=Paper=
{{Paper
|id=Vol-3301/Paper3
|storemode=property
|title=Social Network Analysis and Co-Occurrence: Identifying the Gaps
|pdfUrl=https://ceur-ws.org/Vol-3301/paper3.pdf
|volume=Vol-3301
|authors=Jens Dörpinghaus
|dblpUrl=https://dblp.org/rec/conf/ki/Dorpinghaus22
}}
==Social Network Analysis and Co-Occurrence: Identifying the Gaps==
https://ceur-ws.org/Vol-3301/paper3.pdf
Social Network Analysis and Co-Occurrence:
Identifying the Gaps
Jens Dörpinghaus1,2
1
Federal Institute for Vocational Education and Training (BIBB), Bonn, Germany,
2
University of Koblenz, Germany
Abstract
Social Network Analysis is widely used in the humanities. However, historical and narrative texts
in ancient languages are usually challenging for NLP (natural language processing) methods and AI
(artificial intelligence) technologies developed for modern languages due to their complexity and missing
models. In this article, we will focus on biblical texts. Here, linguistic resources are already available.
However, no approaches for the automated linking of actors and other information, e.g. spatiality, are
available. Thus, in this paper, we will analyze if co-occurrence might improve the linking of data in
manual exegetical work. We will provide a detailed analysis and evaluation to identify the gaps for
further research. The results of this paper are not limited to theology, but can be applied in all fields
working with textual information.
Keywords
Text Mining, NLP, Social Network Analysis, Co-Occurrence
1. Introduction
1.1. Motivation
Social networks play an important role in the social sciences and have been widely used for
several decades, both in theory and in application. Understanding social interactions and
networks and how they influence society are important issues. In the last few years there
has been a growing interest in using social networks in historical sciences. Quite recently,
considerable attention has been paid to social networks in religious studies and especially in
theology. It was shown that social network analysis (SNA) helps to understand the ancient
literature on the early religious movements and social identity.
Collar [1], for example, as an archaeologist, was among the first to combine religious studies
and archaeology using SNA. In her work “Religious Networks in the Roman Empire” she
examines why some cults and religions within the Roman Empire either vanished or became
meaningless while maintaining the same popularity. After an introduction, she examines various
cults, including the Jewish diaspora after 70 a.d. It is not yet known whether the SNA can
be generalized in all cases, since the lack of data is a challenge. Regarding New Testament
research the studies of [2], [3] and [4] should be mentioned. One of the main issues in what we
2nd Workshop on Humanities-Centred AI (CHAI-2022)
" jens.doerpinghaus@bibb.de (J. Dörpinghaus)
0000-0003-0245-7752 (J. Dörpinghaus)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
Biblical Texts Social Network
Transferring results
Text Mining Analysis back to the initial
discipline
Figure 1: Illustration of the proposed workflow. Narrative texts are transformed to social networks,
which can be analyzed with methods from SNA. However, in the humanities these results must be
interpreted within the framework of the initial scientific domain. In this work, we focus on the first step.
do not know about a social network is in particular what we can not reproduce. Thus, “network
analysis needs to be embedded within traditional research both to produce results and not to be
ignored. For this achievement, Collar should be praised.” [5, 226]
Most studies from historians and exegetes have only focused on understanding how the
New Testaments constructs networks and identity. Thus, on the one hand we have developed
mathematical computational social networks using exegetical methods. On the other hand
these results should also raise new questions, show new perspective on biblical texts, and in
particular on how these texts can be analyzed with AI methods. Previous work has been limited
to only one of these goals, while very little work has been carried out on AI approaches for
understanding biblical texts. In particular, techniques to build a large computational social
network of early Christianity based on biblical, or early church texts, and other sources are
time-consuming and require an interdisciplinary approach.
