Computational International Relations: What Can Programming, Coding and Internet Research Do for the Discipline?

Abstract

Computational Social Science emerged as a highly technical and popular discipline in the last few years, owing to the substantial advances in communication technology and daily production of vast quantities of personal data. As per capita data production significantly increased in the last decade, both in terms of its size, bytes, as well as its detail, heart rate monitors, Internet connected appliances, smartphones, social scientists ability to extract meaningful social, political and demographic information from digital data also increased. A vast methodological gap exists in computational international relations, or ComInt, which refers to the use of one or a combination of tools such as data mining, natural language processing, automated text analysis, web scraping, geospatial analysis and machine learning to provide larger and better organized data to test more advanced theories of IR. After providing an overview of the potentials of computational IR and how an IR scholar can establish technical proficiency in computer science, such as starting with Python, R, QGis, ArcGIS or Github, this paper will focus on some of the author's works in providing an idea for IR students on how to think about computational IR. The paper argues that computational methods transcend the methodological schism between qualitative and quantitative approaches and form a solid foundation for building truly multi method research design.

Full text

Computational International Relations
What Can Programming, Coding and Internet Research Do for the Discipline?
Author: H. Akin Unver, assistant professor of international relations, Kadir Has University, and a dual non-resident
research fellow at the Center for Technology and Global Affairs, Oxford University and the Alan Turing Institute in
London. akin.unver@khas.edu.tr
Abstract: Computational Social Science emerged as a highly technical and popular discipline in the last few years,
owing to the substantial advances in communication technology and daily production of vast quantities of personal
data. As per capita data production significantly increased in the last decade, both in terms of its size (bytes) as well as
its detail (heartrate monitors, internet-connected appliances, smartphones), social scientists’ ability to extract
meaningful social, political and demographic information from digital data also increased. A vast methodological gap
exists in ‘computational international relations’, which refers to the use of one or a combination of tools such as data
mining, natural language processing, automated text analysis, web scraping, geospatial analysis and machine learning
to provide larger and better organized data to test more advanced theories of IR. After providing an overview of the
potentials of computational IR and how an IR scholar can establish technical proficiency in computer science (such as
starting with Python, R, QGis, ArcGis or Github), this paper will focus on some of the author’s works in providing
an idea for IR students on how to think about computational IR. The paper argues that computational methods
transcend the methodological schism between qualitative and quantitative approaches and form a solid foundation in
building truly multi-method research design.
Keywords: methodology, computer science, digital research, Internet
Total Word Count: 14673

Introduction –
What is Computational IR (
ComInt
)?
Computational International Relations (ComInt1),
introduced as a specific inquiry of research in this
paper, derives from the Computational Social Science
(ComSoc2) revolution of the last decade. International
relations (IR) literature has long trailed behind political
science (PolSci) since the seminal Designing Social
Inquiry3 (DSI) by King, Keohane and Verba. Setting
the quantitative bounds of the discipline despite its
evolution over the years, DSI has established the
methodological orthodoxy of both IR and PolSci,
becoming the key text in almost all methods classes.
The showdown of critical and supportive camps over
DSI has continued well into today, setting the
parameters of methodological polarization. The strong
empiricism of regression and statistical modelling was
challenged by the qualitative camp for a variety of
reasons, including distortion of analytical focus4,
manipulation of data5, and overall skepticism over how
much mathematical validity can imply causality6. This
long and seemingly unending core debate on
methodology in IR and PolSci has become eclipsed by
the advent of computational social science as a meta-
bridge between extreme ends of hard sciences and
social sciences.
There is no one single gateway to computational social
science. It is rather a meeting point between diverse
disciplines that seek to strengthen their analytical
1 I will be abbreviating Computational IR as ComInt, as CIR
is an over-crowded abbreviation in international relations,
used in reference to Coordinator for International Relations,
Central Intelligence Report, Critical Information
Requirements, CIR Capital Investments Review, among
many others.
2 I am strongly in favour of abbreviating Computational
Social Science as ComSoc, because CSS is already over-
crowded with several computer science-related
terminologies (including ‘computer science’), including
Cascading Style Sheets, Content Scrambling System,
Central/Computing Support Services, Core System
Software, Client Security Software and so on…
3 Gary King, Robert O. Keohane, and Sidney Verba,
Designing Social Inquiry: Scientific Inference in Qualitative Research
(Princeton, N.J: Princeton University Press, 1994).
4 Christopher H. Achen, “Let’s Put Garbage-Can
Regressions and Garbage-Can Probits Where They Belong,”
Conflict Management and Peace Science 22, no. 4 (September 1,
2005): 327–39,
https://doi.org/10.1080/07388940500339167; David
Collier and Henry E. Brady, Rethinking Social Inquiry: Diverse
Tools, Shared Standards (Lanham, Md: Rowman & Littlefield
Publishers, 2004); Philip A. Schrodt, “Beyond the Linear
Frequentist Orthodoxy,” Political Analysis 14, no. 3 (July
2006): 335–39, https://doi.org/10.1093/pan/mpj013.
5 Frank P. Harvey, “Practicing Coercion: Revisiting
Successes and Failures Using Boolean Logic and
Comparative Methods,” The Journal of Conflict Resolution 43,
no. 6 (1999): 840–71; Peter A. Hall, “Aligning Ontology and
approach through the use of a wide array of computer
and data science tools. Although the term used to
define computer (or other hard) scientists using big
data processing methods to explain social
phenomena7, this frame is currently expanding.
Increasingly more social scientists are getting trained
in the ways of data science and Internet research,
harvesting new forms of data to expand some of the
fundamental assertions of their literature. Training a
dedicated ‘computational social scientist’ is a
complicated and broad task, with many definitional
and operational questions. For example, how can a
student with no programming or computer
background start learning computational tools? Which
exact programming languages should a student
master? Is it enough to learn coding? How much?
Once coding yields promising data, should you map it,
or run a cluster network analysis? Should you learn
Python or R? Is it better to specialize in geospatial
research, sentiment analysis, neural networks or data
mining? The difficulty of answering these questions,
beyond the fact that such answers are highly
subjective, lies within the rapidly transforming
technical environment of computer science.
A Brief History of Computational Social Science (ComSoc)
It is hard to build an accurate trajectory of ComSoc.
Different social sciences disciplines have adopted,
dropped, marginalized and re-adopted computer-
based tools at different points since 1980s. Earliest
forms of Dynamic Systems Theory8 and Artificial
Methodology in Comparative Research,” in Comparative
Historical Analysis in the Social Sciences, ed. James Mahoney and
Dietrich Rueschemeyer, Cambridge Studies in Comparative
Politics (Cambridge: Cambridge University Press, 2003),
373–404
6 John Gerring, Social Science Methodology: A Criterial Framework
(Cambridge; New York: Cambridge University Press, 2012);
Judea Pearl, Causality: Models, Reasoning and Inference, 2nd
edition (Cambridge, U.K. ; New York: Cambridge University
Press, 2009).
7 Steven Bankes, Robert Lempert, and Steven Popper,
“Making Computational Social Science Effective:
Epistemology, Methodology, and Technology,” Social Science
Computer Review 20, no. 4 (November 1, 2002): 377–88,
https://doi.org/10.1177/089443902237317; Cristiano
Castelfranchi, “The Theory of Social Functions: Challenges
for Computational Social Science and Multi-Agent
Learning,” Cognitive Systems Research 2, no. 1 (April 1, 2001):
5–38, https://doi.org/10.1016/S1389-0417(01)00013-4;
Flaminio Squazzoni, “A (Computational) Social Science
Perspective on Societal Transitions,” Computational and
Mathematical Organization Theory 14, no. 4 (December 1, 2008):
266–82, https://doi.org/10.1007/s10588-008-9038-y.
8 Walter C. Hurty, “Dynamic Analysis of Structural Systems
Using Component Modes,” AIAA Journal 3, no. 4 (1965):
678–85, https://doi.org/10.2514/3.2947; Erich Jantsch,
“From Forecasting and Planning to Policy Sciences,” Policy
Sciences 1, no. 1 (March 1, 1970): 31–47,
https://doi.org/10.1007/BF00145191; J Brian McLoughlin
and Judith N Webster, “Cybernetic and General-System
Approaches to Urban and Regional Research: A Review of
the Literature,” Environment and Planning A 2, no. 4

