Acknowledgements
Structure, Agency, Hegemony, and Action: Ukrainian Nationalism in East Ukraine
Corresponding Author:
Jesse Driscoll, Ph.D.
Assistant Professor
School of Global Policy and Strategy
University of California San Diego
9500 Gilman Drive
La Jolla, San Diego, 92093
Email: [email protected]
Non-Corresponding Author:
Zachary Steinert-Threlkeld, Ph.D.
Assistant Professor
University of California Los Angeles
Word Count: 13, 381
ABSTRACT:
Do people generally believe what they are told or say what they believe? Trends in the content
of a large sample of user-generated social media data from Ukraine provide strong evidence for
the latter claim. Among young Ukrainians, the explanatory power of hegemonic structural
forces that shape identities - e.g. Russia’s comparative advantage in cultural production that
occurs in the Russian language – is shown to be limited. Using millions of geolocated messages,
the strategic contest between hegemonic and counter-hegemonic discourse among Russianspeakers in Ukraine is mapped over space and time. An advantage of this methodology is
inclusion of data generated by Ukrainian citizens residing in occupied or secessionist areas parts
of the country, in which soliciting information in-person would have been costly and dangerous.
Manuscript EXCLUDING Authors' Names (attached document
must not include identifying information).
Structure, Agency, Hegemony, and Action: Ukrainian Nationalism in East Ukraine
Under Review: International Organization (IO)
Word Count: 13,381
Epigraph:
“I would like to remind you that what was called Novorossiya, back in the tsarist days,
Kharkov, Kugansk, Donetsk, Kherson, Nikolayev, and Odessa were not part of Ukraine
back then. The territories were given to Ukraine in the 1920s by the Soviet government.
… Why? Who knows?”
-- Vladimir Putin, April 17, 2014
1
Introduction
A fundamental question in the study of ideology is the degree to which cultural content
production is the product of conscious choice by strategic agents. Many social theorists argue
that choices are often functionally over-determined by the disciplining effect of internalized
constraints that are mediated by slow-moving social forces such as culture, formal state
institutions, class, religion, and focal disciplinary texts. These structures shape constitutive
relationships between those social actors, making certain actors empowered and others pliant and
willing to “accept their role in the existing order of things.”1 Rational choice approaches, by
contrast, are more likely to emphasize human agency. Cultural identity can itself be a political
resource, since structures themselves can be modeled as equilibrium results of strategic contests,
political entrepreneurs can use cultural appeals to attract mass followings, and individuals can
modify their cultural identities to improve their life chances.2 The language of cultural
hegemony is often employed to describe settings in which it is difficult to discern any conscious
choice, let alone rational choice, contributing to habituated ways of thinking.3 One test would be
to observe whether, and to what degree, ideological hegemony constrains the spread of
ideologically-charged information after a state’s formal institutions disintegrate.
In the Spring of 2014, Russian-speaking Ukrainians found themselves in an existential
period of crisis. Many of the core institutional bargains that structured the polity for the Russianspeaking Ukrainians – not only the Party of Regions as a preference-aggregator and the shared
assumption that power would rotate via a regular election cycle, but also the country’s interstate
1
Lukes (1975), 24, is making an obvious reference to Foucault (1971). The assumptions
undergirding our study were deeply informed by the discussion of hegemonic power in Ikenberry
and Kupchan (1990), 283, 285-94.
2
Laitin (1986), 11.
3
Laitin (1986), especially Chapter 1, 5, and 8. Hoare and Nowell-Smith (1971) provide an
English translation of Antonio Gramsci’s relevant works.
2
borders – ceased to exist. The content of the nationalist narrative that would be rendered in the
social memory of Russian-speakers to make sense of these upheavals was in the process of being
written. The process was transparently democratized via social media. A common behavior was
to attempt to steer the narrative with speech acts, and Russian-speakers residing within Eastern
Ukraine faced a real choice: whether or not to perform the script that they had been handed by
Russian state-influenced media. Were Russian-speaking Ukrainians living in Novorossiya likely
to adopt Russian-government talking points and reproduce their version of events? Did Russia’s
ideological hegemony over Eastern Ukraine manifest in predictably pro-Russian beliefs about
the political crisis as it was unfolding?
We employ social media data to answer this question. Political disorder, and high
political stakes, magnified the biases and costs associated with trying to determine what Russianspeaking Ukrainians were thinking via survey data, historical voting data, or elite interviews.
Instead, our approach was to analyze a subset of Twitter data: 4.5 million tweets that were
generated within the territorial borders of Ukraine between January 1, 2013 and July 31, 2014.
Though we make no claim that our sample is representative, it is informative: If one entertains
the assumption that sentiments shared on Twitter map onto political beliefs and speech-acts
offline, it is possible to map changes in hegemonic and counter-hegemonic sentiment across
regions and over time using this data.
There is some evidence that Russian-government keywords were repeated by Russianspeaking Ukrainians on Twitter, especially in the eastern part of the country. Countervailing
counter-hegemonic trends dominate during the period we study, however. We demonstrate that
hegemonic (pro-Kremlin) Russian-language content was more likely to be generated in parts of
Ukraine where expected, based on slow-moving structural variables. We also demonstrate that
3
counter-hegemonic (anti-Kremlin) Russian-language tweets tended to be generated the same
areas of the country, suggesting that contentious offline political behaviors spilled over into
social media behaviors. One might employ the analogy of white blood cells attacking a virus, as
decentralized Ukrainian nationalists pushed a counter-narrative, in Russian, back against
invasive propaganda. Either Russian hegemony in the Eastern region of Ukraine is weaker than
many area experts initially assumed, or the theory of ideological hegemony is not specified with
sufficient precision to make predictions sufficiently nuanced to capture the variation in narrative
content practiced in everyday life.
Methodologically, the primary contribution of our study is to demonstrate that attitudes
expressed on social media track spatial and temporal changes in youth beliefs. Ukraine
demonstrates that this methodology can be especially informative during periods of large-scale
social change, since the internet infrastructure can continue to operate and generate data even
after institutions of the state cease to function. A secondary methodological contribution of our
study is to show how spatial and time trends in social media production can be brought to bear
on unresolved questions in political science, such as whether micro-political theories of strategic
identity formation ought to be privileged over macro-political theories of inherited ideological
hegemony. These strategic behaviors by Russian-speakers serve as a powerful counterpoint to
the common claim that Russia enjoys uncontested ideological hegemony over Russian-speakers
residing in Eastern Ukraine. Our claim is not that every 140-character tweet or “like” on
Facebook is a political act, but it is possible to make inferences about the geospatial and
temporal distribution of community-belief structures with a society based on the relative
prevalence of certain social media content.
4
Individuals across the world routinely record and share their lives online using platforms
such as YouTube, Reddit, LinkedIn, Facebook, and Twitter. Since social media data lowers
transaction costs associated with generating collective action, it is not surprising that social
scientists are beginning to use these data to make inferences about politically important offline
events.4 Our paper is the first (to our knowledge) to combine text-as-data methods with
geotagged data to make inferences about the presence or absence of a hegemonic constraint.
How, whether, and under what conditions ideas – especially false or contested ideas – are
sustained other than through formal state institutions is a high-stakes question for policymakers
and social scientists. Social media provides opportunities to address these questions with rigor.
The paper proceeds in five parts. The first section provides background information on
the 2014 political crisis in Ukraine. A second section introduces our theory and two research
hypotheses. A third section presents our empirical strategy and data. Hypotheses are examined
in the fourth section and a final section concludes with a discussion of potential confounds.
1. Contested Narratives of Maidan: A Primer on the 2014 Crisis in Ukraine
Though Russian and Ukrainian nationalist narratives diverge quickly, there is agreement
on certain facts. Hundreds of scientific public opinion polls have been conducted in Ukraine
demonstrating the social reality of the “East-West” regional division in Ukrainian national
politics. The polity is often divided by shorthand into groups that break down along territorial
4
The promise is that this new data can provide a window into activity as it happens, such as
mood shifts (Golder and Macy 2011), unemployment rates (Llorente et al. 2014) health
outcomes (Chew and Eysenbach 2010; Davidson, Haim, and Radin 2015), stock market changes
(Bollen, Mao, and Zeng 2011), and crime (Gerber 2014). These activities include outcomes of
interest to political scientists: social media can be used to measure the ideology of voters
(Barbera 2015), to compare foreign policy beliefs across countries (Zeitzoff et. al. 2015),
mobilize voters (Bond 2012), and to examine dynamics of contention (Aday et. al. 2012;
Steinert-Threlkeld et. al. 2015; Metzger et. al. 2015).