From a linguistic perspective, SNA always includes persons. These may well be fictitious or
the information about them can be worked out by exegetical steps from historical sources, which
Rollinger [6] did for the epoch of antiquity. Thus, the social network paradigm can technically
be applied to narrative texts without any problem. As an example for a first systematization
of these relations, the so-called figure configuration which Cornils [7, 75] uses for Acts may
serve: This is a pure listing of characters appearing at the same time in a narrative. Thus, the
computer-based evaluation of this data already used in the literary analysis is merely another
logical step.
However, since little work has been done in the field of AI approaches for biblical texts, see
[8] and [9], there is a gap in research. While more digital methods to analyze the results of
manually curated analyses are used within theology, there is an important overlap to other
methods from the humanities and their approaches to understand texts. But no evaluation of
these methods are yet carried out.
This paper seeks to address these gaps on a particular research question: Can we use AI
methods within the field of theology on biblical texts to build a social network on these narrative
texts to apply methods from SNA?
1.2. Research Question
This work is embedded within the steps to generate a complete computational social network
reconstruction of biblical texts, in particular Luke-Acts, for an analysis with various sociological
distance measures to establish a better understanding of the biblical text. In Figure 1 we present
the ideal workflow which comprises two steps: (1) building a social network, and (2) analyzing
the social network. However, in this paper, we focus on the first step. Since we have already
stated that basically no AI methods for the automated detection of figure constellations exist
– we will prove this statement in the next section – we will present some naive algorithm to
detect the challenges, gaps, and problems to build a social network. In addition, we will provide
a detailed quality control on a manually curated social network representation of the Gospel
of Luke and Acts with classical exegetical methods. Exegetical methods are used to explain or
interpret – not only religious – texts and literature within a given hermeneutic framework.
2. Related Work
Network approaches have been used in historical studies for some decades. Here they are often
called historical network analysis (HNA). Reitmayer and Marx [10] note that many methods are
used and no common formal structure exists. Only selected methods of network analysis are
used, a full network analysis as it is conducted within the social sciences is usually not being
carried out. In particular, a subset of literature uses the term “network” without using methods
from SNA or HNA, see for example van de Kamp [11].
Networks in early Christianity have not yet been fully investigated. Duling [2, 136] sum-
marizes the situation: “interest in SNA by Biblical scholars has been sporadic, but steady, and
is apparently growing”. First approaches can be found in Thompson [12], who examines the
communication of information in the network of early Christians between the years 30 and 70
a.d.Further attempts to explore these questions with the help of social network analysis were
carried out in the work of Duling [13] and Duling [14] which are entitled “The Jesus Movement
and Social Network Analysis”. In general, Dulling’s work remains unfinished. Another scholar
working with SNA is McClure1 who draws her final observations in [17, 35]: “The results
provide a unique window into the relational dynamics portrayed by the Gospels, producing a
variety of insights, some which may not surprise biblical scholars but others which hopefully
will inspire further consideration.” However, to sum up, a complete computable network of
early Christianity according to the biblical texts is still missing.
Within narrative studies, a character is a main (or minor) actor described in the text. This is
equivalent to the actor in SNA. Narrative criticism provides a more detailed view: “Characters
reveal themselves in their speech (what they say and how they say it), in their actions (what
they do), by their clothing (what they wear), in their gestures and posture (how they present
themselves).” [18, 121] Resseguie also points out a social perspective by mentioning their
position within society. Thus, it is also important to think about the constellation of characters,
which means their position in a network2 and their relation to the plot.
The character analysis can be separated into quantitative and qualitative questions: When is
a character present (in drama: “stage presence”) and with whom does he interact? Qualitatively,
1
She worked with a harmonized version of all gospels and was first working on support, conflict and compassion
[see 15]. After that, she investigated subgroups and balance [see 16]. While the methodological approach remains
somehow unclear (for example the data is changed which makes the studies incomparable), she carries no detailed
discussion on her choice of methods.