Intelligence9 debates of the 1950-60s have led to the
emergence of Complexity Science10 and the
popularization of Agent-Based Modelling11 in
sociology and behavioral economics. As computers
became more powerful and widely available in the
1980s, first forms of Data Mining12, Genetic
Algorithms13 and System Dynamics Models14 emerged
in social research. Through the 1990s, earlier
adoptions of Internet data on social research began to
emerge, creating multi-layered connections into
complexity research, network science and urban
systems modelling. At certain times, these attempts
merged into the existing quantitative strand in social
sciences, where in others, computational progress
embarked on its own journey, steering clear of
mainstream statistical and mathematical methods. By
2000s, computer models of large sets of quantified
data were already being used in cognition, decision-
making, behavioral approaches, groups and
organization, social interactions and systemic analysis
of world events. Conte et. al.15 identify three main
schools of development in ComSoc: deductive (macro
theory-building through mathematical modelling and
computer processing), generative (micro theory-
building through behavioral modelling and computer
(December 1, 1970): 369–408,
https://doi.org/10.1068/a020369.
9 M. Minsky, “Steps toward Artificial Intelligence,” Proceedings
of the IRE 49, no. 1 (January 1961): 8–30,
https://doi.org/10.1109/JRPROC.1961.287775; J. R.
Carbonell, “AI in CAI: An Artificial-Intelligence Approach
to Computer-Assisted Instruction,” IEEE Transactions on
Man-Machine Systems 11, no. 4 (December 1970): 190–202,
https://doi.org/10.1109/TMMS.1970.299942; Bonnie
Lynn Webber and Nils J. Nilsson, Readings in Artificial
Intelligence (Morgan Kaufmann, 2014).
10 Kenneth E. Boulding, “General Systems Theory—The
Skeleton of Science,” Management Science 2, no. 3 (April 1,
1956): 197–208, https://doi.org/10.1287/mnsc.2.3.197; A.
K. Zvonkin and L. A. Levin, “The Complexity of Finite
Objects and the Development of the Concepts of
Information and Randomness by Means of the Theory of
Algorithms,” Russian Mathematical Surveys 25, no. 6 (1970): 83,
https://doi.org/10.1070/RM1970v025n06ABEH001269;
Peter Caws, “Science, Computers, and the Complexity of
Nature,” Philosophy of Science 30, no. 2 (April 1, 1963): 158–64,
https://doi.org/10.1086/287926.
11 David L. Banks and Nicholas Hengartner, “Social
Networks,” in Encyclopedia of Quantitative Risk Analysis and
Assessment (John Wiley & Sons, Ltd, 2008),
https://doi.org/10.1002/9780470061596.risk0667; Thomas
J. Fararo, “Mathematical Sociology,” in The Blackwell
Encyclopedia of Sociology (John Wiley & Sons, Ltd, 2007),
https://doi.org/10.1002/9781405165518.wbeosm052.pub2
; Roger D. Evered, “A Typology of Explicative Models,”
Technological Forecasting and Social Change 9, no. 3 (January 1,
1976): 259–77, https://doi.org/10.1016/0040-
1625(76)90011-1.
12 John V. Seidel and Jack A. Clark, “THE
ETHNOGRAPH: A Computer Program for the Analysis of
Qualitative Data,” Qualitative Sociology 7, no. 1–2 (March 1,
1984): 110–25, https://doi.org/10.1007/BF00987111;
David Myers, “‘Anonymity Is Part of the Magic’: Individual
Manipulation of Computer-Mediated Communication
Contexts,” Qualitative Sociology 10, no. 3 (September 1, 1987):
simulations) and complexity science (use of non-static
large and live datasets to explain and forecast behavior
and choice-based uncertainty) variants.
With the emergence of digital platforms and social
media, and global proliferation of smartphones,
ComSoc departed from its previous focus and began
harvesting this new, abundant and highly granular type
of digital data. Current definitions of ComSoc
therefore distinguish between computer-based social
science16, which is using computer programs to
process quantitative social data and ComSoc, which
processes enormous chunks of - often real-time -
Internet data17. Although the quantity and granularity
of digital data produced every day is impressive, a key
question remains how to process such data in a
meaningful way and how to build social theory using
it. As of July 2016, Instagram, Twitter, Facebook and
other social media platforms combined, produced
around 650 million publicly available posts per day18,
making up ‘the largest increase in the expressive capacity of
humanity in the history of the world’19. With the emergence
of increasingly more powerful computers, along with
most creative data processing software, all scientific
disciplines gained access to historically unprecedented
251–66, https://doi.org/10.1007/BF00988989; Ronald L
Breiger, Scott A Boorman, and Phipps Arabie, “An
Algorithm for Clustering Relational Data with Applications
to Social Network Analysis and Comparison with
Multidimensional Scaling,” Journal of Mathematical Psychology
12, no. 3 (August 1, 1975): 328–83,
https://doi.org/10.1016/0022-2496(75)90028-0.
13 D A Snow and and R. Machalek, “The Sociology of
Conversion,” Annual Review of Sociology 10, no. 1 (1984): 167–
90, https://doi.org/10.1146/annurev.so.10.080184.001123;
H. M. Collins, “The Seven Sexes: A Study in the Sociology
of a Phenomenon, or the Replication of
Experiments in Physics,” Sociology 9, no. 2 (May 1, 1975):
205–24, https://doi.org/10.1177/003803857500900202.
14 TOM R. BURNS, “The Sociology of Complex Systems:
An Overview of Actor-System-Dynamics Theory,” World
Futures 62, no. 6 (September 1, 2006): 411–40,
https://doi.org/10.1080/02604020600798619; Reinier de
Man, “The Use of Forecasts in Energy Policy: An
Application of Rule Systems Theory to the Comparative
Analysis of Public Policy Processes,” Systems Practice 2, no. 2
(June 1, 1989): 213–38,
https://doi.org/10.1007/BF01059500.
15 R. Conte et al., “Manifesto of Computational Social
Science,” The European Physical Journal Special Topics 214, no. 1
(November 1, 2012): 325–46,
https://doi.org/10.1140/epjst/e2012-01697-8.
16 Norman P. Hummon and Patrick Doreian,
“Computational Methods for Social Network Analysis,”
Social Networks 12, no. 4 (December 1, 1990): 273–88,
https://doi.org/10.1016/0378-8733(90)90011-W.
17 Joop J. Hox, “Computational Social Science Methodology,
Anyone?,” Methodology 13, no. Supplement 1 (June 1, 2017):
3–12, https://doi.org/10.1027/1614-2241/a000127.
18 Derek Ruths and Jürgen Pfeffer, “Social Media for Large
Studies of Behavior,” Science 346, no. 6213 (November 28,
2014): 1063–64,
https://doi.org/10.1126/science.346.6213.1063.
19 R. Michael Alvarez, Computational Social Science (Cambridge
University Press, 2016): viii.

and unfathomably detailed information on micro and
macro-level human interactions.
Computational IR (hereafter, ComInt) derives largely
from the founding and advent of ComSoc. in the last
few years. Related to, but separate from ComSoc,
ComInt deals exclusively with core IR topics of power,
conflict/peace, state behavior, international
norms/institutions and the world system/order. As
ComInt starts dealing with non-state actors (NGOs,
MNCs, media, religious groups, Diasporas, militants
etc.) it steers further into the domain of sociology and
shares common ground with digital, or tech
sociologists. This domain requires even further novel
methods, as tracing the transient shifts and trends of
non-state actors require a way to bring ethnography
close to the field of computational methods that both
include, but also expand upon the existing approaches
of digital and/or Internet ethnography.
Both data scientists and natural sciences scholars I got
the luck of working with at Oxford Internet Institute,
Oxford Computer Science Department and the Alan
Turing Institute had a distinct interest in the realist
strand of IR. They had an automatic tendency to
accept states as singular and primary units of analysis
in their approaches and without exception, all of them
wanted to address questions related to survival,
conflict and security - all from a state-centric point of
view. Defense, balance of power, armed conflict and
resource-infrastructure (capability) oriented research
agendas have attracted significantly more
computational research attention than other promising
approaches in IR such as constructivism, post-
structuralism, critical or post-modern theories. This is
a shame, as I will demonstrate later on, data mining,
entity recognition or geo-statistical mapping methods
can successfully challenge a number of these
approaches.
Defined in simple terms ComInt, relies on the mining
and processing of vast quantities of digital social
footprint to study, model and explain world events. In
doing that, it transcends the traditional schism
between qualitative and quantitative methodology and
presents a ‘third way’ methodology that frees the
researcher from the restrictions of both
methodological schools. ComInt predominantly (but
not exclusively) uses large chunks of digital footprint
and focuses on social online activities that generate
enormous quantities of social data. This is one of the
reasons why ComInt or ComSoc didn’t exist a decade
ago, and also a reason why merely using numerical
analysis software like R, Python and MatLab to model
20 Gustav Herdan, “Quantitative Linguistics or Generative
Grammar?,” Linguistics 2, no. 4 (2009): 56–65,
https://doi.org/10.1515/ling.1964.2.4.56.
21 Kenneth W. Church and Robert L. Mercer, “Introduction
to the Special Issue on Computational Linguistics Using
Large Corpora,” Computational Linguistics 19, no. 1 (March
1993): 1–24.
existing quantitative data, isn’t really ComSoc or
ComInt. The origin, size and type of data that is
collected make the main difference, as well as the main
focus of study; the Internet and digital
interdependencies.
Charting the Waters: Main Questions of ComInt
ComInt is an emerging field with yet unclear analytical
borders. How to study, research and teach it remains a
developing endeavour. The purpose of this section is
to set five main signal posts across a vast scientific
territory, helping newcomers to identify and scale out
the field. These five signal posts constitute five
different approaches to digital data processing, along
with their theoretical and research design point of
view: a) language/text, b) mapping, c) modelling, d)
communication and e) networks.
i. Language and Text
Although linguistics has so far predominantly been
used by the qualitative part of social sciences and IR,
computational tools introduced new approaches to the
quantitative study of large amounts of text.
Quantitative linguistics20 existed as a vibrant sub-
discipline as far back in the 1960s, but the advent of
computational linguistics21 as relevant to IR is a
relatively new phenomenon, since it renders
historically unprecedented volumes qualitative
information usable by text processing programs.
Traditionally, IR’s relationship with linguistics has
largely been driven through critical discourse analysis,
where qualitative data was interpreted as
‘unmeasurable’22, containing qualities like emotion,
sentiment and judgement that couldn’t conceivably be
analyzed through numeration. Digitization of text and
the advent of speech recognition technologies have
enabled large chunks of text to be searchable. Then
came the process by which vast quantities of
parliamentary archives, historical documents and
official statements became digitized, bringing text
analysis into the domain of computation. Today,
thanks to the Internet and social media, more than 7
million web pages23 of text are being added to our
collective repository of text, searchable, quantifiable
and measureable.
Text mining tools such as WordStat, RapidMiner,
KHCoder aim to dig into vast quantities of written
resources and even real-time transcribed speech
through specialized computer software. They differ
fundamentally from online text searching tools such as
22 Richard E. Palmer, “Postmodernity and Hermeneutics,”
Boundary 2 5, no. 2 (1977): 363–94,
https://doi.org/10.2307/302200; Paul Hernadi, “Dual
Perspective: Free Indirect Discourse and Related
Techniques,” Comparative Literature 24, no. 1 (1972): 32–43,
https://doi.org/10.2307/1769380.
23 http://www.internetlivestats.com/