5
lines: Central and Western Ukrainians that speak Ukrainian, Eastern and Southern Ukrainians
that speak Russian, and Russians residing in Eastern Ukraine that speak Russian. Electoral maps
for every parliamentary and presidential election since Ukrainian independence reveal distinct
voting blocs in the country’s far west and southeast that elect representatives with very divergent
policy preferences.5 Still, since Ukrainian independence, the East-West division was one of the
classic “dogs that did not bark” in post-Soviet ethnic conflicts.6 What emerged in its place was
political polarization and gridlock. Relations with Russia normalized after independence. In
1997, the two states pledged to respect each other’s territorial integrity in The Treaty of
Friendship Cooperation and Partnership. Since the late 1990s, power in Ukraine has essentially
rotated between polarized elites in the West and the Russophone Party of Regions in the East,
with the party out-of-power maintaining a soft veto over many aspects of policy. Most
Ukrainians are fluent in Russian and many travel frequently to Russia.
Western and Eastern narrative timelines of the crisis begin to diverge around November
of 2013, when President Viktor Yanukovych declined to sign an association agreement with the
European Union (EU) in favor of exploring membership in Russia's Eurasian Economic Union
(EEU). Many Ukrainians, particularly in the West, came to fear that the window was closing on
the possibility of labor mobility to and economic integration with Western Europe. Broad-based
opposition emerged, galvanized by the prospect of a geopolitical pivot to Russia. A wide
5
See Craumer and Clem (1999). But for a primordialist primer on Ukrainian history that makes
the same essential point, see Huntington (1996), 165-8. A variety of survey data suggests that
Eastern and Western Ukrainians differ substantially on policy preferences. See Arel (2002),
Barrington and Herron (2004), Constant et. al. (2011, 2012), and Frye (2014. These sorts of
broad generalizations obscure vast ethno-linguistic complexity at the oblast level, of course, but
patterns of non-voting in the October 2014 Parliamentary election track an easily visible Easternsloping gradient, as many have noted.
6
Posen (1993:42) notes that in the early 1990s there were widely-shared expectations of military
intervention by the Russian military in the event of violent ethnic escalation. He speculates that
this played a stabilizing deterrent role.
6
coalition of civil society groups organized protests and weeks of escalation culminated in
prolonged violent clashes with state security forces in Maidan Square at the heart of Kyiv – a
complex set of events that we shall subsequently refer to as “The Maidan Events” in this paper.7
A motion to censor the government and order police back to their barracks passed the Rada on
February 20, 2014, facilitated by a cascade of defections from within the governing Party of
Regions. Two days later citizens awoke to find that President Yanukovych had unexpectedly
fled the country. Then, on February 27th, Russian special forces seized the Crimean Peninsula. A
referendum was organized for March 15, and the territory has been subsequently absorbed into
the territory of Russia (according to official Russian government demarcations of their national
territory). Many Western observers were quick to blame Russian military policy for the
humanitarian disaster resulting from the secessionist violence and to call for multilateral conflict
resolution.
When liberal elites in NATO-member countries use the phrase “conflict resolution” many
Russians simply hear “encirclement.” Western government representatives are accused of crying
crocodile tears for the lives lost in Eastern Ukraine, since the crisis is broadly-understood as
having its root cause in decades of meddling in Russia’s sphere of influence under the aegis of
“democracy promotion” and “building civil society.”8 Western government employees, it is
argued, employed a cynical selective vision when they ignored the role of far-right parties as
instigators of the Maidan events and key figures in the “post-coup” regime.9 In this context, the
Kremlin now admits it used the anarchic situation in Ukraine as a proximate justification for
military intervention into Crimea, but view it as an overdue “homecoming.” Russia emphasizes
7
The preferred terminology of the U.S. State Department is “The Revolution of Dignity.”
This position is well-summarized in Mearsheimer (2014).
9
Yukoshenko (2016).
8
7
NATO’s aggressive expansion, the fundamental arbitrariness of federal boundaries that were
never envisioned as transforming into sovereign state borders, and the long-standing Russian
national interests in the region. 10 In this geostrategic context, very well-educated and
“Westernized” Russian-speakers – even many that are not friendly towards Putin’s regime – will
not hesitate to assert that the Maidan protests were engineered by the CIA, and will usually treat
arguments to the contrary as deeply naïve.
From the point of view of the pro-West Maidan protesters in Ukrainian civil society,
Russian assertions that their risky and costly actions were engineered by Western intelligence
agencies is insulting and absurd. It is understandable that many Russians would rather believe in
a CIA-engineered coup than grapple with the fact that the Maidan events represent clear
evidence of broad social support for geopolitical and economic realignment away from Moscow,
which is why Russian military and economic pressure has been brought to bear as part of a
strangulation strategy to coerce a change in public opinion. The implied claim that Ukrainian
sovereignty is not “real” is anathema, as is the idea that that the borders of small states can be
rewritten according to the whims of the great powers. 11 Many fluent English speakers in Kyiv,
in an effort to shame the Anglophone great powers into sending aid, have demonstrated that they
know the word for when one country sends its military forces into another country (invasion) and
the word for the use of violence against civilians to coerce political concessions (terrorism).
The task of describing the political status of Russian-speakers in contemporary Ukraine
is, for the above reasons, quite fraught. Normative baggage is often loaded into basic
10
The term Novorossiya (New Russia) made its first appearance in Russian legal documents and
on official maps in the latter half of the 18th century, according to O’Loughlin et. al (2016), 4.
These claims are often depicted as extending the length of the north Black Sea. See
MacFarquhar and Kramer (2014).
11
Hillary Clinton immediately drew an analogy between Putin’s actions and the Anschluss. See
also Rucker (2014) and Beissinger (2015).
8
descriptions of events, raising conceptual and methodological challenges for social scientists
attempting to assume a neutral role.12 Ubiquitous misinformation and highly-slanted journalistic
coverage in the service of state policy has contributed to the fog of war.13 Though analysis of
events was difficult at the time, the broad contours of the subsequent five months within Ukraine
is, in retrospect, largely uncontested. In response to the seizure of Crimea and the perceived
impotence of the Ukrainian state, many pro-Russian militias self-organized.14 Dozens of militias
declared autonomous “People's Republics” in Ukraine’s East and many organized haphazard
popular referenda, in the hopes of receiving recognition from Moscow. Moscow sent barelyclandestine military assistance to the rebels while their diplomats denied strenuously that they
were doing so. Pro-Russian separatists, with assistance from volunteers within Russia,
proceeded to inflict humiliating losses on the Ukrainian military.15 Tens of thousands were
displaced by the fighting. But support within Ukraine for a broader separatist project, centered
on historical Novorossiya, failed to materialize or gain very much traction outside of a very
narrow strata of poor, elderly Ukrainians who felt nostalgia for the Soviet Union.16 Lines of
territorial control in the East stabilized in the summer of 2014 and have barely budged since.17
The phenomenon that we shall document for the rest of this paper refers to behaviors that
often occurred far from the battlefields in Ukraine’s far east, as sophisticated political actors
12
See Lustick (1993), 24, 26-56. The absence of a shared language to describe events by the
non-rotating members of the United Nations Security Council is an ongoing barrier to conflict
resolution. Though the identification strategy in this paper exploits the existence of these
mutually incompatible narratives, which we are about to describe in the text of the paper itself,
our goal is not to celebrate or inflame misunderstandings.
13
See, for instance, “A Strategy of Spectacle,” The Economist, March 19-25 (2016), pages 2123.
14
Roth (2014a); Roth (2014b); and Smale and Roth (2014).
15
Kramer (2014).
16
O’ Loughlin et. al. (2016), 16-17.
17
This process is well-documented in day-by-day microdata (Zukhov (2016)).
9
engaged in what scholars call a “war of position”18 to describe the events in real-time. It was
immediately clear that the Russian government was treating Ukraine as a theater in a zero-sum
militarized conflict and that information operations would be part of the conflict. The
hegemonic narrative within the Russian-language space, promulgated by Russia’s government
and state-controlled media, asserted that Russian-speaking Ukrainians were, in the spring of
2014, hostages trapped in a state whose politics had been hijacked by right-wing forces
coordinated by Western intelligence agencies and with the tacit consent of NATO command. A
counter-hegemonic narrative, promulgated by the Ukrainian government, recalls the Maidan
events as analogous to the events of 1989. The hegemonic narrative is calibrated towards
convincing Russian-speakers in Central Eurasia, and uses provocative words like “fascist” and
“coup.” The counter-narrative, calibrated toward the Russian-speaking diaspora residing in the
West, emphasizes words like “invasion” and “terrorism.”19 This language is both instrumental
(i.e., to draw NATO attention to the region and to emphasize the criminal and illegitimate nature
of their opponents) and strategic (in that it denies the premise of the other side’s claims about the
legitimacy of the government in Kyiv, functionally excluding them as coalition partners in a
post-conflict polity). Very high levels of distrust and mutual hostility between polarized
civilians on both sides of the conflict have raised the stakes of the standoff. Components of these
divergent “narrative tracks,” which show no sign of converging, are summarized in Table 1.