2
This equivalent to Figurenkonstellation found in Finnern and Rüggemeier [19, 204].
one can also ask about content (the “character speech”) or about characterizations. The first is
answered by the “figure configuration” and its “configurational structure”: In the first, the person
and their interactions are inferred; in the second, they are juxtaposed. While the extraction
of characters as word entities is not very difficult, the accurate analysis of interactions is
challenging. Therefore, current studies focus on “co-presence”. Rarely are models explored to
precisely describe these interactions, and they are usually limited to actor lists or dramas, see
[20] or [21]. In New Testament studies, figure constellations have been generated manually so
far, see [7],[9], and [22]. However, co-presence is more than co-occurrence, which describes
only those terms which explicitly occur in the same sentence.
3. Methodology
3.1. Data
Here, we will focus on the Greek text, and English Bible translations, although this approach
can be used for any other language with linguistic annotations. There are several software
packages available to access Biblical texts. Some commercial software like Logos offer no or
only very limited access to their API3 . Thus, we did our work on the basis of the SWORD
Project, which offers a full API available under GNU license4 . As a basis for the Greek text, we
used the SBLGNT 2.0 from Tyndale House, based on SBLGNT v.1.3 from Crosswire. This text
is with some minor changes comparable to the Nestle-Aland/United Bible Societies text. The
English texts are based on and ESV (English Standard Version, 2011). Beside of them, all data
is available with a free license. See http://www.crosswire.org/sword/modules/ for details of
these packages. Since these texts are already annotated with linguistic information and contain
Strong’s annotations referring to the original Greek term, several components of NLP-pipelines
like POS-tagging, lemmatization, and NER can be omitted. There are several annotations which
can be displayed in different ways.
We will use annotations for extracting information, storing and comparing them. However,
while these dictionary annotations allow the processing of terms with their linguistic infor-
mation, we still have no information about whether a word refers to a person, a location or
other entities. To collect the training data, we could use the complete New Testament texts
mentioned above. This leads to 7,957 verses in each version. There are 5,624 entries in the
Strong’s dictionary. However, we will now discuss the limitation of our analysis to those parts
with evaluation data.
3.2. Evaluation data
To overcome the limitations mentioned above, we will proceed with a manually curated network
of the Gospel of Luke and Acts. The first one comprises 99 nodes and 628 edges, the network
for Acts comprises 126 nodes and 646 edges, for details we refer to [22] and [8]. We will limit
our analysis to these two books for an evaluation with this manually created network. The
3
See for example https://wiki.logos.com/Logos_4_COM_API and [23].
4
See http://crosswire.org/sword/index.jsp
|𝑉 (𝐺)| |𝐸(𝐺)| |𝐸(𝐺)| Precision Recall 𝐹1 Score
Gospel of Luke 99 628 4223 0.62 0.56 0.58
Acts 126 646 7229 0.71 0.53 0.61
Table 1
Number of nodes |𝑉 (𝐺)| and edges |𝐸(𝐺)| in the network, and number of edges not in the network but
in the complementary graph |𝐸(𝐺)|. We present precision, recall and 𝐹1 Score for the co-occurrence
approach.
networks provide a list of expected actors, locations, and concepts. Thus, it already limits the
output of the algorithmic approach, which we will discuss in the next subsection.
3.3. Algorithmic approach
As we have discussed earlier, there are currently no AI approaches available for stage presence
of actors [9]. Thus, our initial algorithmic approach is based on the following observations:
Given two actors and their Strong-number, for example, Paul (strong:G3972) and Barnabas
(strong:G0921): Which verses have a co-occurrence of both actors? Given a corpus C of texts
with sentences 𝑠 ∈ C, we will denote this value with 𝑐(𝑎, 𝑏) for given actors 𝑎 and 𝑏:
{︃
0 ̸ ∃𝑠 ∈ C : 𝑎 ∈ 𝑠, 𝑏 ∈ 𝑠
𝑐(𝑎, 𝑏) =
1 ∃𝑠 ∈ C : 𝑎 ∈ 𝑠, 𝑏 ∈ 𝑠
In the social network, we can add edges between 𝑎 and 𝑏 if they are co-occurent, which means
(𝑎, 𝑏) ∈ 𝐸 ⇔ 𝑐(𝑎, 𝑏) = 0. Co-occurrence is a widely studied, yet not unproblematic, approach
for text analysis, see [24] and [25].