Google or Bing by allowing the analyst to establish
connections, detect patterns and build networks
between very large text datasets, rather than merely
searching within them. Although these tools and
methods are being advanced and updated at great pace,
some analytical elements remain recurrent across most
studies.
• Information Retrieval is perhaps the oldest of
text mining tools and one that is the easiest
to replicate by simple programming.
Information retrieval identifies the
documents in a text dataset to match a
specific search term. Google, Yahoo or Bing
search engines are the best-known
information retrieval systems, and almost all
libraries use a version of these systems.
• Natural Language Processing on the other hand
bring text mining into the domain of artificial
intelligence: how can computers understand
a diverse set of human language in a way that
humans communicate with each other? In
other words, how can a computer
automatically identify verbs, nouns,
emotions, threats and sarcasm in a new
language it is introduced to? How should an
algorithm recognize ‘Donald Trump is a
great President’ when the statement is used
as sarcasm, as part of a critical tweet, for
example? Natural Language Processing is
crucial to ‘teach’ a computer how to code and
interpret different, hidden meanings in
language, as well as culturally-contingent,
unique expressions. This process is usually
the first step in building a corpus (body of
textual digital knowledge) to build
information extraction and data mining
systems.
• Information Extraction is the process by which
unstructured textual data is reorganized into
a structured form based on the corpus
obtained by Natural Language Processing. It
is further split into three main approaches
• Term Analysis, which extracts
different versions and references to
the same term in a range
documents, especially when these
documents include a mixture of
official, unofficial, translated and
native-language versions.
• Named-Entity Recognition, which
extracts people, organizations or
24 Adam Bermingham et al., “Combining Social Network
Analysis and Sentiment Analysis to Explore the Potential for
Online Radicalisation,” in Proceedings of the 2009 International
Conference on Advances in Social Network Analysis and Mining,
ASONAM ’09 (Washington, DC, USA: IEEE Computer
Society, 2009), 231–236,
https://doi.org/10.1109/ASONAM.2009.31.
25 H. Chen, “Sentiment and Affect Analysis of Dark Web
Forums: Measuring Radicalization on the Internet,” in 2008
groups, in addition to different
expressions of numbers
(percentages, time and location)
• Fact Extraction, which identifies
and extracts relationships, networks
and subtle connections in a
document, such as between entities,
events, dates and geographic
designations.
The resultant processes enable the researcher to
discover previously unidentified and unestablished
knowledge from text and especially by studying large
bodies of text in relation to each other. For example,
by deep diving into American, British, Russian and
French official documents about the 1945 Yalta
Conference might give us comparative information
over how all four sides understood and interpreted the
terms of the conference, allowing us to generate new
knowledge in diplomatic history over how these
countries structured their foreign policies through
early Cold War.
Some of the most promising employments of
computational language and text analysis on
international relations involve sentiment analysis,
detection of certain types of behavior (such as
radicalization) through text, opinion mining and
violent event detection/prediction. In one of the most
relevant cases for IR, Bermingham et al.24 demonstrate
how harvesting word and sentiment combinations in
Youtube’s comment section of jihadi videos can offer
a predictive model of radicalization. Delving beyond
the scope of the Internet, Hsinchun Chen25 for
example, has discovered an automated sentiment
mining model the Dark Web text data, presenting a
methodological avenue for the detection of potential
radicalization online. Dubvey et. al. took out
automated sentiment mining methods beyond
radicalization/terrorism research and harvested digital
media posts of Indian diplomats. In doing so the
researchers presented a promising model on how
foreign service members interact with politics in online
space. Finally, a personal favorite of mine, Hannes
Mueller and Christopher Rauh have recently published
an excellent paper26, which successfully predicts
political violence by harvesting automated newspaper
text. The authors argue that it is possible to predict
armed conflict and political violence in a specific
country by analyzing within-country variation of topics
in national newspapers. Finally, one of the earliest
forms of IR-relevant text mining studies in Turkey has
IEEE International Conference on Intelligence and Security
Informatics, 2008, 104–9,
https://doi.org/10.1109/ISI.2008.4565038.
26 Hannes Mueller and Christopher Rauh, “Reading Between
the Lines: Prediction of Political Violence Using Newspaper
Text,” American Political Science Review, December 2017, 1–18,
https://doi.org/10.1017/S0003055417000570.

been conducted by Hatipoglu et. al.27 on how digital
information and content diffusion on 2015 ‘Kobani
riots’ in Turkey influenced foreign policy perceptions
at the national scale.
Text mining is useful when it is used alongside another
method, such as discourse analysis, process tracing or
statistics. Furthermore, text mining research groups
perform better when they contain a subject expert
(historian, sociologist or ethnographer) and a linguist
(theorist) alongside programmer(s) and engineer(s), in
contrast to research clusters that contain only the latter
two. Usually the hardest and unfortunately the most
overlooked aspect of text mining is building a corpus
that is culturally, contextually and case-wise aware of
the nuances and subtleties of the language(s) that
is/are being studied. Furthermore, both the corpus
and search terms need to be grounded in theory, in
order to avoid concept stretching or build a corpus
with redundant or irrelevant terms.
ii. Mapping
Mapping and geospatial analysis contribute to some of
the most central components of IR, including
geopolitics/geography, borders and space. It is also
one of the most popular approaches to generating
event data, which allows researchers to display spatial
dynamics of war, conflict and inequality. The terms
GIS (Geographic Information Systems) and
‘geospatial’ are usually used interchangeably, with
often unclear differences that separate the two. Both
approaches refer to visual systems where geographic
information is stored in layers, that are then viewed,
manipulated and measured through a dedicated
mapping software. The essence of mapping research is
geographical coordinates and other geographical data
(altitude, topography, elevation, depth, etc.) that are
often coupled and analyzed in relation to tabular data
that contains various sets of statistical information
such as landmarks, infrastructure or econometric data.
Mapping has become exceptionally relevant and
important to the study of IR and other social sciences,
mainly because of the introduction of geolocation
information integrated into smart devices and social
media. Through the generation of large sets of social
data containing geographical information, researchers
are now able to study social and political phenomena
with much higher level of granularity, sometimes in
real-time. Instead of using geographical data to study
natural resources, transportation infrastructure or
household income, we are increasingly able to derive
behavioral information through micro expressions of
27 Emre Hatipoğlu et al., “Sosyal Medya ve Türk Dış
Politikası: Kobani Tweetleri Üzerinden Türk Dış Politikası
Algısı,” Uluslararası İlişkiler 13, no. 52 (2016): 175–89.
28 Hein E. Goemans and Kenneth A. Schultz, “The Politics
of Territorial Claims: A Geospatial Approach Applied to
Africa,” International Organization 71, no. 1 (January 2017): 31–
64, https://doi.org/10.1017/S0020818316000254.
digital activity. (Foursquare check-ins, Facebook status
updates, tweets containing geo-location information)
This yields vast quantities of new information related
to human behavior and relations for the use of
emergency response teams (emergency behavior of
large groups of people), local governments
(transportation and traffic behavior of individuals),
companies (purchasing power analysis, response to
advertisements, marketing analysis), among others.
Geospatial research is conducted both on dedicated
GIS application packages (such as ArcGis, QGis), or
mainstream data processing-programming platforms
that have GIS plug-ins (like Python and R). Even
Excel is experimenting with mapping plug-ins that can
be used with existing .xls or .csv files. Geospatial data
on the other hand, is divided into two categories:
vector and raster data. Vector data refers to points and
polygons that designate or enclose a specific
coordinate on a base map, such as coordinates of
schools in a geographical area, or allocated farmland in
a rural province. Raster data on the other hand refer to
aerial imagery and digital elevation models that render
a map three dimensional. While raster data is not really
necessary to analyze school districts, it is crucial for the
study of river flows or transportation systems. These
datasets are usually stored in dedicated geodatabases
that can be downloaded for study, or users can
generate their own datasets through manual entries, or
web scraping techniques. Increasingly, LiDAR (Light
Detection and Ranging), UAVs (unmanned aerial
vehicles), GPS (geographic positioning systems) and
satellites have begun to be used more frequently to
generate open-access geospatial analysis data.
Some of the best examples of geospatial IR excel not
only in finesse in visualizing location data, but
successfully tell a story that builds and tests a theory.
Hein E. Goemans and Kenneth A. Schultz for
example, demonstrated how states in Africa make
claims to some border areas and not others through
aggregating a digital geospatial dataset of border
disputes in a cross-continent study.28 The ingenuity of
the piece is that it discovers territorial contestation
taking root not from natural boundaries such as
watersheds or rivers, but mostly from historical-
colonial contestation points. Mark Graham et al. on
the other hand, have demonstrated how digital labor
affects global worker micro-economies, specifically in
terms of how type of online labor influences worker
bargaining power, economic inclusion and worker
livelihoods.29 By using a dataset showing geographic
engagement with digital labor, the researchers come up
with both micro-level behavior and macro-level
29 Mark Graham, Isis Hjorth, and Vili Lehdonvirta, “Digital
Labour and Development: Impacts of Global Digital Labour
Platforms and the Gig Economy on Worker Livelihoods,”
Transfer: European Review of Labour and Research 23, no. 2 (May
1, 2017): 135–62,
https://doi.org/10.1177/1024258916687250.