[TABLE 1 ABOUT HERE]
2. Spheres of Influence and Wars of Ideas: Two Hypotheses on Narrative Production
18
Lustick (1993), 53-56. His discussion of state boundaries on 38-9 is useful and relevant to this
case.
19
See Arel (2014).
10
When Russian-speaking Ukrainians opined, to their social media peers, that the proRussian militias were engaged in “terrorism” or that the government in Kyiv had been seized by
“fascists,” were they doing what they felt they were supposed to do or were they calculating that
it was a good idea instrumentally for them to express those sentiments?
Nationalist identity politics in the area play out in the shadow of what might be called
Russian political hegemony. Anthony D. Smith’s famous definition of national identity begins
with a reference to the stories that people tell each other. Whether these stories are thought of as
constitutive or as common knowledge of shared focal points for coordination, the content of the
stories rarely changes.20 Cultural identity is often seen to be relatively static, not a choice
variable that can be easily manipulated on short notice: Most individuals are born into
communities in which certain beliefs are shared by most members – the hegemonic “givens” that
constitute the “rules of the game.”21 Hegemonic beliefs of this sort create an outer-boundary of
possible political demands, constraining the possibilities around which interests or identities
could coalesce.22
Russia has long been assumed to possesses a comparative advantage in the production of
what Strange (1987) would call structural power of knowledge creation in much of the territory
that used to comprise the Soviet Union, including Ukraine. Ukrainian nationalism has always
seen itself self-consciously as “counter-hegemonic,” at a competitive disadvantage vis-à-vis its
20
(“1.) … myths and memories of common ancestry and history of the cultural unit of
population … 2.) the formulation of a shared public culture based on an indigenous resource
(language, religion, etc.) … 3.) the delimitation of a compact historic territory or homeland.”
Smith (1994) 381. See also Laitin (1986), Chapter 10.
21
Laitin (1986): “People do not quit their ethnic groups as they do their jobs; nor do they change
their ethnic identities in the same way they change their brand of beer,” 101.
22
Lustick (1993), 38-9.
11
wealthier and more populous neighbor. Russia’s cultural pull is not felt evenly across the
country, however. The “East-West” division has deep historical roots that probably precede the
Soviet experiment.23 Darden and Grzymala-Busse (2006) suggest a division between
“Hapsburg” or “Polish Ukraine” in Western Galicia and what might be called “Russian Ukraine”
East of the Dnieper River. The argument, in its strong form, is that a socio-cognitive mechanism
anchors this division, relating to the timing of a community’s first shift “from an oral to a
literature mass culture,” since school curricula “shaped common national identities and political
loyalties” in patterns that can endure for generations.24 Hapsburg divide-and-rule policies created
“a new Ukrainian identity in Galacia to counter the active nationalism of the Poles and potential
irredentism of the Russians” while, further east, Russian-speaking Ukrainians “were schooled to
believe … that they were part of a broader Russian nation, with a common past in Kievan
Rus”.25 Spatial patterns, then, may explain intergenerationally transmitted “standards for what
would constitute legitimate rule” and “shared understandings of economic and political
development”.26
An advantage of the Russian political hegemony hypothesis is that it offers clear
predictions, at least in the Ukrainian case, of where one would expect to observe the
reproduction of the Russian narrative. Since the Russian government has a de facto monopoly
on the supply of certain kinds of information via television, variation in demand for that
information is the critical variable of interest. Linguistic demographics and distance from
23
Darden and Grzymala-Busse (2006).
Ibid. 90, 94, 98-103
25
Ibid 95, 97
26
The authors proceed to assert that the consequences for society in Eastern and Western
Ukraine are substantial, manifesting as differentiated understandings between the appropriate
valuations of Soviet-provided public goods (like industrialization and electrification), bifurcated
social memory (105, 107), Communist party vote share in contested elections (110) and the fact
that in the East “pro-Russian sentiments … predominate” (106).
24
12
Moscow should be strongly predictive of anti-Kyiv sentiment, reflected as anti-Kyiv storytelling
in times of crisis.
H1: The Russian-Language Hegemony Hypothesis: As one goes further east within Ukraine,
the content of speech acts is more likely to reflect the point of view of the Russian government.
This hegemonic account, however, provides a diminished role for human agency.27 The
Russki-Mir contains vast ideological heterogeneity, and people use the language of Russian to
express very different political sentiments. Whereas the ideological hegemony arguments
sketched above suggest that reciting certain scripts are non-strategic and functionally overdetermined (analogous to doing the wave at a football game or reciting a rote church prayer), a
strategic communication perspective treats speech acts as performances designed to set
boundaries on the political coalition (analogous to calculated moves on a battlefield, choosing
what to ask for in a high-stakes negotiation, or what to memorize as a closing statement in a
court trial). A second theory would emphasize improvised counter-hegemonic nationalism,
aimed at lowering the costs of counter-mobilizing collective action.
Constitutive relations between social actors are functional prerequisites to community
formation and collective action.28 While it is possible to crudely manipulate the boundaries of
these communities through the use of force, speech acts are a more common tool (Habermas,
1985). Preparing, rehearsing, and performing speech acts to reify competitive and mutually
exclusive readings of the historical record is a core component of political life. Most speech acts
27
This point is made quite eloquently in Zhurzhenko’s (2002) response to the
“Huntingtoniazation” of the academic discourse on Ukrainian politics in the Western academy.
28
Arendt (1958).
13
are not efforts to create coordinated focal points for political activity, but those few acts that do
have this characteristic correspond to counter-hegemonic “wars of position” or “wars of
maneuver.”29 In caricatured formulations of the theory of hegemony, the weight of culture
overwhelms human agency, such that inherited stories about the past are fully constitutive,
leaving little room for anything but rote reproduction. Strategic elites draw attention wars of
position and wars of movement to attempt to shape hegemonic and counter-hegemonic
interpretations of ongoing events.30
Counter-hegemonic political speech has three characteristics: it must occur in the same
language as the hegemonic narrative, attempting explicitly to confront an audience with
alternative sources of information; it must emphasize conflictual or competitive trans-historical
processes; and it must contain coded information that suggests that those who spread the
hegemonic narrative are not trustworthy.31 When individuals spread information of this kind,
they often believe themselves to be engaging in brokerage: “the linking of two or more currently
unconnected social sites by a unit that mediates their relations with each other and/or with yet
another site … [to] create new collective actors.” 32 In Ukraine, for instance, signaling shared
acceptance of exclusionary labels (e.g., racial slurs towards either ethnic Ukrainian or Russians,
``terrorist," “fascist”) demarcate the limits of acceptable political coalition-building projects.
Opting to use this language is an overtly political expressive act, and often the result of a
calculating attempt to use words (and technology to dissimulate those words) to accomplish
29
Lustick (1993).
Ibid, 53-56.
31
Lupia and McCubbins (1998) note the importance of trustworthiness as one of the three
prerequisites of meaningful communication (alongside costly signaling and expert knowledge).
32
McAdam, Tarrow, and Tilly (2001). Brokerage is distinguished from the simple dissemination
of information, which the authors call “diffusion,” 332-335, 142-3.
30
14
something modest and specific: to link previously unconnected social sites into a new political
coalition, advertising its intent to exclude rival social groups.
Whether or not social actors actually shape opinions is less relevant to our theoretical
account than their aspiration to do so. If these efforts are strategic responses by citizens
attempting to participate in an information war, “fighting back” to change the story that is being
told, rather than passively receiving a hegemonic discourse, then we would expect the
oppositional narrative to appear in the same locations as the hegemonic narrative. Since this
formulation emphasizes that the counter-hegemonic narrative emerges as a strategic response
that, ought to correlate spatially with the hegemonic narrative, it implies the following:
H2: The Counter-Hegemonic Wars of Position Hypothesis: The content of Russian-language
speech acts is more likely to be antagonistic towards the point of view of the Russian government
in the same parts of the country that pro-Russian government speech acts are taking place.
3. Data
Data generated via social media platforms represents an exciting new frontier in social
science, but there are important hurdles associated with the uptake of “big data” within political
science.33 One problem is that the quantity of data is overwhelming. Every day, users publish
500 million messages on Twitter,and Facebook ingests 500 terabytes of new data (Constine
2012). Since most of this content is apolitical – celebrity gossip, family holiday greetings, and
the like – even when working with a tiny sample of these data, noise can quickly drown out any
signal if careful processing is not employed. Consensus on best practices for processing signal
33
“Big data” is a marketing term with no clear definition. A more precise definition for this
paper’s data would be “user-generated data,” but we use the colloquial term for familiarity.
15
from noise do not yet exist. A second concern is sample bias. People who use social media
may be so systematically different from the population of non-users as to render generalizations
based on a Facebook or Twitter data sample uninformative.34 A third concern is that, unlike
traditional surveys or polls, data generated via social media are not the result of solicited
questions. It is difficult to interpret silence and easier to measure the extremes than the
moderates. Certainly analysis of non-response, “don’t know” and “refuse-to-answer” patterns in
a survey can be highly informative.35 There is no clear analogue in social media production.
