes (Page et al. (1998)). The computation
of all these metrics was based on the weighted edges. Then, a regression analysis was
carried out to see which of the demographic variables (gender, descent) is the best
predictor for a character’s place in the rankings.
5 Results regression analysis
Because of the exploratory nature of the present research and the absence of prior
research on this topic, we did not have any formal hypothesis about which demographic
factors would possibly determine a character’s place in the rankings. However, we
preferred to not just enter all possible variables into the regression equation as this
would have possibly obscured the results of the analysis. Therefore, we formulated
a non-formal hypothesis based on traditional, non-statistical research in the critique
of literary representation. Several studies suggest that female characters and/or
characters of mainly non-Western descent are often represented in a stereotypical
manner and are therefore likely to be staged in less central, more marginal positions in
literary texts (e.g. Pattynama (1994), Meijer (1996a), Meijer (1996b), Pattynama (1998),
Minnaard (2010), Meijer (2011)). Gender and descent might therefore be possible
14
https://networkx.github.io/, last accessed 7-5-2018.
79
Figure 3:
Gender and descent distributions among characters in the corpus (
N = 2137
). The numbers
are based on types, not on tokens.
predictors of a character’s position in the rankings. Based on descriptive statistics of
our data, we suspected that male and non-immigrant characters would end up as
more central,
15
as these types of characters are simply more present in the dataset
(see fig. 3).
16
More precisely, we hypothesise that both male characters and non-
immigrant characters will score higher on the centrality metrics than female characters
and characters with a migrant background.
For each of the five centrality metrics (degree, betweenness, closeness, eigenvector,
Katz), a multiple linear regression was conducted to predict characters’ centrality
scores based on their gender and descent. Gender is coded as 0 for male and 1 for
female. Descent was coded as 0 for non-immigrant and 1 for immigrant. As our aim
was to generalise across all novels, we did not include the division into sub-corpora
(third-person, first-person, multi-perspective) in the statistical model.
No significant results were found for betweenness, closeness and eigenvector central-
ity. Gender and ethnicity are thus no predictors for characters’ scores on betweenness,
closeness and eigenvector centrality.
However, significant results were found for degree and Katz centrality. First, for
degree centrality, a significant regression equation was found (F(2, 2128) = 6.424, p
< 0.01), with an R
2
of 0.006. Characters’ predicted degree centrality is equal to a B
value of 0.428 + 0.024 (GENDER) + 0.059 (DESCENT) (see fig. 4). This means that, on
degree centrality (on a scale from 0 to 1), female characters scored 0.024 higher than
male characters, and immigrant characters scored 0.059 higher than non-immigrant
characters.
Secondly, for Katz centrality a significant regression equation was found (F(2, 2128)
= 6.124, p < 0.01 ), with an R
2
of 0.006. Characters’ predicted Katz centrality is equal to
a B value of 0.272 + 0.009 (DESCENT) + 0.007 (GENDER) (see fig. 5). This means that,
15
Referring to characters who either have or do not have a migrant background, the terms
‘immigrant’ and ‘non-immigrant’ are used in a loose sense. In this article, immigrant characters can
also refer to characters who are born in the Netherlands or Belgium but whose parents migrated to the
Netherlands or Belgium. In this broad definition, immigrant characters are considered to have some
sort of bond with a socio-cultural tradition that is not the same as their current country of residence. We
chose the Netherlands and Belgium as a point of departure as the books in the corpus are either written
by Dutch or Flemish authors who operate in a shared literary field of Dutch literature.
16
A chi-square goodness of fit test was calculated comparing the occurrence of male and female
characters with the hypothesized occurrence of a 50-50 gender distribution. Significant deviation from
the hypothesized values was found (
χ
2 (1) = 82,030, p < .001). Also, a chi-square goodness of fit test was
calculated comparing the occurrence of characters with a Dutch/Belgian and Other country descent with
the hypothesized occurrence of a an equal distribution among those categories. Significant deviation
from the hypothesized values was found (
χ
2 (1) = 1350,773, p < .001). This means that the 40-60 gender
divide and the 89,8-10,2 divide in descent are not due to chance, but is a statistically significant difference.
80
Table 1:
Linear model of predictors of degree centrality, with unstandardized coefficients (columns 2 and
3) and standardized coefficients (column 4)
Unstandardized Standardized
Model B Std. Error Beta Sig.