structures of what could be termed as ‘digital Marxist
research’. In one of the most famous examples of
geospatial IR dataset construction, Alberto Alesina et.
al. delve into the origins of ethnic inequality using
satellite-generated nighttime luminosity data.30 By
exploring time-frequency distribution of electricity, the
researchers come up with important tests of intra-state
ethnic inequality theories, including more macro-scale
developmental and economic disparities within and
across countries. One of the most interesting newer
studies on geospatial proximity networks has been
conducted by Jesse Hammond31, who demonstrated
that network of roads and connections between
population centers are the primary determinants of
conflict onset and diffusion in civil wars. This study
challenges previous findings on geography and conflict
by discovering a significant reporting bias in the
building of past conflict datasets.
iii. Modelling
Mathematical and physical modelling of social
phenomena aren’t new. Since 1960s, applying natural
sciences principles and functions on social events have
been amply used by researchers.32 The advent of
computational methods allowed computer science to
bridge this interdisciplinary gap between natural and
social sciences. In the last decade, three types of main
modelling approaches have grown in popularity:
mathematical, physics-based and bio-organistic
models. These approaches allow us to better study
mass social events like voting, riots, war and political
30 Alberto Alesina, Stelios Michalopoulos, and Elias
Papaioannou, “Ethnic Inequality,” Journal of Political Economy
124, no. 2 (March 4, 2016): 428–88,
https://doi.org/10.1086/685300.
31 Jesse Hammond, “Maps of Mayhem: Strategic Location
and Deadly Violence in Civil War,” Journal of Peace Research,
September 26, 2017, 22343317702956,
https://doi.org/10.1177/0022343317702956.
32 A. G. Wilson, “Modelling and Systems Analysis in Urban
Planning,” Nature 220, no. 5171 (December 1968): 963,
https://doi.org/10.1038/220963a0; Hugh Donald Forbes
and Edward R. Tufte, “A Note of Caution in Causal
Modelling,” American Political Science Review 62, no. 4
(December 1968): 1258–64,
https://doi.org/10.2307/1953917.
33 Steven Polgar, “Health and Human Behavior: Areas of
Interest Common to the Social and Medical Sciences,”
Current Anthropology 3, no. 2 (April 1, 1962): 159–205,
https://doi.org/10.1086/200266.
34 Duncan Black, “On Arrow’s Impossibility Theorem,” The
Journal of Law and Economics 12, no. 2 (October 1, 1969): 227–
48, https://doi.org/10.1086/466667.
35 L. S. Shapley and Martin Shubik, “A Method for
Evaluating the Distribution of Power in a Committee
System,” American Political Science Review 48, no. 3 (September
1954): 787–92, https://doi.org/10.2307/1951053.
36 David L. Wagner, Ronald T. Perkins, and Rein Taagepera,
“Complete Solution to Richardson’s Arms Race Equations,”
Journal of Peace Science 1, no. 2 (February 1, 1975): 159–72,
https://doi.org/10.1177/073889427500100206.
engagement at an unprecedented granularity. The
advent of computational methods significantly
increased the impact and relevance of all three
modelling approaches for social sciences and IR.
Mathematical modelling of social phenomena is
structured upon a somewhat problematic assumption
that human behavior can be observed within and
based on arbitrarily set constants and can be measured
in numerical terms. Although elements such as
uncertainty, chance and bias are added into models, the
foundational assumption that human behavior can be
quantified is still there.33 While this assumption is
subject to a separate set of epistemological debates,
mathematical models of human and social behavior are
nonetheless both popular and useful in testing
concepts such as equilibrium/non-equilibrium,
stability/instability or order/chaos that can assume
subjective meanings without proper measurement.
Mathematical models in social sciences are structured
upon a number of sub-approaches such as
voting/preference (Arrow’s impossibility theorem34,
Shapley-Shubrik index35), dynamic models
(Richardson arms race model36, Lanchester combat
models37, predator/prey model38) and ecology (phase
space39, boxicity40) and stochaistic models (Markov
chains41, learning theory42, social power approach43).
Physics models are more complex and less intuitive for
social sciences, mainly because of lack of bridging
literature between physics and social sciences.
Although the number of approaches are growing by
the emergence of a new breed of interdisciplinary
researcher doing this bridging work, there are roughly
37 N. J. MacKay, “Lanchester Combat Models,”
arXiv:math/0606300, June 13, 2006,
http://arxiv.org/abs/math/0606300.
38 Michael E. Gilpin, “Spiral Chaos in a Predator-Prey
Model,” The American Naturalist 113, no. 2 (February 1, 1979):
306–8, https://doi.org/10.1086/283389.
39 Brian Walker et al., “Resilience Management in Social-
Ecological Systems: A Working Hypothesis for a
Participatory Approach,” Conservation Ecology 6, no. 1 (June
19, 2002), https://doi.org/10.5751/ES-00356-060114.
40 Margaret B. Cozzens and Fred S. Roberts, “Computing the
Boxicity of a Graph by Covering Its Complement by
Cointerval Graphs,” Discrete Applied Mathematics 6, no. 3
(September 1, 1983): 217–28,
https://doi.org/10.1016/0166-218X(83)90077-X.
41 Arthur Spirling, “‘Turning Points’ in the Iraq Conflict,”
The American Statistician 61, no. 4 (November 1, 2007): 315–
20, https://doi.org/10.1198/000313007X247076; Simon
Jackman, “Bayesian Analysis for Political Research,” Annual
Review of Political Science 7, no. 1 (2004): 483–505,
https://doi.org/10.1146/annurev.polisci.7.012003.104706.
42 Bruce A. Campbell, “Theory Building in Political
Socialization: Explorations of Political Trust and Social
Learning Theory,” American Politics Quarterly 7, no. 4
(October 1, 1979): 453–69,
https://doi.org/10.1177/1532673X7900700404.
43 Arnold S. Tannenbaum, “An Event-Structure Approach
to Social Power and to the Problem of Power
Comparability,” Behavioral Science 7, no. 3 (July 1, 1962): 315–
31, https://doi.org/10.1002/bs.3830070304.

two main types of physics modelling that can
meaningfully be adapted into social sciences. The first
of those, cellular automata44 for example, deals with
the interaction of particle systems in parallel and
sequential dimensions. Take for example the spread of
war and conflict between neighboring countries. Each
country i becomes ‘infected’ with war (Si = 1), if at least
one of its nearest neighbors is already witnessing
conflict. Computational tools handle this diffusion
mechanism well, predicting and modeling the
likelihood of, for example, infection (i) to be spread
across 24 different neighboring countries in time t,
coming up with a predictive model of how far and how
fast can the conflict spread into adjacent territories.
The second type of physics modelling derives from
temperature models (Boltzmann probability45). They
give us how energy and pressure are diffused across
different units and how systems enter into entropy or
recalibration based on the latent or free energy
travelling between the constituent elements of the
system. To clarify for our purposes, Boltzmann
probability would give us diplomatic pressure,
international bandwagoning or buck-passing behavior
within an alliance or regional system: if your allies sign
a diplomatic treaty, they can also influence you into
signing the same treaty, even though the said treaty
may not be in your country’s interests. Thus, let E be
the number of closest allies signing the treaty, minus
the number of allies that aren’t signing the treaty. The
probability for your country to switch then is given by
the energy different and equal to exp(−2E/T) (or 1 if E
< 0), generating the terms in which you can withstand
the pressure from treaty-signing allies and refrain from
signing the treaty that doesn’t serve your national
interests. Both cases can be adopted into ComInt
through testing theories on voting in international
institutions, alliance behavior, international financial
markets and interactions between security cultures.
Bio-organistic types of modelling also substantially
derive from mathematical and physics modelling.
However, one particular type of biology model
penetrated more than other types into the domain of
social sciences: epidemiology46. Epidemiological
modelling is a simplified version of describing
transmission of diseases through a pre-determined
network of agents. Epidemiological models allow
social scientists to make sense of collective action and
large-scale popular mobilization in the form of riots,
44 Andrzej Nowak and Maciej Lewenstein, “Modeling Social
Change with Cellular Automata,” in Modelling and Simulation
in the Social Sciences from the Philosophy of Science Point of View,
Theory and Decision Library (Springer, Dordrecht, 1996),
249–85, https://doi.org/10.1007/978-94-015-8686-3_14.
45 Bernd A. Berg and Thomas Neuhaus, “Multicanonical
Algorithms for First Order Phase Transitions,” Physics Letters
B 267, no. 2 (September 12, 1991): 249–53,
https://doi.org/10.1016/0370-2693(91)91256-U.
46 Stanley Wasserman, Advances in Social Network Analysis:
Research in the Social and Behavioral Sciences (SAGE, 1994).
47 Laurent Bonnasse-Gahot et al., “Epidemiological
Modeling of the 2005 French Riots: A Spreading Wave and
protests, migration and emergency social behavior,
such as disasters. Epidemiological models based on
mathematical formulations of how infectious diseases
spread, can offer to make sense of complex social
behavior that would otherwise be very hard to monitor
and measure. There have been different
methodological approaches to the study of complex
social behavior such as agent-based models, spatial
data studies and simple mathematical formulations.
What makes epidemiological models different from
past methods is its conceptualization and modes of
measurement on disorder, uncertainty and
unpredictable complexity.
One of the most fascinating and novel studies on
social epidemiology has been Laurent Bonnasse-
Gahot et. al.47 seminal study on how 2005 French riots
spread and were contained. Building a riot contagion
model, the authors assess geographic proximity, social
networks and riot outcome in explaining how
neighborhood/district relations have been
instrumental in the diffusion of these riots. Such riot
and social movement modelling works are of direct
interest for IR scholars as they will substantially
strengthen some of the existing IR and PolSci theories
on how conflicts start, diffuse and end. A second key
study is Toby Davies et. al.48 account of how 2011
London riots and their policing have followed a direct
spatial contagion model, building a high-granularity
digital event dataset. The researchers test a number of
IR-relevant topics such as force deterrence, local
escalation models and crisis signaling, through
measuring police-to-riot distance, with the added
variables of police versus rioter numbers. Finally, both
Guo et. al.49 and Kirby and Ward50 make attempts to
generate a macro explanation of war and peace
through spatial modelling. Guo et. al. formulate
‘betweenness centrality’ (a physics principle) in order
to assert that cities that have the highest betweenness
factor (population density, ethnic fractionalization
versus the number of outside connections) are more
likely to contain conflicts in geographies in-between.
Kirby and Ward on the other hand reject nation states
as the primary actors in peace and war, and using a
digital dataset from Africa, they argue that it is the local
and tribal relations that determine the course and
extent of state-level violence.
the Role of Contagion,” arXiv:1701.07479 [Physics], January
25, 2017, http://arxiv.org/abs/1701.07479.
48 Toby P. Davies et al., “A Mathematical Model of the
London Riots and Their Policing,” Scientific Reports 3
(February 21, 2013): 1303,
https://doi.org/10.1038/srep01303.
49 Weisi Guo et al., “The Spatial Ecology of War and Peace,”
arXiv:1604.01693 [Physics], April 6, 2016,
http://arxiv.org/abs/1604.01693.
50 Andrew M. Kirby and Michael D. Ward, “The Spatial
Analysis of Peace and War,” Comparative Political Studies 20,
no. 3 (October 1, 1987): 293–313,
https://doi.org/10.1177/0010414087020003002.