Finally, there is a problem of cheap talk: that what people say online may be an insincere
performance, reflecting something other than their true beliefs that will guide actions. Many
scholars are therefore hesitant to connect online behaviors to real political outcomes.
These are serious concerns. Though there disadvantages associated with this new data
frontier, these types of data also have many attractive features for social scientists.
3a. Advantages of Social Media
First, social media allows the analysis of data from populations that would otherwise be
difficult to reach via survey enumeration. The Donbas region and the Crimean Peninsula in
spring 2014 are salient examples: populations living in areas that became extremely difficult to
reach with traditional survey enumeration teams due to the presence of Russian military forces
and active fighting remained in our study. No one was put in harm’s way collecting our data.
Second, social media data are not structured. Individuals express opinions on topics of
their choosing, producing large, spontaneous datasets of speech. The researcher does not have to
34
35
Hill 2015, Malik 2015
See Driscoll and Hidalgo, 2014.
16
pre-define the topic under study. Text-as-data allows the researcher to proceed with a more
accurate understanding of topics' real saliency in the society that is being studied.
Third, analysis of large quantities of social media is completely non-invasive. What is
observed is only the voluntary behaviors of users. The observation takes place from a distance.
There are many epistemological advantages associated with participant observation, but these
benefits are weighted against the risk that the presence of the observer changed subjects’
behavios. This concern becomes more salient in times of social unrest when social scientists are
engaged in descriptive efforts designed to influence policy or shape social memory. Ubiquitous
smartphone and internet penetration provides researchers the opportunity to observe tens of
millions of decentralized micro-decisions in places like Ukraine, as individuals opted to either
repeat the Russian government’s party line or not. These behaviors can be aggregated and
measured from offshore.
Fourth, though it is true that the data provided on social media are haphazard in their
production, and that users of social media may not be representative of society, the other side of
the argument is that the data that is produced is, arguably, more likely to represent true opinions
of those users than surveys. This is especially salient for studies of political behavior in war
zones, authoritarian states, or contentious political environments in which citizens are strategic
about information revelation and sophisticated about social desirability bias.36
Finally, observing trends in social media data is faster and lower-cost than the kinds of
observational data that emerge via in-person enumeration of a representative survey. This
difference is increased once one takes into account the secondary costs of in-person enumeration
36
The act of enumerating a survey can have the effect of inventing exclusive identity categories
that do not reflect lived social experiences. Self-defined “Russian-Speaker” is often presented as
a statistically significant predictor variable for a host of politically-relevant questions in Ukraine
because the technology of measurement flattens social nuance.
17
(devising an instrument, finding a team of surveyors, applying for grant funding, IRB approval
for human subjects, and waiting weeks or months for the data to be cleaned). With less, but not
less rigorous, preparation, researchers can pool observations of many more people than would be
feasible or economical through in-person enumeration. The trade-off is that unstructured data are
overwhelmingly apolitical, so filters must be customized to the research question. Large samples
of individuals – 146,977 in the cumulative sample of our study, for instance – allow researchers
to overcome the low prevalence of relevant discourse. Accounts can be inexpensively observed
for long periods of time, dramatically lowering the cost for incorporating longitudinal analysis as
validation of, or compliments to, cross-sectional studies. Social media data are less expensive
than, yet provide certain advantages over, traditional kinds of polling and survey data.37
3b. From Text To Data
A “tweet” is a 140-character text to be distributed to one's followers on the social media
platform Twitter. We analyze Twitter and not Facebook or VKontakte (Russian Facebook) data
for four reasons. First, it is a popular social media platform with 280 million monthly active
users generating 500 million tweets per day. Second, Twitter does not generally edit or censor,
so the content of messages can easily reflect political extremes. Third, Twitter, like Facebook, is
used to discuss a diverse range of topics (Frank et al. 2013; Huang, Thornton, and Efthimiadis
2010). Other sites, such as LinkedIn or Instagram, are either more focused on narrow topics or
not used conversationally. Fourth, the choice to analyze Twitter over Facebook in our study was
37
To pick a recent example, the NSF is funding an ongoing project to understand attitudes in
Eastern Ukraine. This project lasted from May 15, 2014 through April 2016 and cost $156,633
(Toal and O’Loughlin 2015). The survey will no doubt provide compelling data on beliefs in
Ukraine and its 140 questions will surely provide information that will not appear spontaneously
via social media. This paper, by contrast, has cost (at most) $25,000 in labor and computational
resources.
18
due to data availability: Twitter provides the most data from the most representative sample at
the lowest cost.38 Approximately 90% of Twitter users make information publicly available.39
Starting on September 1, 2013, we connected to Twitter's streaming application
programming interface (API), requesting only tweets with GPS coordinates. When connecting to
the streaming API, Twitter returns all tweets matching filtering parameters until the number of
tweets returned equals 1% of total tweet production. Since 500 million tweets are authored every
day, that ceiling is 5 million tweets. For example, there are probably more than 5 million tweets
with the characters “the”, “lol”, or “?”, so asking Twitter to supply all tweets with them will
return a sample of those tweets. Asking Twitter to supply all tweets from the user
@BarackObama, on the other hand, will return every tweet that account authors, assuming it
does not publish more than 5 million tweets. Since about 3% of tweets contain GPS coordinates,
approximately 1/3 of all tweets with GPS coordinates have been gathered.40 This study contain
198 days, or roughly 990,000,000 tweets. Searching those tweets for all from Ukraine between
January 1, 2014 (midway through the Maidan protests) and July 17, 2014 yielded 4,510,634
tweets. These tweets are used for the rest of the analysis in this paper.
38
Facebook also provides a programming interface for anyone to access its data, but only for
data which users have made public is available to researchers. Gaining private data requires
either permission from a user, such as when a user installs an application, or from Facebook's
data science and legal teams. The former will restrict the sample size; the latter gives veto power
to Facebook and increases costs.
39
Moore (2009).
40
Leetaru et. al. (2013). Though the question of whether Twitter users are systematically
different from non-users is unresolved for the authors at this time, there is no compelling
theoretical rationale that Ukrainian users with geotagged tweets are systematically different than
Ukrainian users who have turned GPS tracking off on their smartphones – certainly nothing
systematic that would threaten the inferences in our study.
19
Our research design avoids common pitfalls prevalent in emerging social media
analysis.41 First, most studies rely on hashtags as an initial filter, functionally selecting on the
dependent variable with no analysis of the beliefs of out-of-sample individuals.42 Second, many
internet-behavior studies fail to account for how significant offline events affect observed
patterns. Real world “field events” are essentially omitted variables in numerous studies of
online behaviors. Our empirical strategy anticipated these concerns. In contrast to the method
employed elsewhere our sample of tweets is not initially selected on keywords or the hashtag
they contain, but rather by geography.43 We do filter for political speech through a process we
describe below, but the initial sample is not selected on the dependent variable. We also
organize the data to compare content trends over time and space specifically in order to show
how Russian-language “hate speech” keywords respond to “field events” (such as Russia’s
seizure of Crimea, the referendum on independence in Ukraine’s east, and more). These trends
are always displayed as a percentage of overall local activity on Twitter to avoid selecting on the
dependent variable. A biased picture would have emerged if we only examined #Euromaidan
tweets.44
Every study of social media behaviors must begin by applying filters to make sense of the
overwhelming quantity of data. Our first filter was geography. In order to exclude nonUkrainian chatter about Ukraine (originating from North America, Russia, or Europe) from our
study, we opted to rely only on Tweets with GPS coordinates. The decision by users to enable
geolocation on their smartphones is our first filter. The advantage is that we can be assured that
41
Tufekci (2014).
For a related criticism, see Zhang et. al (2015).
43
Metzger et. al. (2015).
44
Much of the observational scholarship informed by participant observation in the Maidan
events suffers from this kind of bias.
42
20
all of our study subjects Tweeted from inside Ukraine. A potential disadvantage is that it may be
a somewhat biased sample of overall Ukrainian Twitter users.45
Our second filter is language. We are interested in only the subset of Twitter content that
contained political speech in the Russian language and we restrict analysis to Russian-language
keywords. The Maidan events brought many new Ukrainian youth to Twitter, presumably
because the technology lowered the costs of coordinating protest behaviors.46 Recent work
looking at Twitter during Ukraine’s Euromaidan events shows that, after selecting a sample on
hashtags and keywords, users increase the proportion of tweets they write in Russian, regardless
of whether their default language is Russian or Ukrainian, after the seizure of Crimea.47 Figure
1 provides evidence of the same behavior in our subsample. The switch to Russian occurs only
amongst a subset of individuals, suggesting that narrative contention was a phenomenon distinct
from the rest of the communication occurring in our sample.