1 (Constant) 0.438 0.006 0.000
Descent_revised 0.058 0.019 0.065 0.003
2 (Constant) 0.428 0.008 0.000
Descent_revised 0.059 0.019 0.066 0.002
Gender 0.024 0.012 0.043 0.048
Table 2:
Linear model of predictors of Katz centrality, with unstandardized coefficients (columns 2 and 3)
and standardized coefficients (column 4)
Unstandardized Standardized
Model B Std. Error Beta Sig.
1 (Constant) 0.273 0.002 0.000
Gender 0.007 0.003 0.061 0.005
2 (Constant) 0.272 0.002 0.000
Gender 0.007 0.003 0.062 0.004
Descent_revised 0.009 0.004 0.045 0.038
on Katz centrality (on a scale from 0 to 1), immigrant characters scored 0.009 higher
than non-immigrant characters, and female characters scored 0.007 higher than male
characters.
These findings suggest that our initial hypothesis, based on traditional critiques
of literary representation, should be rejected. Contrary to what we expected, female
characters and immigrant characters scored higher, at least on two of the five centrality
metrics used in the analysis. Furthermore, it should be noted that a higher frequency
distribution of a character type does not necessarily lead to a more central position
in a character network, as the results of the regression analysis has shown. Although
male and non-immigrant characters are more present in the corpus, they do not end
up as more central in network analytic terms. The question remains as to how these
results can be interpreted in a close reading context.
6 Narratological evaluation
The quantitative representational patterns suggested by the outcome of the multiple
linear regression require a narratological evaluation, as it is unclear what their sig-
nificance is for the critique of literary representation. In concrete terms, the outcome
for degree centrality is that female and immigrant characters have significantly more
relations than male and non-immigrant characters. More specifically, women and
characters with a migrant background often co-occur with a wider range of fellow
characters in the novels. The higher scores of female and immigrant characters on Katz
centrality indicate that they often co-occur with characters who also have relatively
81
high Katz centrality. In sum, female and immigrant characters have both more relations
in general and more relations with important characters.
In order to make sense of this pattern, we subsequently conducted a small narrato-
logical exploration of character centrality in one novel from the corpus and confronted
it with the results of the statistical analysis. As a case study, we used a novel that
thematises both gender and ethnicity, as these issues of representation were taken as
points of departure for the regression analysis. For the sake of brevity, we only used
the two concepts of narrative mode and focalisation as points of departure. Note that
there is a wide variety of other narratological concepts and perspectives that might
potentially lead to alternative insights.
Eus (2012) by Özcan Akyol is a semi-autobiographical, first-person novel, in which
the reader follows the life of the first-person narrator Eus, the son of Turkish immigrants
living in Deventer, a small city in the Netherlands. Eus gets involved in criminal
activities and ends up in jail, where he starts a writing career. This plotline foregrounds
the theme of upward social mobility: a character with a migrant background who
initially has a hard time finding his way in Dutch society eventually finds his creative
ambition and becomes a successful author.
This theme is underscored by Eus’s foregrounding of the economic and social
hierarchies that exist between Dutch people and people with a migrant background.
At the beginning of the novel, Eus states that he and his friends ‘... didn’t dare to go to
the better neighbourhoods’, although they ‘knew that they existed’ (Akyol (2012): 24).
An implicit opposition is thus postulated between ‘better neighbourhoods’ and Eus’s
own rough neighbourhood. Later on, Eus is more explicit when he characterises the
‘indigenous youth, rich kids’ as ‘white scum’ (idem: 120).
Another less prevalent, but latently present theme in the novel is the way men with a
migrant background engage with (Dutch) women. Throughout the novel, women are
treated with little respect by Eus and his friends. Female characters are either objects
of sexual desire or considered a man’s possession. They are repeatedly referred to as
‘whores’ (idem: 36, 58, 85, 145) and ‘sluts’, or variants of the term (idem: 43, 57, 62, 86,
145, 157, 176, 253). The male characters seem mostly interested in whether or not a
woman is ‘fuckable’ (163). In general, women are constantly objectified. The following
quote is a good example:
Sometimes I stared out of the window for hours, in search of the hottest girls in school,
about whom I then started fantasising. How beautiful they were! Nice tits! Nice ass! (idem:
50)
On the basis of such thematic cues, one could argue that two hierarchical oppositions
take shape in the narrative:
1. immigrants ←→ non-immigrants
2. male ←→ female
Although there are some indications in the quotes given above, it is not evident
what the specific hierarchy of these relations might be. The two basic concepts of
narrative mode and focalisation might help to clarify this. First of all, the novel is
narrated by Eus, which means that he controls the flow of information in the narrative.