iv. Communication
Digital technologies have brought forward another big
leap in communication, comparable to the effect of the
invention of writing, telegram and telephone. Thanks
to digital technologies, we communicate more
frequently, in verbal and non-verbal ways (such as
emojis, or ‘like’s) allowing us to engage with a
multitude of social, political and economic activities
simultaneously. The rise of social media too, has
allowed us to view and measure human
communication in interactive and forum-like settings,
leading to the testing of central IR communication
topics like misinformation, uncertainty, signaling and
cognitive bias.51 Furthermore, Internet and social
media have fundamentally changed how we seek
information, access the news and form our opinion on
political and social matters.52 An added factor is how
social media algorithms are acting as intermediaries in
our political searches, giving us non-random search
results based on a number of parameters.53 This means
that how people access and consume facts and
information online may be different than another,
leading to sustenance or exacerbation of polarization
in political views.54 The issue of how political
information is communicated online and represented
in digital news media has become a key debate in
political science and one that has significant
implications for IR. How do key foreign policy actors
and decision-makers use social media? How does the
Internet facilitate or impede information-seeking
behavior of citizens and politicians during an
international crisis? How does different consumption
patterns of digital news influence how citizens and
politicians view and understand diplomacy and in turn,
how does these patterns translate into actual foreign
policy?
51 Bruce Bimber, “Information and Political Engagement in
America: The Search for Effects of Information Technology
at the Individual Level,” Political Research Quarterly 54, no. 1
(March 1, 2001): 53–67,
https://doi.org/10.1177/106591290105400103.
52 A great overview of the primary debates in this field can
be found in: Andrew Chadwick and Philip N. Howard,
Routledge Handbook of Internet Politics (Taylor & Francis, 2010).
53 Pablo Barberá et al., “Tweeting From Left to Right: Is
Online Political Communication More Than an Echo
Chamber?,” Psychological Science 26, no. 10 (October 1, 2015):
1531–42, https://doi.org/10.1177/0956797615594620;
Helen Nissenbaum Lucas D. Introna, “Shaping the Web:
Why the Politics of Search Engines Matters,” The Information
Society 16, no. 3 (July 1, 2000): 169–85,
https://doi.org/10.1080/01972240050133634.
54 Markus Prior, “Media and Political Polarization,” Annual
Review of Political Science 16, no. 1 (2013): 101–27,
https://doi.org/10.1146/annurev-polisci-100711-135242.
55 Nic Newman et al., “Reuters Institute Digital News Report
2017,” SSRN Scholarly Paper (Rochester, NY: Social Science
Research Network, June 1, 2017),
https://papers.ssrn.com/abstract=3026082.
56 Samuel C. Woolley, “Automating Power: Social Bot
Interference in Global Politics,” First Monday 21, no. 4
(March 10, 2016), https://doi.org/10.5210/fm.v21i4.6161;
A newly emerging field of research in digital
communication is the advent of bots (automated
accounts) in digital space, fueling fake news and
misleading information that exacerbate international
crises and often lead to popular unrest. Fake news is
conceptualized as misleading, incomplete or out of
place information that is deliberately directed towards
consuming and distraction online attention.55
Although fake news can be driven by human accounts,
recent scholarly attention has focused on how
automated accounts (bots) help distribute such news
during crucial time frames, such as pre-election periods
or international crises.56 Bot research has thus
suddenly become a key topic in political science and
concerns IR directly, although the subject of inquiry
sits at the intersection of computer science and
communication theory.
From a methodological standpoint, Derek Ruths and
Jürgen Pfeffer have already demonstrated57 how social
media – although not always representative58 – can
offer better results compared to traditional polling.
This is both due to significant biases associated with
social media access and expression, but also the blurry
picture provided by bots. Kollanyi et. al. has
demonstrated59 how bots have influenced the results
of the US Presidential elections; a study that was
repeated in Forelle et. al. work60 on bots during
Venezuelan elections. Although this seems like a
political science question, external involvement and
disruption in national elections is definitely a problem
for international relations, explained in detail in Taylor
Owen’s book on how digital disruption is
contextualized in IR.61 An especially vibrant debate
currently revolves around Russian capabilities as a ‘bot
superpower’, able to disrupt and distract political
Samuel C. Woolley and Philip N. Howard, “Automation,
Algorithms, and Politics| Political Communication,
Computational Propaganda, and Autonomous Agents —
Introduction,” International Journal of Communication 10, no. 0
(October 12, 2016): 9.
57 Ruths and Pfeffer, “Social Media for Large Studies of
Behavior.”
58 Jonathan Mellon and Christopher Prosser, “Twitter and
Facebook Are Not Representative of the General
Population: Political Attitudes and Demographics of British
Social Media Users,” Research & Politics 4, no. 3 (July 1, 2017):
2053168017720008,
https://doi.org/10.1177/2053168017720008.
59 Bence Kollanyi, Philip N. Howard, and Samuel C.
Woolley, “Bots and Automation over Twitter during the
First U.S. Presidential Debate,” Data Memo (Oxford, UK:
Oxford Internet Institute, October 2016),
https://assets.documentcloud.org/documents/3144967/Tr
ump-Clinton-Bots-Data.pdf.
60 Michelle Forelle et al., “Political Bots and the Manipulation
of Public Opinion in Venezuela,” SSRN Scholarly Paper
(Rochester, NY: Social Science Research Network, July 25,
2015), https://papers.ssrn.com/abstract=2635800.
61 Taylor Owen, Disruptive Power: The Crisis of the State in the
Digital Age, 1 edition (Oxford ; New York: Oxford University
Press, 2015).