[FIGURE 1 ABOUT HERE]
[Caption: The frequency of tweeting in Russian increases after Viktor Yanukouvych flees
the country. The solid line is all accounts in our sample; the dotted line, for only those
accounts that engage in hegemonic or counter-hegemonic narrative construction While
accounts generating tweets that use politically-charged keywords clearly switch into
Russian after the Maidan events, a similar change is not seen in the full sample.]
45
In a study of American Twitter behaviors, Malik et. al. (2015) contend that because geotagged
Tweets are more likely to originate from smartphones, they are more likely to come from
wealthy individuals. While rich people may be more likely to own smartphones, Ukraine has a
large middle class and there are many different kinds of cheap commercially available Android
phones that can be purchased at a reasonable price in every major city in Ukraine. When you
add to this the fact that one can (and many do) access social media (particularly Twitter) from
“dumb” phones in the Former Soviet Union, we have no ex ante reason to think that it should
make our sample more pro-West. Our empirical strategy also explicitly controls for regional
trends to calculate subnational temporal trends reported below, alleviating residual concerns.
46
Tucker (2014).
47
Metzger et. al. (2015).
21
The Russian government used its influence over Russian-language media to promulgate a
clear script, complete with heroes and villains, frames and provocative metaphors, in order to
create focal points for those Ukrainian citizens who wished to “opt out” of the Ukrainian state
project.48 The majority of citizens in Ukraine are functionally fluent in Russian and receive their
news about politics primarily from television. The hegemonic narrative had the following basic
components: 1) a coup had taken place in Kyiv in February of 2014; 2) fascists seized the
Ukrainian state apparatus; 3) analogies to World War II, linking “NATO” with “Nazis” using
consonant repetition; and 4) anti-Americanism.49 We constructed a dictionary of Russianlanguage synonyms for fascism to test Hypothesis 1. The counter-hegemonic narrative emerged
without nearly as much centralized direction, but had the following components: 1) a broadbased, non-violent social revolution against an illegitimate government had taken place; 2)
Russia had responded to the loss of their client by illegially invading Crimea; and 3) by
encouraging the formation of illegal militia units to engage in terrorist activities against patriotic
Ukrainians. A smaller dictionary (Slavic-language translations of the word terrorism) is used to
test Hypothesis 2. A tweet is coded as fascist if it contains at least one word in the fascism
dictionary, terrorist if it contains at least one word in the terrorism dictionary. A single tweet can
be both fascist and terrorist, but no single tweet can count more than once no matter how many
flagged words it contains.50 Dictionaries are presented in Table 2, and word clouds based on the
content of the Tweets selected by the dictionary sorting are found in Figure 2.
48
Slanted Russian media coverage has frequently been described as an information operation or
an arm of state policy. See, for instance, Sherr (2015).
49
Cottiero et al. (2015) and Toal and O’Loughlin (2015).
50
Careful readers will note that the word “terrorism” appears in both dictionaries, since this word
was often deployed in descriptions of post-Maidan anarchy (e.g., “right-wing terrorism”) and
activities of pro-Ukraine militias (“terrorism against Russian-speaking civilians”). Subsequent
analysis suggests including this word still provides identification, as we show below.
22
[TABLE 2 [KEYWORDS] ABOUT HERE]
[FIGURE 2 ABOUT HERE]
[Caption: As another validity check, we show the 100 most common words associated with
fascism (left) and terrorism (right) discourse. The results are shown in these wordclouds.
The pro-Russian cloud (on the left) has “Right” (a reference to to the ultra-nationalist
right-wing party that played a key role in the Maidan events) as the most prevalent word.
The pro-West cloud (on the right) has “Terrorist” as the most prevalent word.]
3c. Descriptive Trends
The assumption that informs the analysis in this paper is that spreading content through
one’s social network is analogous to shouting and hoping to be overheard. Technological
changes allow individuals to self-publish instantly, at negligible cost and with no editorial filter,
potentially projecting their charismatic authority over vast distances. In our view, this is an
easily-measured form of ancient and well-analyzed discursive practices.
A practical limitation to making inferences from content analysis of Twitter data is that it
can be difficult to know who an individual actually is or if the account belongs to a real person
(rather than a company or bot) without further investigation. In traditional surveys, respondents
may be asked to provide demographic information, such as age, gender, education, occupation,
partisan support, and marital status; Twitter only requires an e-mail address and a screen name.
This is only a superficial barrier, however; at least some additional demographic information on
almost all users in our sample was easily available and public through detailed research, on and
off Twitter.51 We manually coded each of the 1,592 users who authored at least one geotagged
51
This effort was inspired by concerns raised elsewhere by Tufekci (2014) – in particular her
sociological observations regarding strategic behaviors by users, many of whom are resentful of
the fact that their behaviors are being studied by social scientists employed by advertising
conglomerates. The thrust of her observation is that many users engage in familiar behaviors
23
tweet about the hegemonic or counter-hegemonic discourse, using a combination of public
searches on Google, Facebook, and VKontakte. We did not record names of users, but, following
work that handcoded accounts from Tunisia and Egypt, we coded respondents on profession, sex,
age, and primary language.52 As Figure 3b makes clear, the sample is dominated by people
whose ages were indeterminant from what could be found online, but other information
(including, importantly whether the user was not a real person or an automated “bot”) was easily
discernable We read the content of the whole sample to ensure that our coding criteria were
working and to assess the prevalence of hate-linking, ironic posting and other possible
confounds. These behaviors were rare.
Demographic characteristics of the sample and summaries of how characteristics map
onto political opinions are presented in Tables 3 and 4. The sample skews young and male: only
8 tweets are from individuals over the age of 60 and the bulk of users are ages 21-40. For most
of the accounts (1166/1592, 73.24%), it was not possible to identify the user’s profession, but it
was not difficult to tell that the account belonged to a real person, not a bot. Only 37 accounts
were unidentifiable. Very few accounts (9) belonged to self-identified government officials or
state bureaus and only 20 belonged to news organizations. About three time as many of our
respondents fell into the “Other Individual” category as all of the rest of the categories combined,
confirming that the vast majority of our data came from “normal people” with no particular
social affiliation. Our method did not pull much data from government bots or media re-tweets.
that might be considered microresistance – taking steps that are illegible to standard algorithms
by design (such as sending text as images) or are easily misidentified, e.g. the use of bots to
create trending hashtags to fool search algorithms. She notes dryly that the most common
outcomes studied via commissioned papers – sharing links and retweeting – are chosen for
scientific study because of potential commercial applications for corporate entities, not because
results speak to social theory in a meaningful way.
52
Lotan et. al. (2011).
24
The majority of the tweets in the hate speech subsample, 69.71%, were words flagged
with the terrorism dictionary – despite the fact that the selection dictionary was much shorter
than the fascism dictionary. In every age group, except 0-20, counter-hegemonic language
noticeably outweighed hegemonic.53 Tweets in English were rare but virtually guaranteed to be
counter-hegemonic (pro-West). Only 1 English tweet in our sample was pro-Kremlin, compared
to 9.58% of Ukrainian-language tweets and nearly 37.09% of Russian-language tweets. The
inference that we draw from these trends is that very young Russian-speaking Ukrainians were
the most credulous consumers of the Kremlin’s version of events.
[TABLES 3 AND 4 ABOUT HERE]
The three groups that tweet the most are web newscasters, activist organizations, and
unidentified accounts. Figure 3 displays tweet production as it varies across four dimensions:
language, profession, age, and sentiment. To ease interpretation, we separate the figure into two
parts, one that varies by profession and the other by age. The majority of the tweets are counterhegemonic and support the current government of Ukraine – but certain profession groups, when
tweeting in Russian, were systematically more likely to voice opinions consistent with the
hegemonic narrative. Almost half of known and unknown individuals in our sample used words
from the fascism dictionary; a majority of tweets from journalists (89.27%, 333/373) and
bloggers (58.02%, 152/262) used these words as well. Our interpretation is is that both
professional and amateur journalists were drawn into a “war of position” – reproducing the
phrases of the enemy in order to refute and denature their standard meanings.
53
Support among those older than 60 splits evenly, but there are only a handful of these tweets.
25
[FIGURE 3 ABOUT HERE]
[Caption: Most, but not all, tweets in our sample were in Russian. A tweet originating in
the territory of Ukraine in English or Ukrainian that also contained a keyword was almost
guaranteed to be identified using the pro-West (hegemonic narrative) keyword dictionary.]
[TABLE 5 ABOUT HERE]
We also validated that the language of terrorism and fascism identify counter-hegemonic
and hegemonic discourse. This required manually reading a large sample (2,989) of tweets.