It is a logical consequence of the I-narration that Eus decides which events to report
and which to leave out. When he, e.g., reports that ‘I was born and raised in Koekstad,
a small town by the IJssel, exactly on the border of two eastern provinces’ (idem: 13),
82
he chooses to use an alias (‘Koekstad’) for a town the reader might know as Deventer.
As an I-narrator, Eus is able to manipulate the narrative at will.
Furthermore, he is also the main focaliser: the narrative events are filtered through
his perceptions. This means that the description of events is not neutral but colored by
the vision and judgement of Eus. The following two sentences are illustrative:
The next two years I went to the Hegius school, where I was surrounded by the beautiful,
posh girls who pursued the highest level of education. Rumour had it that these girls had
an above-average interest in foreign boys because they never saw those types of boys (idem:
37)
A whole group of girls is lumped together by Eus and characterised as ‘beautiful’
and ‘posh’. Eus thus foregrounds their physical appearance and highlights their
poshness, thereby suggesting that these girls are spoiled rich kids. In the second
sentence he repeats a rumour relating to their supposed sexual interest in boys with a
migrant background. These two sentences show an extremely biased representation
of a specific type of characters (in this case: female, highly educated).
These basic narratological observations are of major importance for the interpretation
of character hierarchies in the novel. As first-person narrator and main focaliser Eus is
both a character with a migrant background and male, so the non-Dutch and male
perspectives are a priori more dominant than the Dutch and female perspectives.
Taking into account that Eus’s friends and fellow criminals (Kosta, Ata, Meltem, Mahir)
are also predominantly of a migrant background and male, one could argue that the
centre of gravity lies with non-Dutch and male characters.
But although this interpretation of character centrality is based on plain narratologi-
cal insights, it relies heavily on qualitative evidence (i.e. the few quotes we used to
illustrate our point). A more data-driven and quantitative approach might potentially
shed a different light on the question of character centrality in this novel. How does
our network analytic approach relate to this narratological approach? Table 3 shows
the characters in the novel ranked by their scores on degree centrality.
First of all, this novel conforms to the general pattern as observed in the regression
equation only with regards to descent. 12 of the 21 identified characters have a migrant
background, and they are higher in the rankings than the Dutch characters, which is in
line with the general pattern according to which characters with a migrant background
have significantly higher degree centrality.
However, with regards to gender, the novel deviates from the pattern. 14 of the 21
identified characters are male, and they occupy higher positions in the rankings on
degree centrality, indicating that the male characters in Eus have more relations than
female characters.
For Katz centrality, a similar pattern emerges. Table 4 lists the characters in the
novel ranked by their scores on Katz centrality. Here, too, both characters with a
migrant background and male characters occupy higher positions in the rankings than
non-immigrant and female characters. The first types of characters are thus connected
to more important characters than the latter.
Interestingly, these rankings are in line with the findings of our narratological
approach to character centrality in the novel. Our narratological argument that the
perspectives of male characters with a migrant background are dominant in terms
of narrative mode and focalisation is backed up by our quantitative argument that
these types of characters have higher scores on degree and Katz centrality. In this
specific case, our narratological argument that the male and immigrant perspectives
are dominant does not conflict with the character rankings for this particular novel.
83
Table 3: Characters in Eus (2012) ranked by degree centrality score
Name Gender Descent Degree
1 Kosta male immigrant 0.65
2 Kareltje male non-immigrant 0.55
3 Eus male immigrant 0.50
4 Turis male immigrant 0.40
5 Meltem male immigrant 0.40
6 Ata male immigrant 0.40
7 Mahir male immigrant 0.35
8 Selma female immigrant 0.30
9 Metin male immigrant 0.30
10 Haakneus female immigrant 0.30
11 Levine female non-immigrant 0.30
12 Theo male non-immigrant 0.15
13 Nathan male non-immigrant 0.15
14 Eef female non-immigrant 0.15
15 Inez female non-immigrant 0.1
16 Ömer male immigrant 0.1
17 Angelo male non-immigrant 0.1
18 Vinny male non-immigrant 0.1
19 Osman male immigrant 0.05
20 Daphne female non-immigrant 0.05
21 moeder Eus female immigrant 0.00
84
Table 4: Characters in Eus (2012) ranked by Katz centrality score
Name Gender Descent Katz
1 Kosta male immigrant 0.218218982823223
2 Mahir male immigrant 0.21821840342212764
3 Eus male immigrant 0.2182184034212662
4 Kareltje male non-immigrant 0.2182182875401764
5 Turis male immigrant 0.21821822960063142
6 Ata male immigrant 0.218218113719957
7 Meltem male immigrant 0.2182179398985992
8 Selma female immigrant 0.21821782401818632
9 Haakneus female immigrant 0.218217824018094
10 Levine female non-immigrant 0.21821782401787862
11 Metin male immigrant 0.2182178240177709
12 Theo male non-immigrant 0.2182177660783798
13 Nathan male non-immigrant 0.21821770813789643
14 Eef female non-immigrant 0.21821765019709
15 Inez female non-immigrant 0.21821759225711432
16 Angelo male non-immigrant 0.218217592257022
17 Vinny male non-immigrant 0.218217592257022
18 Ömer male immigrant 0.21821759225692972
19 Daphne female non-immigrant 0.21821753431670787
20 Osman male immigrant 0.21821753431661559
21 moeder Eus female immigrant 0.21821747637639374
85
In sum, our narratological evaluation highlights two important points with regards
to the interpretability of our regression analysis. 1) A narratologically oriented anal-
ysis of a case can provide a qualitative contextualisation of a statistical argument.