processes in Western countries.62 Further detailed
accounts of Chinese governmental controls on social
media and what it means for state-society relations
have been beautifully modelled in King et. al. 201363
and 201764.
A secondary strand of IR-related literature in digital
communication is the extent to which online
campaigning affects political processes and social
mobilization. Koc-Michalska et. al.65 has explained
how online campaigning affects political elections in
France, Germany, Poland and the UK, demonstrating
that resources, rather than innovation determines
success in digital campaigns. In a similarly pessimistic
study, Margetts et. al.66 ran an experiment, testing how
political information online affects decision-making of
individuals, finding that online information is
behavior-changing when it is shared by large groups of
people. If the information – whether true or false –
isn’t shared by a critical mass (or ‘social network
capital67’), it has little influence over political behavior,
the study finds. In an interesting twist, Yasseri and
Bright explore digital information seeking behavior
through Wikipedia traffic data, discovering that
political parties whose Wikipedia pages witness a surge
in visits close to elections, tend to do well in those
elections, compared to other candidates or parties that
haven’t enjoyed Wikipedia attention.68 This model can
be replicated in to study UN voting patterns or
elections within international organizations.
v. Networks
Digital network research is another field that is
growing in popularity and allows researchers to study
political and power relations in digital space. Often,
creative computational researchers discover digital
62 John Kelly et al., “Mapping Russian Twitter,” SSRN
Scholarly Paper (Rochester, NY: Social Science Research
Network, March 23, 2012),
https://papers.ssrn.com/abstract=2028158.
63 Gary King, Jennifer Pan, and Margaret E. Roberts, “How
Censorship in China Allows Government Criticism but
Silences Collective Expression,” American Political Science
Review 107, no. 2 (May 2013): 326–43,
https://doi.org/10.1017/S0003055413000014.
64 Gary King, Jennifer Pan, and Margaret E. Roberts, “How
the Chinese Government Fabricates Social Media Posts for
Strategic Distraction, Not Engaged Argument,” American
Political Science Review 111, no. 3 (August 2017): 484–501,
https://doi.org/10.1017/S0003055417000144.
65 Karolina Koc-Michalska et al., “The Normalization of
Online Campaigning in the web.2.0 Era,” European Journal of
Communication 31, no. 3 (June 1, 2016): 331–50,
https://doi.org/10.1177/0267323116647236.
66 Helen Margetts et al., “Social Information and Political
Participation on the Internet: An Experiment,” European
Political Science Review 3, no. 3 (November 2011): 321–44,
https://doi.org/10.1017/S1755773911000129.
67 Nicole B. Ellison, Charles Steinfield, and Cliff Lampe,
“The Benefits of Facebook ‘Friends:’ Social Capital and
College Students’ Use of Online Social Network Sites,”
Journal of Computer-Mediated Communication 12, no. 4 (July 1,
relations and influence maps that cannot be discovered
through research in physical space - either due to the
controversial nature of the topic, or the difficulty in
finding data. Extremism and radicalization networks
are the primary foci of computational network
analysis. Through digital relations, researchers are able
to find influencers, hierarchies and relations in digital
space. This could be employed to discover diplomatic
networks at the state and institutional level, as well as
networks of radicalization at the non-state and sub-
state actor level. Ideology research too, can benefit
greatly through computational methods, by the use of
entity extraction algorithms.
Classical network theory69 focuses on social networks
among individuals (friendships, advice-seeking..) and
formal contractual relationships (alliances, trade,
security community). What makes network theory
important to social science, politics and IR is its ability
to conceptualize and theorize relations at the micro,
meso and macro-levels of analysis in political
processes, offering a structure to seemingly complex
interactions.70 Accordingly, network theory stipulates
that relations and internal-external pressures on those
relations have the ability to affect beliefs and
behaviors.71 Instead of adopting IR’s mainstream
levels of analysis approach, network theory focuses on
the interactions between these levels of analyses,
aiming to conceptualize how these interactions lead to
policy and behavior.72 Computational network analysis
on the other hand, takes classical network theory to
vast levels of size and complexity, not only designating
relations between them, but also use artificial
intelligence, machine learning and neural networks
approaches to automatically generate real-time
changes in these relations.73
2007): 1143–68, https://doi.org/10.1111/j.1083-
6101.2007.00367.x.
68 Taha Yasseri and Jonathan Bright, “Wikipedia Traffic Data
and Electoral Prediction: Towards Theoretically Informed
Models,” EPJ Data Science 5, no. 1 (December 1, 2016): 22,
https://doi.org/10.1140/epjds/s13688-016-0083-3.
69 J. L. Moreno and H. H. Jennings, “Statistics of Social
Configurations,” Sociometry 1, no. 3/4 (1938): 342–74,
https://doi.org/10.2307/2785588; John Arundel Barnes,
Social Networks (Addison-Wesley Publishing Company,
1972).
70 Mark Granovetter, “The Strength of Weak Ties: A
Network Theory Revisited,” Sociological Theory 1 (1983): 201–
33, https://doi.org/10.2307/202051; Manuel Castells, The
Rise of the Network Society: The Information Age: Economy, Society,
and Culture (John Wiley & Sons, 2011).
71 Bruno Latour, Reassembling the Social: An Introduction to Actor-
Network-Theory (OUP Oxford, 2005).
72 Stephen P. Borgatti et al., “Network Analysis in the Social
Sciences,” Science 323, no. 5916 (February 13, 2009): 892–95,
https://doi.org/10.1126/science.1165821.
73 Ravindra K. Ahuja, Thomas L. Magnanti, and James B.
Orlin, Network Flows: Theory, Algorithms, and Applications, 1
edition (Englewood Cliffs, N.J: Pearson, 1993); Aaron
Clauset, M. E. J. Newman, and Cristopher Moore, “Finding
Community Structure in Very Large Networks,” Physical

One of the most relevant recent complex network
studies to IR is Jonathan Bright’s work on identifying
online extremist networks and the role of ideology in
polarized digital structures.74 This work is relevant to
IR, because it covers around 90 different political
parties across 23 countries, providing a much-needed
cross-national empirical evidence on the role echo
chambers play in concentrating and isolating extreme
views in a political communicative setting. Another
important work is Efe Sevin’s working paper on how
international actors and foreign policy practitioners
use digital media to expedite and re-negotiate existing
diplomatic processes.75 Building upon a Twitter-
scraped dataset of embassy and consulate connections
across the global, Sevin makes the case that middle
powers may have disproportionately more significant
weight in international diplomacy by seizing upon the
amplifying potential of social media. Finally, Caiani
and Wagemann demonstrate how the Italian and
German extreme far right connect in digital space,
exploring aspects of communicative radicalization and
network capital of extreme political ideologies.76 The
authors discover that extremist networks cluster and
connect differently across political cultures, with
separate layers of connectors, leaders and marginalized
sub-groups.
My Personal History with ComInt
I come from a qualitative background. Given
University of Essex being a stronghold of discourse
analysis, I developed keen interest in Foucauldian
approaches to power and politics. Through my
dissertation however, the amount of data I collected
on discursive construction of violence, terrorism and
conflict became so large that I was unable to deal with
them meaningfully through qualitative analysis alone.
When I bounced the idea of quantifying discourse with
my PhD supervisor, he momentarily panicked, as the
practice wasn’t as commonplace as it is nowadays. ‘You
will either get kicked out of the PhD program, or get an award’
was his reply. In the following months, I learned
statistics from scratch, engaging in successive crash
courses in regression analysis and mathematical
modelling offered at the university. These didn’t help,
as such courses were still taught for extremely large
classes with economy, management and political
science students with different quantitative skills all
pitted into the same class. I learned statistics and
regression analysis mostly through self-study (Youtube
didn’t exist back then).
Review E 70, no. 6 (December 6, 2004): 66111,
https://doi.org/10.1103/PhysRevE.70.066111.
74 Jonathan Bright, “Explaining the Emergence of Echo
Chambers on Social Media: The Role of Ideology and
Extremism,” arXiv:1609.05003 [Physics], September 16, 2016,
http://arxiv.org/abs/1609.05003.
My resultant dissertation combined a ten-year content
analysis of open floor debates in three parliaments,
coded and sorted according to sentiment, syntax and
lexicon, with another matrix of coding for politicians’
ideologies and political interests. Eventually, I
demonstrated that regardless of country and political
system, conservative and liberal politicians used the
same linguistic and sentiment characteristics to define
intra-state conflicts. A conservative politician in the
United States, Belgium and Turkey sounded
significantly more alike, compared to liberal politicians
in their own countries, and vice versa. And I could
reliably demonstrate the relationship from a statistical
point of view. This was good evidence that contributed
to the trans-nationalization of conflict behavior and
how crisis periods end up internationalizing certain
ideologies. Thankfully, my advisor’s second prediction
ended up happening. I wasn’t kicked out of the
program and won the Middle East Studies
Association’s Malcolm H. Kerr dissertation award.
Although the methodology isn’t new anymore at this
point, back when I submitted, quantifying and
measuring discourse numerically was considered as a
methodological heresy of sorts. This was effectively
merging positivist and post-positivist traditions and
like one of my examiners put: ‘like writing a Muslim
Bible’.
The second turning point in my multi-method odyssey
was in 2015. After writing my book, I was focusing on
the study of armed non-state actor behavior in
northern Syria and northern Iraq, both getting
increasingly more complex and frustrating to monitor.
As actors on the ground quickly exchanged territory,
merged, broke-away and disappeared on the
battlefield, generating new knowledge or testing
theories on conflict were all getting increasingly more
difficult. I began generating elementary maps for my
own study purposes using Google Earth layers and
basic image processing software like Paint. It was
around this time that I began to realize that the
sophistication of one’s maps aren’t as important as the
story those maps are telling. One of my completely
low-tech maps would later be solicited for publication
by the New York Times along an op-ed on armed
violence in northern Iraq. But getting battlefield
information was proving extremely difficult, as the
majority of conflict events were taking place across the
inaccessible parts of Syria and Iraq. Lucky for me - and
perhaps for all conflict scholars - that the advent of
mass social media, smartphone and digital propaganda
coincided with the war against ISIS. This allowed
conflict researchers to extract and process enormous
75 Efe Sevin, “Traditional Meets Digital: Diplomatic
Processes on Social Media” (International Studies
Association Annual Conference, Baltimore, MD, 2017).
76 Manuela Caiani and Claudius Wagemann, “Online
Networks of the Italian and German Extreme Right,”
Information, Communication & Society 12, no. 1 (February 1,
2009): 66–109,
https://doi.org/10.1080/13691180802158482.