85.69% of the time when people used a word from the terrorism dictionary, it was part of the
counter-hegemonic narrative. Occasionally the dictionary captured “false-positive” tweets (for
example, in which a user was communicating ironically, or describing counterinsurgency tactics
used by the Ukrainian state in the East as terrorism), but this was rare. The fascism dictionary
had a true positive rate of 96.67%, meaning 96.67% of tweets it identified as being hegemonic
were in fact hegemonic. The largest source of noise arose from pro-Kyiv Ukrainians describing
Putin as a fascist, expressions of outrage at tactics by police at Maidan, and descriptions of the
Pravii Sektor political party. Table 5 reports these results.54
4. Hypotheses Testing
54
Note that our algorithm over-predicts fascism tweets. While a more precise classifier would
be desirable, the bias is in a direction that would make it harder to find counter-hegemonic
discourse. That we find support for H2 even in the presence of a classifier that over-identifies
hegemonic discourse is reassuring.
26
We present our results in three stages, each of which is meant to demonstrate an
advantage of our method. First, we show trends in hegemonic and counter-hegemonic
production over time, pooled across the entire territory of Ukraine. Second, since both of the
hypotheses sketched above lend themselves to spatial analysis, we map the geographic variation
in hegemonic and counter-hegemonic social media posts across the Ukrainian polity. Third, we
display changes in these trends broken down by time-period across Ukrainian oblasts.
Figure 4 shows changes in the number of tweets per day, by dictionary, between January
1, 2014 and July 31, 2014. We break the conflict into periods in order to see how online
behaviors respond to focal events (shown as dotted lines in Figure 4): the first reported death in
Maidan square, the unexpected flight of Viktor Yanukovych, Russia’s seizure of Crimea, the
referendum by secessionists in Ukraine’s east to form independent or autonomous “People’s
Republics,” and the downing of Malaysian Airlines Flight 17.55 The greatest density of
hegemonic tweets occurred between February 20 and the beginning of April 2014. During this
time, the Russian military seized Crimea, the specter of war in the East became a reality, and the
Russian state media was promulgating the fascist narrative 24 hours a day. The counterhegemonic narrative did not emerge until after the fascist language peaked in intensity.
[FIGURE 4 ABOUT HERE]
[In order to give a sense of how often the keywords in our sample appear as “background
noise” in the course of normal conversation, we show daily count variables for tweets
containing one of our dictionary words all the way back to November 1, 2013, before the
Maidan events escalated. The use of language in our hegemonic narrative (pro-Russia)
dictionary takes off after the flight of Yanukouvich; the counter-hegemonic narrative is
slower to emerge but ultimately dominates on most days starting in mid-April.]
55
The daily coded territorial microdata in Zukhov (2016) confirms that the referendum was a
critical juncture in territorial consolidation and in the conventionalization of the war in the East.
27
Figure 5 compares the overall trends in terrorism and fascism tweets across Ukraine.
Each tweet is mapped to one of 26 geographic areas: one of Ukraine’s 24 uncontested oblasts,
the city of Kyiv, or Crimea. We employ a straightforward methodology to correct for the
underlying variation in tweet density across Ukraine: For each period, we first calculate the
percentage of all Russian-language geotagged tweets that originated from within each oblast,
then repeat that calculation for the smaller population of tweets that contained either a fascist or
terrorist Russian-language keyword. We thus know the percent of all tweets in Russian from
each oblast (Ai), the percent of all hegemonic tweets in Russian from each oblast (Bi), and the
percent of all counter-hegemonic tweets from each oblast (Ci). Next, we measure the percent
difference between Bi and Ai, Ci and Ai. This quantity measures the over- or under-production
of hegemonic (Bi) or counter-hegemonic (Ci) tweets, based on how many Russian tweets each
oblast produces that do not engage in those narratives.56 The result is a basic comparative
measure of which oblasts are responsible for over- and under-producing hegemonic or counterhegemonic content. To visualize, we bin the results: whether the oblast produced more than 50%
fewer tweets than expected, 0-50% fewer, no change, 0-50% more, 50-100% more, or more than
double the number of tweets expected. Darker colors indicate a relative surge in tweets
containing target keywords relative to overall Twitter traffic in the district. For example, in the
period after President Yanukouvich fled but before Crimea seceded, Crimea produced 2.3% of
all tweets, but it produced 3.93% of all tweets reproducing the hegemonic discourse. It therefore
produced 70.87% (3.93-2.30)/(2.30) more tweets than expected.
56
The results are robust to an alternative model specification in which the denominator is the
percentage of all geotagged tweets that originated from within each oblast regardless of
language. Contact authors for more detail.
28
[FIGURE 5a & 5b ABOUT HERE]
[Caption: These maps show an oblast’s relative level of production of politicized “hate
speech” tweets as a percentage of all Russian-language tweets, pooled across all time
periods. The hegemonic narrative is stronger in the East, especially the contested
militarized frontlines of the Donbas region. This is consistent with Hypothesis 1. The
counter-hegemonic narrative is most prevalent in the Donbas Region and the central
districts of Cherkasy, Mykolayiv, and especially Kirovohrand – also production centers of
hegemonic narrative keywords on Twitter. This is suggestive evidence for Hypothesis 2.]
Figure 5a provides clear support for Hypothesis 1. The reproduction of the pro-Kremlin
narrative on Twitter was most common among Russian-speakers residing in Ukraine’s east, most
pronounced in the war zones of the Donbas.57 Comparing Figures 5a and 5b also suggests
support for Hypothesis 2, at least in terms of rough spatial correlations, though it is necessary be
to disaggregate the data by time period to observe whether the counter-hegemonic narrative
“follows” the hegemonic narrative.
Figure 6 is an alternative visualization of the same data, with Ukrainian oblasts arrayed
roughly from West to East along the x-axis (eastern-most oblasts are on the right). Three oblasts
– Donets’k, Luhans’k, and Kirovohrand – are the centers of online political activism. These
three oblasts have the highest overall percentage of both hegemonic and counter-hegemonic
content, with the counter-hegemonic content overwhelming the hegemonic by density in all
three. There were nine oblasts in which the difference from the baseline was positively signed,
and in seven of these districts, the sign was significant for both hegemonic and counterhegemonic trends. Taken together, this is extremely strong additional support for Hypothesis 2:
contestation between hegemonic and counter-hegemonic discourses. For two districts in the
57
Survey data suggests pro-Russia sentiment in Crimea was high in March 2014. Our findings
also square with the thrust of ground-truth surveys conducted on the geo-spatial variation in
respondent beliefs, such as those conducted by the Kiev International Institute of Sociology
(2014) or the International Republican Institute (2014a and 2014b).
29
Donbas region, online conflicts are obviously a reflection of off-line conflict (e.g., the
conventionalization of the war around stable front lines, with patriots promulgating their
viewpoints in a competitive fashion). Our favored ex-post interpretation of the other eyecatching outlier, Kirovohrand, is that virtually all of the Russian-language Twitter activity was
politicized and competitive political speech, making for an unusually small denominator, but
there is nothing about Kirovohrand that would have allowed us to predict ex-ante that it would be
the oblast in which a social-media “war of position” would occur. Only in one oblast, Cherkasy,
could it be plausibly argued that the hegemonic narrative “drowned out” the counter-hegemonic.
[FIGURE 6 ABOUT HERE]
[Caption: Bar charts show show an oblast’s relative level of production of politicized “hate
speech” tweets as a percentage of all Russian-language tweets, pooled across all time
periods. The dominance of production from Donbas region Kirovohrand is clear.]
Figure 7 provides a much more granular picture of the time-space trends in Ukrainian political
discourse. It breaks the same data down into four time periods: Crimea (dated from the
unexpected flight of Viktor Yanukouvich on 2/22), Donetsk People’s Republic (dated from 3/16
the referendum on secession from Ukraine in Donetsk), the Donbas War (dated from 4/8, up
until the election of Petro Poroshenko) and Poroshenko’s War (dated from 5/27 until the
shooting down of Malaysian Airlines Flight 17).
There are many trends that are not explained well by either of our hypotheses, such as
the puzzling social media behaviors within Transcarpathia, in Ukraine’s far West, which
emerged as the dominant center of pro-Russian hegemonic social media production in the
anarchic aftermath of Yanukouvich’s flight. Other trends are intuitive. Crimea had a substantial
increase in discourse about fascism and terrorism immediately after President Yanukouvich fled,
30
suggesting an explosion of highly politicized rhetoric. But note also, during the same period, the
neighboring district of Zaporizhzhya – north of the Sea of Azov, and, quite suddenly,
sandwiched inconveniently in the obvious land-bridge between Russian territory and the Crimea
peninsula – saw an explosion of the language of terrorism. We were surprised to see such a
strong counter-hegemonic presence in Crimea in the second period – suggesting that many
Russian-speaking Crimeans “behind the lines” were critical consumers of media during this time.
Space constraints prohibit a full exposition of trends, which would be speculative in any case.
We will only note that the fact that 43% of the time, if the difference from the baseline was
positively signed based on either the hegemonic or counter-hegemonic keyword dictionary, it
was also positively signed for the other dictionary. This is consistent with Hypothesis 2.