The evaluation will be carried out using two approaches: (1) Which gaps can be identified
when comparing the results of co-occurrence with a manually curated network as ground truth?
(2) Which gaps can be identified with a detailed perspective on particular actors?
4. Analysis
4.1. Social Network Comparison
First, we will analyze the quality of the co-occurrence based approach with a manually curated
network of Acts. We will consider the the 𝐹1 -score which is as a weighted average of the
precision and the recall:
Precision · Recall
𝐹1 = 2 · , where
Precision + Recall
𝑇𝑃𝑅
Precision = , and
𝐴𝑃 𝑅
𝑇𝑃𝑅
𝑅𝑒𝑐𝑎𝑙𝑙 = .
𝐴𝑃 𝑆
Damascus
Puteoli Church Malta
Damascus_Church
Puteoli
Athens Church
Damaris
Perga Tarsus Dionysius
Antioch of Pisidia Athens Ananias Thyatira
Attalia
Antioch of Pisidia Church Lystra_Church
Ikonien_Church
Seleucia
Bar-Jesus Derbe_Church
Salamis Paphos Simeon
Mary - 93.253(6) Manahen Samothracia
Apollonia
Pamphylia Sergius Paulus Lucius
Philippi Lydia
Derbe Church Neapolis
Cyprus_Church Galatia
John Mark Jason 93.163(1)
Rhoda
Barnabas Antioch Phrygia
Thessalonica Church
Thessalonica
Cyprus
Amphipolis
Judas 93.173(5) Paul Silas
Mysia
Antioch_Church Lystra
Andrew
Judas Iscariot Thomas Publius
Derbe
Matthias Simon the Zealot Titius Justus
Simon Matthew Bartholomew Iconium
Josef Barsabbas James 93.158(3) James 93.158(1) Crispus
Philip 93.379(1)
Joppa_Church John Judas Rome
Nicanor
Joppa Simon Peter Nicolaus James 93.158(2) Corinth Church
Women Prochorus
Timon Jerusalem Corinth Cenchreae
Parmenas Agabus
Tabitha Stephen Priscilla
Mary - 93.253(1) Timotheus
Mnason Achaia
Philip 93.379(3) Troas
Aeneas Ephesus Aquila
Lydda_Church Patara
Caesarea Aristarchus Ephesus Church
Tyre Church
Lydda Tychicus Miletus Berea
Samaria_Church Secundus Tyre Apollos
Sopater
Ptolemais Church
Trophimus Ptolemais
Gaius 93.83 (2) Pontus
Cornelius
Samaria Alexandria
Ashdod
Ethiopian eunuch
Ethiopia
Figure 2: Illustration of the manually curated network representation of Acts. The edge color refers to
the value 𝑐(𝑎, 𝑏) of edges between actors 𝑎 and 𝑏 by co-occurrence. Blue refers to a value of zero, green
to low and red to the highest values.
Here, TPR refers to true positive results, APR to all positive results, and APS are all samples that
should have been identified as positive.
In Table 1 we present the results for both the Gospel of Luke and Acts, in Figures 2 and 3 a
visual presentation of the values for the edges in the network. For Acts, we found in average
1.49 evidences for every edge. However, for 286 edges, we could not find any evidence in
the text with this method. Some of these values suggest problems: For example, we see 30
co-occurrences of Paul and John – and Paul and John Mark. Both are annotated with the same
name ‘John’. In addition, several actors are called James. We will continue a discussion about
these well-known issues of disambiguation in the next subsection.
Thus, the true positive rate is 360 and the recall 0.56. However, to calculate the precision, we
should also think about those 7229 edges which have not been added to the network. For this
purpose, we build the complement graph and compute the co-occurrences of both actors. 𝐺 is
called the complement of 𝐺 with 𝑉 (𝐺) = 𝑉 (𝐺) and
𝑒 ∈ 𝐸(𝐺) ⇔ 𝑒 ̸∈ 𝐸(𝐺).