volumes of digital content shared by the locals, citizen
journalists and the militants, who documented war
deep inside the fog of war. A young militant for
example, could share how their group struck a Syrian
Army tank and post it online with a video and
associated hashtag, intended as propaganda, but
ending up becoming a data node for conflict
researchers. I began scraping some of that content
through Twitter, Instagram (before they changed their
API) and Flickr. Perhaps the most interesting detail
about these battlefield posts were the selfies, that
constituted a large portion geotagged conflict events,
which practically exposed them on the battlefield. I
continued to scrape more of such tweets, eventually
coming up with a test run of around 17,352 geotagged
tweets through a two-year period, mapping them
through time-frequency diffusion. The resultant
combination of those images and videos gave me one
of the most detailed and high-granularity war map that
was out there at the time, rivaling the level of detail of
many state-produced war maps that exited at the time,
which I published with the Financial Times and
Journal of International Affairs.
I took my newfound methodological odyssey to
Oxford, where I went as a visiting fellow at the Oxford
Internet Institute (OII). OII was an interdisciplinary
Oxford department, dedicated solely to the study of
the Internet and digital data, along with its political,
economic, social and psychological effects on human
relations. It was made up of a very uncommon
combination of scholars, from physics, biology,
geography, computer science, mathematics and
political science, all trying to approach different
theoretical topics related to the Internet, through a
multitude of methodologies. It was there that I learned
how Gaussian particle physics principles could help
explain how people chose their mates on Tinder and
other online dating platforms, or how epidemiological
models in biology could explain how riots and protests
emerge and spread. It was there that I learned how to
code (thanks to computer science doctoral training
program for having me audit their Python classes),
conduct network measurement, build algorithms for
text mining, use more advanced mapping and network
analysis software to dig deeper into the logic of large
coding structures. I was then admitted to the Alan
Turing Institute in London, where I had a chance to
participate in data science research groups from
Cambridge, Warwick, UCL and Edinburgh, that
focused on urban analytics, extremism networks and
measuring human digital behavior.
The luxury of months of incubation and daily access
to some of the brightest and pioneering minds on
computational studies forced me to think about the
future of IR, its methodological debates and how
computational tools can be incorporated into the study
of world events. My first project was an expanded and
improved version of the earlier work on battlefield
data. Focusing solely on selfies, I scraped battlefield
digital data from geographically confined locations in
Syria, using a corpus of keywords in English, Farsi,
Kurdish and Arabic and generated an event dataset
that only contained armed incidents. In my second
project, I developed my earlier work on measuring
how pre-digital and digital forms of mobilization
influenced protest and resistance networks, by
focusing on Turkey’s failed coup on 15 July 2016.
Then, I took part in multiple research clusters on how
cultural and geographical proximity between cities
helped us measure the likelihood of conflict, how
dyadic and multi-level sentiment analysis of digital text
allow us to predict radicalization and terrorism
networks, and using machine learning algorithms to
visually detect and predict the likelihood of armed
conflict using Google OpenMap images.
The culmination of my dual visit to Oxford Internet
Institute and the Alan Turing Institute was my ‘Turing
Lecture’ in the latter one, where I made an
introduction and exposition of the term
‘Computational IR’, detailing how data science and
international relations can form a productive
partnership, in a way that doesn’t only benefit these
two disciplines, but also form as the basis of
collaboration with the full range of natural sciences
scholars and social scientists. Since - to the best of my
knowledge - there has been no previous use of the
term, I’d like to coin ‘Computational International
Relations’ as a way to establish a new methodological
field that hopefully will transcend the traditional
quantitative-qualitative schism in the field, as well as in
social sciences.
Method Training: Or ‘How to be a Computational IR Scholar’
Up until very recently, ComSoc training existed largely
within the confines of quantitative-leaning social
scientists taking data science courses, with the
exception of the Oxford Internet Institute, Harvard
Institute for Quantitative Social Science and Stanford
University’s Computational Social Science Program,
which are pioneering institutions of the field. Currently
there are a wide range of choices for social scientists
from summer courses to master’s degrees dedicated to
computational social science. To the best of my
knowledge, there are no ComInt programs; rather, IR
scholars currently can take ComSoc courses and create
their own sub-specialization. Oxford’s Center for
Technology and Global Affairs, where I’m currently a
research fellow, is also gearing up to fill in this vacuum
in the near future.
There are two aspects of ComInt training. The first
one is the easier part: what kind of a technical
foundation should students develop? Different
ComSoc programs provide different curricula for this
purpose, but there are common denominators. Data
visualization, model construction and estimation,
along with honing statistical skills is generally the first
step. Later, understanding different data types used in
computing, and various processing principles –
clustering, event-driven simulation, approximating

functions, derivatives and basic Monte Carlo
techniques - are required to build upon the initial
foundation. At this time, introductory knowledge of
Java, Shiny, Python, R and C++ should be introduced,
along with mainstream programs such as ArcGis or
QGis (for geospatial analysis), NVivo, RapidMiner or
QDA (for text analysis), Gephi, iGraph or NodeXL
(for network analysis), and Repast, Swarm, EpiModel
or MASON (for various modelling analyses). These
technical skills must be reinforced through
qualitative/historical theoretical courses on spatial
analysis, complexity research, logic of algorithms and
basic neuroscience (mostly for complexity research).
Final touches can be made through large dataset
maintenance skills through Entity-Relationship
Diagram (ERD), SQL (Structured Query Language),
data definition language (DDL) and data manipulation
language (DML).
The second aspect of ComInt/ComSoc training is the
harder part: understanding how much technical skill
you need to learn and sustain for your own research
career. Like any language, computer science requires
sustained daily use to remember and preserve
knowledge. To that end, being a ComInt/ComSoc
scholar means a) knowing you can’t master all
computer science tools, b) balancing between the main
task of social scientists (theory-building) and methods-
driven nature of computer science and c)
understanding which computational tools you need to
develop and which ones to outsource. My answer to
all three questions – at least for graduate students – is:
be promiscuous. Spend at least six months to dig deep
into the computer science world and immerse yourself
in methods-driven research. Build your R packages,
learn how to scrape Twitter data and spend some time
visualizing them on a multitude of spatial, network and
text-based software. Although most senior social
scientists will advise you to not forget the fact that you
are a social scientist, my advice is: forget it – at least
for a limited period of time. This period is critical to
learn how to think like a computer scientist; not just to
get a new perspective, but also to understand the basics
of computer-driven research. This is crucial, as
although computer science methods are constantly
evolving, basic principles of computers (automation,
the logic of repeating work, strings, data structures,
loops, variables, functions etc.) don’t change radically.
You can quickly adapt to new programming languages
77 Joshua A. Tucker et al., “From Liberation to Turmoil:
Social Media And Democracy,” Journal of Democracy 28, no. 4
(October 7, 2017): 46–59,
https://doi.org/10.1353/jod.2017.0064.
78 Zachary C. Steinert-Threlkeld, “Spontaneous Collective
Action: Peripheral Mobilization During the Arab Spring,”
American Political Science Review 111, no. 2 (May 2017): 379–
403, https://doi.org/10.1017/S0003055416000769.
79 Espen Geelmuyden Rød and Nils B. Weidmann,
“Empowering Activists or Autocrats? The Internet in
Authoritarian Regimes,” Journal of Peace Research 52, no. 3
(May 1, 2015): 338–51,
https://doi.org/10.1177/0022343314555782.
and platforms, once you know what a programming
language does. It is also only after spending several
months on coding that students can get a sense of what
computational tools can do for their own research
agenda and the kind of questions they seek to ask.
The final part of ComInt/ComSoc training is the
hardest part: forget everything. This phase is about
deliberately stopping computer science work and
return back to IR, PolSci or another social science
discipline of origin. My advice would be to re-read the
core theoretical readings of the field the student is
coming from and to rethink the fundamentals of the
field following several months of immersion in the
world of programming. Another twist to this
suggestion would be to return back to another social
science discipline, instead of the student’s own point
of origin. To give an example, an IR student going
through the computational curve should ideally go to
sociology and history to establish an introductory
foundation there, creating a triangular expertise.
Although not easy, mastery of IR and good
introductory knowledge of computer science, and
sociology or history will expand the student’s analytical
prowess significantly.
Case Studies from My Research Trajectory: How
Can Conflict Researchers Benefit from ComInt?
ComInt is hard to explain by demonstrating its
application on one single research question. One
significant line of research in ComInt focuses on the
relationship between social media and international or
comparative political processes under conflict. Some
of the most important works in this field are: Tucker
et. al. (2017) work on the relationship between social
media and democracy,77 Steinert-Threlkeld’s study on
the effects of Internet on social mobilization,78 Rød
and Weidmann’s work on comparative
authoritarianism and the Internet,79 Anita Gohdes’
study on how regimes hide their atrocities on the
Internet,80 (as well as her important overview of how
the use of Internet data has changed the study of
conflict81), Mitts’ work on ISIS radicalization on
Twitter,82 Little’s formal modelling work on how ICTs
80 Anita R. Gohdes, “Pulling the Plug: Network Disruptions
and Violence in Civil Conflict,” Journal of Peace Research 52,
no. 3 (May 1, 2015): 352–67,
https://doi.org/10.1177/0022343314551398.
81 Anita R. Gohdes, “Studying the Internet and Violent
Conflict,” Conflict Management and Peace Science, October 25,
2017, 738894217733878,
https://doi.org/10.1177/0738894217733878.
82 Tamar Mitts, “From Isolation to Radicalization: Anti-
Muslim Hostility and Support for ISIS in the West,” SSRN
Scholarly Paper (Rochester, NY: Social Science Research
Network, March 31, 2017),
https://papers.ssrn.com/abstract=2795660.