[FIGURE 7 ARRAYS ABOUT HERE]
[Caption: : Bar charts show show an oblast’s relative level of production of politicized
“hate speech” tweets as a percentage of all Russian-language tweets, broken down into
sequential time periods. It is much easier to observe the spatial co-location of hegemonic
and counter-hegemonic narrative production when the data is analyzed a few weeks at a
time, rather than pooling across the entire sample.
5. Discussion
Interstate borders are not often revised. Hegemonic ideas can disappear quickly in
principle (via processes such as information cascades, paradigm shifts, or outright experimental
falsification), but, in practice, hegemonic ideas more often fade very slowly. Empirical
measurement of key concepts in the study of ideological hegemony has stalled empirical
31
research on the subject as well. Behaviors are only an indirect window into ideas.58 Even
without institutional failure, it is difficult to design tests that convincingly probe the edges of
hegemonic idea structures. A slight misstep and the researcher will be “asking silly questions,”
squandering the goodwill of subjects and generating meaningless data.59 If a deep sensitivity to
local context is required to coax data out of subjects, the external validity of even the most wellconducted participant observational study is questionable.
This paper demonstrates that social media data provides new opportunities to probe the
causal effects of hegemony while side-stepping these salient concerns. Since content generated
on smartphones is costless to produce and easy to spread, it is now possible, even during periods
of acute state failure, to make inferences from the prevalence (or relative absence) of keywords
in large samples of user-generated messages over time and space. As proof-of-concept, we
examine a large sample of Twitter data from Russian-language users of Twitter living Ukraine in
the winter, spring, and summer of 2014 – a time in which the ruling party had imploded and
many believed that the polity was teetering on the brink of total institutional failure. 60 Ukraine is
a practically custom laboratory to critically examine the claims of ideological hegemony. The
58
Product consumption patterns are used to evaluate the efficacy of advertising campaigns,
voting and survey behaviors are used to infer political beliefs, church attendance proxies for
religious beliefs – but peaceful institutional failure, of the sort we saw in Ukraine, disrupts daily
bureaucratic rituals, normal politics, and economic transactions. It is difficult to make confident
inferences about regime legitimacy, for instance, by examining patterns of voting (or non-voting)
in a special election that is held months after a highly-irregular regime change.
59
Though ethnographic observation has been agreed upon as the “best practices” solution to this
problem, limitations of ethnography as a method relate primarily to concerns of replicability and
standpoint epistemology. A second-best empirical strategy, employed by Lustick (1993), is to
observe the content of speeches and publications by elites as they wage “wars of position” and
“wars of movement,” though the texts that survive the march of history is limited by compound
archival biases (see Lustick (1996)).
60
It should also be emphasized again that, for our purposes, whether or not the actors succeed in
their effort to shape the future of their country's politics is not relevant to our analysis. It is not
even relevant to us if it is realistic for someone to believe that their actions are going to change
minds.
32
existential geopolitical stakes, combined with the well-understood “East-West” division of the
Ukrainian polity, provide clear theoretical priors on the relative influence of Russian hegemony.
The dramatic disintegration of the political coalition that had been elected to rule Ukraine 2014,
the high levels of resultant polarization among Ukrainian Russian-speakers, and the presence of a
clear set of narrative focal points – code words – that extremists on both sides were using to
identify each other vastly simplifies the measurement and interpretation of trends. Since most
Russian-speakers in Ukraine get their news from television, and the Putin administration has
used Russian-language television broadcasts as a functional arm of the state, we can also assuage
fears that what is being observed is top-down hegemonic processes, not the kind of strategic selfselection into ideological “Fox vs. MSNBC media bubbles” observed in the United States.61
Our inference strategy depends upon three assumptions. First, it must be the case that
individuals in our sample do not maintain a “performance identity” on social media that
promulgates information that is contrary to their true beliefs. We find this intuitively plausible
but anticipate far more research on this subject in the future. Second, it must be the case that
Twitter-users, and more specifically Twitter-users who are using the geotagged accounts that
comprise our data, are not systematically different from non-Twitter users in a way that would
bias our findings. This claim is somewhat more problematic. It may be the case that the beliefs
of the users of accounts that did not disable geotagging on their Twitter accounts were
systematically different than those who did; for instance, because they lacked strategic
sophistication about the ability of commercial and state actors to track them. Third, it must be
the case that the bulk of the traffic on Twitter is produced by the indigenous population of an
oblast, not by individuals that have traveled from a great distance in order to document that they
61
This point has been argued in various ways by Triesman (2011), 595; Pomerantsev (2014), 5;
Dawisha (2014), 274, 276-80, 336-9.
33
were “a part in the story” (in the case of journalists) or to create a false impression of indigenous
social support (in the case of paid Russian agents engaging in clandestine military operations).
There are broader scope limitations on our ability to make inferences from social media
data. An ongoing concern is bias. Social media penetration, like the penetration of other kinds
of information technology, tends to skew young and correlates positively with income per
person. Within a country, rich urban regions have higher penetration than poor rural ones.62 Our
paper demonstrates that spatial and time trends are legible to analysis, so long as there is
ideological diversity within the sample. We did not expect to find support for pro-Russian
discourse to be as concentrated among the very young as it turned out to be, and we doubt that
this important social trend would have emerged via deduction alone. Given the polling results
from the traditional surveys that have been conducted in Ukraine, we are cautiously confident
that the spatial trends that we identify in Ukraine’s Twitter discourse are a mirror of the beliefs
that occur in the real world.
Social media analysis and survey polling vary in important ways and are not perfect
substitutes. The advantages of traditional surveys are well understood. We do not anticipate that
social media data will replace surveys (and do not think that it ought to). But this paper
demonstrates some of the new capabilities that are just on the horizon for researchers interested
in mapping the variation in public opinion in settings where surveys are infeasible. Social media
62
This pattern does not mean that Twitter cannot provide representative samples for countries or
regions with low penetration. Instead, there is probably a threshold effect in which, once a
country has a high enough percentage of Twitter users, additional users decrease standard errors
but do not affect bias. Careful research to find and define this threshold is an important frontier
for research on social media and political behavior. A deeper kind of potential bias is that social
media users in the study location may not represent a diversity of opinion. Having a Facebook or
Twitter account in Egypt during the Arab Spring was practically synonymous with opposition to
President Hosni Mubarak’s regime according to (Gunning and Baron 2013); a snap-poll of
Facebook or Twitter users in Kyiv around the Maidan events would likely have yielded a
similarly skewed anti-regime picture.
34
data is quite cheap when compared to traditional surveys, opening the possibility of highresolution longitudinal studies on difficult-to-reach populations.63
Prior to the events of 2014, it was often stated, as a stylized fact, that Russia was the
hegemonic power in the Russki-Mir, including Eastern Ukraine. This study documents a lowcost kind of spontaneous counter-hegemonic response from Russian-speaking Ukrainians: telling
a different story than that which they were expected to tell. Heterogeneity in the belief structure
of the Russian-speaking population of Ukraine was reflected in silence – not re-tweeting
Russian-language media keywords – and by participating in the creation of a counter-hegemonic
narrative. The individuals that used social media platforms to publicly endorse the premises of
one narrative or another are were randomly distributed across the territory of Ukraine. A
reasonable interpretation of these data is that decentralized Ukrainian social actors – bloggers,
academics, and private citizens with cell phones – acting in the absence of state institutional
backing, demonstrated that the Russian state does not have an ability to generate automatic
compliance based on its comparative advantage in cultural production among Russian speakers.
63
Social media polling budgets could easily become expensive, depending on the scale and
ambition of the research. But the scaling arguments still apply: Though the representativeness of
the social media sample may be questioned, increasing “n” of the study would almost certainly
orders of magnitude more if attempted with pencil-and-paper surveys.
35
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Table 1: Narrative Comparison
Hegemonic Narrative,
Spring 2014
The appropriate Russia-Ukraine
relationship, taking the relevant
historical facts into account, ought
to be one of …
Counter-Hegemonic
Narrative, Spring
2014
...natural hierarchy. Ukraine ...diplomacy between
does not
sovereign equals.
actually enjoy real
Ukrainian
Westphalian sovereignty.
independence is a
Borders are gifts from Soviet reified social fact, and
times. Kyiv is part of “old
Ukraine has rights
Rus.”
under international law.
Future historians, writing about
the Maidan events, will describe
them as...
...a coup by far-right social
forces, emboldened by
material and moral support
of the NATO alliance and
Western intelligence
agencies.
Future historians, writing about
Putin’s responses to the Maidan
events and their aftermath—
including the seizure of Crimea —
will describe them as...
The proximate cause of the
violence in East Ukraine is...
…heroic.
Any account of the violence in
East Ukraine is incomplete if it
does not reference deeper
structural causes, such as…
…decades of Western
policies to encircle Russia,
expanding NATO and
aggresively pushing regime
change in post-Soviet states
under the aegis of
“democracy promotion.”