Here, we found in average 0.16 evidences for every missing edge. However, for 6648 edges we
could not find any evidence. The precision of this approach is 0.62, and the 𝐹1 -score is 0.58 and
the result is similar for the Gospel of Luke, see Table 1.
However, in the Gospel of Luke we found in average 3.51 evidences for every existing edge,
which highlights that the narrative shows a different structure. However, the scores underline
the question, if this method is applicable at all. Thus, we will discuss some results in more detail.
Figure 3: Illustration of the manually curated network representation of the Gospel of Luke. The edge
color refers to the value 𝑐(𝑎, 𝑏) of edges between actors 𝑎 and 𝑏 by co-occurrence. Blue refers to a value
of zero, green to low and red to the highest values.
4.2. Detailed Analysis
For a detailed analysis, we consider the actor Paul (strong:G3972). Acts states several times that
he is working closely with Barnabas (strong:G0921). Indeed, we find 14 co-occurrences of both
terms. However, we can identify the problem of disambiguation. In Acts 13:7 we find a different
Paul tagged with the same Greek form: “Sergius Paulus, a man of intelligence, who summoned
Barnabas and Saul”. Next, we can study the co-occurrences of Paul and Peter (strong:G4074) –
there are none, although scholars identify a connection between both. Thus, the second issue
can be identified by information which is only stated implicitly.
However, we might try to find connections identifying major groups of actors. Thus, we
might either consider disciples (strong:03101) or apostles (strong:00652), Peter should belong
to both. For the first group we find four, for the last group three co-occurrences. Again, these
terms are not clearly defined. For example, in Acts 19:1 we see that Paul finds disciples in
Ephesus which are not related to the disciples in Jerusalem. In Acts 14:14 Barnabas and Paul are
called apostles, and not the initial twelve. Thus, before continuing to solve disambiguation by
hierarchies of actors, we do not only need its data but also information on how these terms are
used.
Yet another unresolved problem is the naming of actors in scenes. A verse-based co-occurrence
will fail on actors mentioned at different occasions. Although promising results were described
for dramas, see [26], it is not clear if they generalize. In addition, for biblical texts we might rely
on the traditional pericopes, but again it is questionable if they precisely represent the stage
occurrence of actors.
To sum up, even the co-presence of actors is difficult to extract using co-occurrence, since
often we do not have detailed information about every particular actor in a narrative. Thus,
applying methods to compute the figure configuration or the narrative connection between
two actors remains challenging.
5. Discussion and Outlook
This paper has described and analyzed a first naive approach towards the automated generation
of social networks on narrative texts. It comprises the automated linking of actors and other
information, e.g. spatiality, from a previously defined list, and we analyzed how co-occurrence
can be used to generate these networks or how it might improve the linking of data in manual
exegetical work. In this article, we focused on biblical texts. Here, linguistic resources are
already available, and thus we did not consider NLP methods. Even though, the performance of
this naive approach turned out to be rather poor.
Our analysis of performance reveals some questions and also possible further improvement.
First, we need to consider a hierarchy or taxonomy of actors and groups to tackle some challenges
of implicit data. However, this will not solve all problems, since actors might be named in
different places, which leads to the problem of scene-detection.
Second, we need to investigate on name disambiguation. Finally, it is worth to consider the
results of this method within the framework of exegesis. Bourgeois et al. [25, 4] stated that
with co-occurrence “it is impossible to extract a meaningful information”. The results of this
work underline this. However, they might support scholars working on the text. Working
with historical and narrative texts brings several challenges. Thus, we might also consider the
improvement of tools to support the scholars and exegets with feedback of their data to novel
AI approaches. This might also help for disciplines where pre-curated texts are not available.
While our naive implementation is both working and generic, it is still very early work on an
issue which needs more attention. We hope that it will also highlight the importance of more
interdisciplinary research in this field.
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