affect protest behavior,83 Zeitzoff’s review of how
social media is changing conflict84 (and his social
experiment of the 2012 Gaza conflict85) and
Gunitsky’s work on how autocracies use social media
as a form of regime stabilization tool.86 The list is far
from complete, however, as the discipline and its
exciting methods are rapidly evolving and improving.
Here, I’ll steer clear of engaging in yet another
literature review and instead, try to explain how
computational tools improved my own scholarship
across different topics in IR by adopting hybrid
methods. My first exposure to computational research
has been through the nudge of a group of recently
graduated Harvard computer science PhDs, who
wanted to tackle issues related to security and conflict.
IR students will receive similar calls from computer
scientists. If not, they should initiate contact
themselves, either through speaking to a computer
scientist faculty, or peers in the computer/data science
department. Such calls are usually the first step for any
social scientist to collaborate with computer scientists.
However, interdisciplinary research builds its
momentum slowly, can be frustrating and this
shouldn’t discourage new researchers from being
persistent and continuing to engage with research
partners. Although my research partners decided to set
up a startup and drifted away from our research
eventually, I learned the basics of web scraping, setting
up web crawlers and using API data from them. These
tools would ultimately be instrumental in my research
project on mapping militant selfies in Syria.
Conflict research in IR has developed a keen interest
in event data in recent years. From statistical to
geographic layers, event data enables us to track
conflict patterns, targeting choices and border
contestations across a single, or multiple conflict
settings. But the majority of that event data (such as
UCDP/PRIO or UMD) comes from ‘official’ sources,
derived largely from state-level resources, of
mainstream media companies that report on battlefield
developments. But what about the inaccessible parts
of a conflict? What if neither reporters, nor intelligence
operatives of state actors can access no-go zones in a
conflict and how do we get event data from there? Up
until 2012-13, a clear answer was hard to provide.
Thankfully for researchers, non-state actors’ use of
83 Andrew T. Little, “Communication Technology and
Protest,” The Journal of Politics 78, no. 1 (December 17, 2015):
152–66, https://doi.org/10.1086/683187.
84 Thomas Zeitzoff, “How Social Media Is Changing
Conflict,” Journal of Conflict Resolution 61, no. 9 (October 1,
2017): 1970–91,
https://doi.org/10.1177/0022002717721392.
85 Thomas Zeitzoff, “Does Social Media Influence Conflict?
Evidence from the 2012 Gaza Conflict,” Journal of Conflict
Resolution 62, no. 1 (January 1, 2018): 29–63,
https://doi.org/10.1177/0022002716650925.
86 Seva Gunitsky, “Corrupting the Cyber-Commons: Social
Media as a Tool of Autocratic Stability,” Perspectives on Politics
13, no. 1 (March 2015): 42–54,
digital technologies, smartphones and social media
have led to a strange setting where active combat and
insider developments in no-go zones are broadcast
digitally on a minute-by-minute basis with geotags.
Militants overwhelmingly began using social media to
publicize important events such as armed clashes,
declarations of loyalty, or reports of death. Most
groups in Syria such as ISIS, YPG or FSA have learned
to catalogue these events online with dedicated
hashtags and visuals for propaganda, making sure that
they are easily searchable. For the exact same reason,
they make excellent computational conflict data. In the
first phase of my research, I have scraped around
15,000 selfies from Syria, all belonging to Kurdish
groups YPG, SDF and their offshoots, through
January 2014 - June 2016. Building a word corpus
consisting of words related to armed events (bombing,
shooting, explosion, airstrike…) I’ve applied entity-
recognition algorithm to scrape all tweets containing
these keywords in pre-set coordinates isolating
northern Syria, and containing photos that were taken
with the front camera of a smartphone (back then, this
was the best way of scraping selfie data). I mapped out
the resultant dataset to infer where Kurdish groups
were fighting, where they were defending and which
battles were they avoiding.87 This became the
foundation of my article on Kurdish geopolitics later
on.88
While I was planning to expand the militant selfie
study to other groups in Syria and also bring in
Ukrainian groups, a failed coup attempt took place in
Turkey, in July 2016. I reorganized my work to focus
on the digital engagement patterns during the coup
attempt and started to scrape geotagged tweets that
clustered around six most widely shared hashtags.
These hashtags not only gave me which districts
mobilized the most against the coup attempt, but also
generated a valuable dataset to model later on through
physics or epidemiological approaches. Several things
stood out from the study: first, it was religious
networks (tariqas), rather than political party networks
of AKP that had initiated the first mobilization against
the coup. Although AKP networks later mobilized to
significantly increase the numbers in the streets, tariqa-
dominant districts have been deployed faster and at
greater volume during the earlier hours of the coup
attempt.89 This computational data is important
https://doi.org/10.1017/S1537592714003120.
87 H. Akın Unver, “Mapping Militant Selfies: Application of
Entity Recognition/Extraction Methods to Generate
Battlefield Data in Northern Syria” (Computer Science
Doctoral Training Program Seminar, Oxford University,
May 31, 2017),
https://www.cs.ox.ac.uk/seminars/1855.html.
88 H. Akin Unver, “Schrödinger’s Kurds: Transnational
Kurdish Geopolitics In The Age Of Shifting Borders,”
Journal of International Affairs 69, no. 2 (Spring/Summer 2016):
65–98.
89 H. Akin Unver and Hassan Alassaad, “How Turks
Mobilized Against the Coup,” Foreign Affairs, September 14,

because it gives us a good early measurement of digital
sociology: in an increasingly interconnected world,
what is the most foundational source of collective
action? During emergencies and times of uncertainty,
which fundamental social organizing forces manages
to generate the momentum enough to mobilize masses
into collective action? The case of Turkey’s failed coup
reveals to us that at least in Turkish socio-cultural case,
religious networks fill in this emergency role. I’m
currently at the point of expanding this study into how
different religious movements adapt to digital
technologies and generate collective action in the US,
Hungary, Serbia, Ukraine and Israel.
Simultaneously, I’ve been drawn further into the
concept of digital spoilers and distractors. Bot research
is a growing and popular area of study, yet we still
know so little about their role in international relations
and how they influence global crises. My conversations
with Phil Howard, the director of computational
propaganda project, reinforced my view that much of
the research on bots is dedicated to their impact in
politics and sociology, but not enough on IR. To that
end, I’ve begun collecting real-time data during
particular international crises to measure anomalies in
hashtags and fake news diffusion. My hypothesis,
based on raw personal observation during digital crises
was that bot-driven hashtags were more likely to
disproportionately increase during very short periods
(15-20 minutes) and end abruptly, without organic
sustain. In contrast, organically-driven hashtags,
usually increase more gradually and are sustained over
the course of several hours, sometimes days. The first
incident I could test this hypothesis was the Saudi
Arabia, Qatar and UAE diplomatic crisis, which began
in June 2017, as a response to statements attributed to
the Qatari Sheikh. A time-frequency geospatial analysis
of the most frequently shared hashtags show us that
the majority of anti-Qatari messaging were driven by
bots.90 This finding was important because it was one
of the first measureable evidence that countries use
social media to escalate, signal and pressure other
countries into desired behavior. By driving mass anti-
Qatar hashtags on social media Saudi Arabia and UAE
were using a new way of diplomatically pressuring
Qatari leadership to toe the line. A second case where
I could further test my hypothesis was the Al Aqsa
riots in July 2017. Measuring diffusion patterns of
seven widest-shared hashtags, I was able to infer bot-
driven versus organically-driven messaging, giving me
a good idea on external countries trying to influence
foreign crises.91 This gives us good data to test and
challenge some of the central IR hypotheses such as
signaling, bargaining, pressuring and diversionary
conflict theories.
2016, https://www.foreignaffairs.com/articles/2016-09-
14/how-turks-mobilized-against-coup.
90 Akin Unver, “Can Fake News Lead to War? What the Gulf
Crisis Tells Us,” War on the Rocks, June 13, 2017,
https://warontherocks.com/2017/06/can-fake-news-lead-
to-war-what-the-gulf-crisis-tells-us/.
Currently, I’m a co-principal investigator in a Turing-
funded study that aims to build an artificial-intelligence
based conflict event detection database. The project
combines some of the methodological perspectives
I’ve discussed here – text mining, network analysis,
geospatial data – to automatically harvest battlefield
digital data in order to generate armed events and log
them in real-time (with some redundancy-check lag, of
course).
Conclusion - How Can IR Benefit from Big Data
and Machine Learning?
Big data and computational approaches to social
research has revolutionized social sciences and will
inevitably impact how IR methods evolve in the
coming decades. Two factors define the potential of
computational research; sheer size of data that is
extremely hard (often impossible) to process with
conventional tools of quantitative or qualitative
analysis and the advent of more powerful tools that
allow us to zoom in and out of various levels of human
behavior simultaneously. From this perspective alone,
the big data revolution will force us to rethink a
fundamental component of IR research: the levels of
analysis problem. Big data gives us data granularity that
enables micro-level approaches such as behavior,
cognitive biases or worldview analysis, as well as the
volume that can be scaled to meso-level (networks,
collective action, ethno-nationalist movements) and
macro-level (ideology, identity, systems research)
simultaneously. When done properly, big data and
computational tools allow us to model and understand
human behavior much better than past approaches,
while it is also easier to misuse and exaggerate their
explanatory power.
One of the main problems with big data research is an
over-reliance on the processing power of the tools,
without an eye on cultural and local differences in data.
One very common line of dreadful mistake I
encounter is usually in social media extremism research
that concerns jihadi networks. When engineer or
programmer-dominated research groups employ
computational tools in extremism research without a
social scientist, and/or a scholar with area and cultural
expertise, they overwhelmingly produce faulty
machine learning word corpus clusters. These clusters
often confuse religious statements that express radical
behavior with commonplace, regular cultural religious
expressions. One computer science conference paper
I’ve had the misfortune of reading (and won’t cite) had
built a corpus of jihadi radical word corpus, which
included common religious terms that Muslims use
91 H. Akın Unver, “What Twitter Can Tell Us about the
Jerusalem Protests,” Washington Post, August 2017, sec.
Monkey Cage Blog,
https://www.washingtonpost.com/news/monkey-
cage/wp/2017/08/26/what-twitter-can-tell-us-about-the-
jerusalem-protests/?utm_term=.6a8a0bd94c55.

everyday, such as ‘Allāhu akbar’ or ‘InShaAllah’,
fundamentally skewing the results. Although this was
an extreme case of tone deafness in computational
research, there are very frequent, common and subtler
ways of bias in research that is produced by research
groups that aim to tackle culturally-sensitive social
science research. Equally problematic are social
science research clusters that aim to build machine
learning algorithms by checking Youtube tutorials,
without using a computer science specialist, or
generate behavioral models without a dedicated
modeller. Computational research reaches its true
potential in truly multi-disciplinary research clusters
and this is precisely why facilitator networks that
bridge diverse sciences disciplines and establish a
common language across them is the most urgent and
important step universities must take in initiating
computational research groups.
That is why machine learning, as a way of enabling
computers to build new ways of approaching evolving
tasks, without being explicitly programmed to solve
them, is a field that should go beyond computer
science and needs the attention of social scientists.
Since we can (and in the near future, will) build
machine learning algorithms to track the extent of
nationalist sentiment in multiple countries, explore
real-time public opinion during an international crisis,
or how armed or non-armed non-state actors behave
during a violent conflict, the topic doesn’t fall far off
of the radar of international relations. Not only should
future IR doctoral students and early career academics
will encounter issues related to big data, computational
social science and machine learning, some of them will
have to build a foundation in reading and
understanding how algorithms work and how to
communicate with computer scientists for
collaborative research. This means that IR PhDs will
have to learn Python or R as a foundational
programming language, and add a second software
(like ArcGis, Gephi, LingPipe, Ontotext) that fits their
immediate research needs.
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