…Russian patriots.
Soldiers fighting to secede from
Eastern Ukraine are best
described as...
Keyword for narrative track:
...the coup. Secessionist
parts of the Donbas Region
are Party of Regions
strongholds that have “opted
out” after their votes were
invalidated by street politics.
fascist.
...a broad-based social
movement against an
illegitimate
government, with
limited, basically
analogous to 1989 or
decolonization.
…criminal.
...Putin’s illegal seizure
of the Crimean
peninsula, leading some
in Ukraine’s east
calculated that if they
organized militias,
Russia might assist
them too.
… the basic
incompatibility of
values between Putin’s
regime and the EU
…terrorist insurgents.
terrorist.
43
Table 2: Keywords Dictionary
Terrorism (English)
Terrorism (Russian Variants)
Terrorist
Terrorists
Terrorism
tеррорист, терорист
террористы, терористи
терроризм, тероризм
Fascism (English)
Fascism (Russian Variants)
Radicals
Right-Wing Radicals
Nationalist Radicals
Right-Wing Extremists
Right Terrorism
Extremists, Extremism
Neo-Nazism
Nazis, Nazism
Nationalist, Nationalism
Nationalist-Radicals
National Minority
Ultra-nationalism
Fascism, Fascists
Mercenaries, Fighters
Anti-Semitism
Russophobe
радикальные, pадикалы
праворадикальные
национал-радикальный
правоэкстремистская
правый терроризм
экстремистский, экстремизм
неонацизм, неонацистский,
нацисты, нацизм, нацистская
националист, националистическое
национал-радикальный
нацменьшинства
ультранационалистические
фашизм, фашистский
наемники, боевики
антисемиты
русофобы
44
Table 3: Tweets by Identity
News
Web
News
Other
Org.
Journ.
Blogger
Activist
Org.
Activist
Indiv.
Gov.
Org.
Gov.
Indiv.
Celeb.
Academic
Bot
Other
Org.
Other
Indiv.
Unknown
6
18
14
1115
8
15
60
373
80
262
7
115
151
322
2
4
7
12
6
11
13
94
23
43
12
15
1166
2724
37
772
Indivs.
Tweets
1592
5909
Table 4: Tweets by Language, Age Group
English
Russian
Ukrainian
Total
Hegemonic
1
1723
66
1790
0-20
21-30
31-40
41-50
51-60
61+
Unknown
Total
334
547
172
89
32
5
611
1790
By Language
Counter-Hegemonic
574
2922
623
4119
By Age
337
823
562
200
108
4
2085
4119
Total
575
4645
689
5909
671
1370
734
289
140
9
2696
5909
45
Table 5: Verification of Coding
Human Codes as Fascism
Computer Predicts Fascism
Computer Predicts Not
Fascism
Computer Predicts Terrorism
Computer Predicts Not
Terrorism
96.67%
3.42%
Human Codes as Not
Fascism
39.89%
60.11%
Human Codes as
Terrorism
85.69%
14.31%
Human Codes as Not
Terrorism
32.33%
67.66%
46
Word count EXCLUDING Authors' Names (attached document
must not include identifying information).
Structure, Agency, Hegemony, and Action: Ukrainian Nationalism in East Ukraine
Word Count: 13, 381
Figure EXCLUDING Authors' Names (attached document must not include identifying information).
Figure 1: Twitter Accounts By Language Over Time
0.9
0.8
0.7
0.6
0.5
0.4
Percent of Tweets in Russian
2013−11−01
0.4
Malaysia Airlines
Poroshenko
Poroshenko
0.5
Donestk PR
Donestk PR
0.6
Crimea Secession
Political Tweets
7−10
7−31
6−12
5−15
4−17
Malaysia Airlines
3−20
2014−07−10
2014−07−31
All Tweets
Yanukovych Flees
2014−06−12
Crimea Secession
2−20
2014−05−15
Sample
Protest Death
2014−04−17
0.7
1−23
2014−03−20
Yanukovych Flees
2−26
2014−02−20
1−28
2014−01−23
0.8
Protest Death
1−01
2013−12−26
0.9
Percent of Tweets in Russian
2013−11−28
Figure 2: Wordclouds By Dictionary
Figure 3: Sample Characteristics
3a. Occupation and Language
3b. Age and Language
2014−06−26
2014−06−12
2014−05−29
Crimea
2014−05−15
2014−05−01
2014−04−17
Protests
2014−04−03
2014−03−20
Pre
−Protest
2014−03−06
2014−02−20
2014−02−06
2014−01−23
2014−01−09
2013−12−26
2013−12−12
200
2013−11−28
2013−11−14
2013−01−01
Total Tweets
Figure 4: Hegemonic vs. Counter-Hegemonic Trends Over Time
Civil War
Topic
Pro−Russia
Pro−West
150
100
50
0
Figure 5a (Hegemonic Narrative, Jan 1-July 31, 2014)
Volyn
Rivne
Chernihiv
Kyiv
City
Sumy
Zhytomyr
L’viv
Kyiv
Kharkiv
Ternopil
Poltava
Khmelnytsky
Ivano-Frankivs’k
Luhansk
Cherkasy
Vinnytsya
Transcarpathia
Kirovohrad
Dnipropetrovs’k
Chemivtsi
Donetsk
Mykolayiv
Zaporizhya
Kherson
Odessa
Crimea
Figure 5b (Counter-Hegemonic Narrative, Jan 1-July 31, 2014)
Volyn
Rivne
Chernihiv
Kyiv
City
Sumy
Zhytomyr
L’viv
Kyiv
Kharkiv
Ternopil
Poltava
Khmelnytsky
Ivano-Frankivs’k
Transcarpathia
Luhansk
Cherkasy
Vinnytsya
Kirovohrad
Chemivtsi
Dnipropetrovs’k
Donetsk
Mykolayiv
Zaporizhya
Kherson
Odessa
Crimea
Transcarpathia
L'viv
Volyn
Chernivtsi
Ivano−Frankivs'k
Rivne
Khmel'nyts'kyy
Zhytomyr
Vinnytsya
Odessa
Mykolayiv
Kirovohrad
Cherkasy
Kiev
Kiev City
Chernihiv
Sumy
Poltava
Dnipropetrovs'k
Kherson
Crimea
Zaporizhzhya
Kharkiv
Donets'k
Luhans'k
Difference vs. Baseline
Figure 6
Discourse
Counter−Hegemonic
Hegemonic
750%
500%
250%
0%
300%
Discourse
Counter−Hegemonic
Hegemonic
600%
400%
200%
0%
Transcarpathia
L'viv
Volyn
Chernivtsi
Ivano−Frankivs'k
Rivne
Khmel'nyts'kyy
Zhytomyr
Vinnytsya
Odessa
Mykolayiv
Kirovohrad
Cherkasy
Kiev
Kiev City
Chernihiv
Sumy
Poltava
Dnipropetrovs'k
Kherson
Crimea
Zaporizhzhya
Kharkiv
Donets'k
Luhans'k
Transcarpathia
L'viv
Volyn
Chernivtsi
Ivano−Frankivs'k
Rivne
Khmel'nyts'kyy
Zhytomyr
Vinnytsya
Odessa
Mykolayiv
Kirovohrad
Cherkasy
Kiev
Kiev City
Chernihiv
Sumy
Poltava
Dnipropetrovs'k
Kherson
Crimea
Zaporizhzhya
Kharkiv
Donets'k
Luhans'k
Difference vs. Baseline
Apr 8May 26
200%
200%
100%
May 27July 17
Difference vs. Baseline
Hegemonic
250%
Difference vs. Baseline
DonetskPR
Counter−Hegemonic
Donbas War
0%
Poroshenko’s
War 750%
Transcarpathia
L'viv
Volyn
Chernivtsi
Ivano−Frankivs'k
Rivne
Khmel'nyts'kyy
Zhytomyr
Vinnytsya
Odessa
Mykolayiv
Kirovohrad
Cherkasy
Kiev
Kiev City
Chernihiv
Sumy
Poltava
Dnipropetrovs'k
Kherson
Crimea
Zaporizhzhya
Kharkiv
Donets'k
Luhans'k
Mar 16Apr 7
Difference vs. Baseline
Feb 22Mar 15
400%
Transcarpathia
L'viv
Volyn
Chernivtsi
Ivano−Frankivs'k
Rivne
Khmel'nyts'kyy
Zhytomyr
Vinnytsya
Odessa
Mykolayiv
Kirovohrad
Cherkasy
Kiev
Kiev City
Chernihiv
Sumy
Poltava
Dnipropetrovs'k
Kherson
Crimea
Zaporizhzhya
Kharkiv
Donets'k
Luhans'k
Figure 7
Crimea
Discourse
Discourse
Counter−Hegemonic
Hegemonic
400%
0%
−100%
Discourse
Counter−Hegemonic
Hegemonic
500%
0%