The mode of narration and focalisation in Eus illustrate the dominance of the male,
immigrant perspective, which is supported by the characters rankings for the novel. A
narratological argument may also give nuance to or conflict with a statistical argument,
but that is not the case for this specific novel. 2) A specific novel (in this case, Eus)
can very well deviate from an observed general statistical pattern. Characters with
a migrant background score higher than Dutch characters in the novel in terms of
network centrality, which is in line with our regression model. But contrary to the
general pattern, the male characters score higher than the female characters. This
highlights the importance of a qualitative contextualisation of the statistical analysis.
7 Conclusion
In this contribution, we have demonstrated that a data-driven and empirically in-
formed approach to character centrality informs the ideological critique of literary
representation in at least three ways.
First, instead of focusing on a limited number of cases, general patterns can be
discerned in a larger corpus that represent a specific literary-historical period, which
can lead to new, surprising insights. Contrary to what is suggested by the wide range
of ideologically oriented close readings of character representation in literature, our
results suggest that female and immigrant characters take up a more central position in
the social networks of present-day Dutch literary fiction than non-immigrant and male
characters, statistically speaking. This remarkable outcome requires an explanation,
particularly in light of the highly imbalanced frequency distribution of immigrant
and non-immigrant characters in the corpus. Almost 90% of the characters in the
corpus are non-immigrants, but our regression model suggests that immigrants are
more central in the networks than non-immigrants. Possibly, these higher centrality
scores might be explained by the probability that novels that thematise descent, and
stage a higher number of immigrants, also ascribe more central roles to them. Overall,
the novels have fewer immigrant characters (only around 10% of all characters have
an immigrant background), but these immigrants score higher on degree and Katz
centrality. Something similar holds for female characters: there are fewer female
characters than male characters in the corpus (almost a 40-60 ratio), but they have
relatively high centrality values. In order for immigrant or female characters to be
central in network theoretical terms, a high frequency of occurrence is not a necessary
prerequisite as long as they interact with a high number of other (central) characters.
This is a possible explanation of the discrepancy between the descriptive statistics and
the outcome of the regression equation.
Second, combining a narratological close reading with network analysis enables a
formalisation of abstract terms as importance, influence or power that are typically
used in a strict metaphorical sense. Our case study on Eus illustrates that ideologically
oriented interpretations regarding gender or ethnicity can be either backed up or
nuanced by network statistics which make such interpretations less susceptible to
unarticulated and implicit presuppositions.
Third, this study demonstrates that quantitative statistical patterns only make sense
when confronted and contextualised with close readings of specific cases. Statistical
trends might indicate general patterns of literary representation, but they can only
86
serve as an analytic backdrop for the individual analysis of particular novels. The
novel Eus, for instance, lives up to the pattern only with regards to descent but not with
regards to gender. The extent to which a single novel either conforms to or deviates
from the general pattern can then be used to determine the particularity of a certain
aspect of representation.
Our main contribution to the field of Digital Literary Studies lies in bringing to-
gether the methodological toolkits of narratology and social network analysis, which
often seems to be lacking in data-driven approaches to networks in literary texts. As
opposed to other network extraction methods, we propose a method that departs from
domain knowledge and combines this with a bottom-up operationalisation of character
interactions. As such, this article is situated within the mixed-method framework of
the text-oriented Digital Humanities, and provides an argument for more strongly
connecting qualitative and quantitative strands of research in the field.
8 Acknowledgements
The authors would like to thank annotators Maartje Weenink and Lisa Rooijackers for
their precise annotations and Lucas van der Deijl and the anonymous reviewers for
commenting on earlier versions of this article.
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