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Measuring news bias: Russia’s official news agency ITAR-TASS’ coverage of the Ukraine crisis

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Objectivity in news reporting is one of the most widely discussed topics in journalism, and a number of studies on bias in news have been conducted, but there is little agreement on how to define or measure news bias. Aiming to settle the theoretical and methodological disagreement, the author redefined news bias and applied a new methodology to detect the Russian government’s influence on ITAR-TASS during the Ukraine crisis. A longitudinal content analysis of over 35,000 English-language newswires on the Ukraine crisis published by ITAR-TASS and Interfax clearly showed that ITAR-TASS’ framing of Ukraine was reflecting desirability of pivotal events in the crisis to the Russian government. This result reveals Russia’s strategic use of the state-owned news agency for international propaganda in its ‘hybrid war’, demonstrating the effectiveness of the new approach to news bias.
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DOI: 10.1177/0267323117695735
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Measuring news bias: Russia’s
official news agency
ITAR-TASS’ coverage of
the Ukraine crisis
Kohei Watanabe
The London School of Economics and Political Science (LSE), UK
Abstract
Objectivity in news reporting is one of the most widely discussed topics in journalism, and a
number of studies on bias in news have been conducted, but there is little agreement on how to
define or measure news bias. Aiming to settle the theoretical and methodological disagreement,
the author redefined news bias and applied a new methodology to detect the Russian government’s
influence on ITAR-TASS during the Ukraine crisis. A longitudinal content analysis of over 35,000
English-language newswires on the Ukraine crisis published by ITAR-TASS and Interfax clearly
showed that ITAR-TASS’ framing of Ukraine was reflecting desirability of pivotal events in the
crisis to the Russian government. This result reveals Russia’s strategic use of the state-owned
news agency for international propaganda in its ‘hybrid war’, demonstrating the effectiveness of
the new approach to news bias.
Keywords
Computerized-content analysis, news bias, propaganda, Russia, Ukraine crisis
There is almost unanimous agreement on the importance of independent journalism
among scholars of mass communication, and objectivity in news reporting is one of the
most widely discussed topics in journalism (Barkho, 2013b; Donsbach and Klett, 1993;
Maras, 2012). The independence of journalists is a precondition for objective news
reporting (Barkho, 2013a). Importantly, journalistic independence provides objective, or
unbiased, political information allowing for effective democracy, constrains the power of
the mass media and maintains the trust of the public in mass media (Maras, 2012).
Corresponding author:
Kohei Watanabe, The London School of Economics and Political Science (LSE), Houghton Street, London
WC2A 2AE, UK.
Email: K.Watanabe1@lse.ac.uk
695735EJC0010.1177/0267323117695735European Journal of Communication 00(0)Watanabe
research-article2017
Full Length Article
2 European Journal of Communication 00(0)
Furthermore, biased news reporting leads to the marginalization of certain social groups,
misperceptions of political agendas and public disenchantment and cynicism
(Brandenburg, 2005). Researchers have embarked on empirical studies of bias in news
on elections (Brandenburg, 2005; Hopmann et al., 2011; Kahn and Kenney, 2002;
Robinson and Sheehan, 1983), wars (Aday, 2010; Aday et al., 2005; Dickson, 1994;
Entman and Page, 1994; Pfau et al., 2004) and foreign countries (Chaudhary, 2001;
Jones, 2008; Meyer, 1989; Miller, 2007), but there is little agreement on how to define or
measure news bias.
In the empirical studies, one school of thought defines the lack of objectivity in
news as unbalanced coverage of different subjects (Brandenburg, 2005; Cushion et al.,
2009; D’Alessio and Allen, 2000; Dominick, 1977; Hopmann et al., 2012). Within this
conception of news bias, researchers focus on the sheer number of articles and the
length of airtime allocated to certain issues, events or actors. Other groups of research-
ers pay attention to tones of news reports, using metrics such as ‘positive-negative’
(Aday, 2010; Brandenburg, 2005; Hopmann et al., 2012; Pfau et al., 2004; Robinson
and Sheehan, 1983), ‘favourable-unfavourable’ (e.g. Hofstetter, 1976) or ‘supportive-
critical’ (Aday et al., 2005; Entman and Page, 1994; Kleinnijenhuis et al., 2007). In this
approach, news reporting with predominantly positive or negative tones is considered
to be biased.
The definition of news bias must be operationalizable in empirical inquiries, but it
should also be based on the theories of media effect. Agenda-setting theory suggests
that the amount of news coverage allocated to certain issues, events or actors influ-
ences their perceived importance among audiences (‘what to think about’) (Besova
and Cooley, 2009; Hester and Gibson, 2003; McCombs et al., 1997; Salwen and
Matera, 1992; Wanta et al., 2004), but if our primary interest is investigating the mass
media’s role in shaping news audiences’ attitudes towards subjects (‘how to think’), we
must scrutinize the ways those subjects are represented in news reporting. According
to the theory of second-level agenda-setting, or priming, news reporting focusing on
negative or positive aspects of events, issues and actors has a significant impact on an
audience’s attitude towards them (Entman, 1993; Hester and Gibson, 2003; Iyengar
and Simon, 1993; McCombs et al., 1997). The concept of media framing, which is
defined as ‘selecting and highlighting some faces of events or issues, and making con-
nections among them so as to promote a particular interpretation, evaluation and/or
solution’ (Entman, 2004: 5), also establishes a link between news reporting and peo-
ple’s understanding of public affairs.
Selective media frames manifest as unbalanced tones of news stories, which become
either positive or negative when they concern events, favourable or unfavourable when
they concern opinions, or supportive or critical when they concern policy options.
However, not all news stories with a predominantly positive or negative tone can be
considered biased because tone can be a simple reflection of objective reality, that is,
tones of news reports will be profoundly negative when stories describe inherently nega-
tive events, such as natural disasters, armed conflicts and social disruptions, as Stevenson
(1984) correctly points out in negative representation of the under-developed countries
in foreign news. Also, tones become overwhelmingly supportive of the status quo when
disagreement among political elites is absent (e.g. the aftermath of the 9/11 attacks), as
Watanabe 3
Bennett’s (1990) index theory suggests. The reflection of objective reality in the tones of
news reporting poses methodological challenges in measuring news bias. Entman (2007),
who is agnostic regarding objective reality, has even proposed an approach to news bias
focusing only on the balanced coverage of different aspects of events, issues or groups.1
This methodological challenge has constrained how news bias has been defined and
measured in earlier empirical studies. The adoption of concepts such as ‘balance’ and
‘fairness’ as proxies to objectivity has been a common practice among researchers, as
well as regulators, because of the difficulty in measuring objectivity itself (Maras, 2012).
The Fairness Doctrine of the Federal Communication Commission, which required
American broadcasters to produce ‘balanced’ news reporting on public agendas between
1949 and 1987, has strongly affected the concept of news bias in scholarly debates, but
‘balance’ in news reporting is not so obvious in countries where the political landscape
is more complex and the simple 50-50 benchmark derived from the US two-party system
does not hold (Hopmann et al., 2012). Some researchers of European media have resorted
to benchmarks constructed based on the number of seats political parties hold in legisla-
tures (Brandenburg, 2005), but it seems unrealistic to expect equal coverage of political
groups in polarized media systems where partisan journalism is the norm. As a result of
this, empirical studies on news bias have concentrated in the United States.
Aiming to facilitate empirical studies on news bias in complex media systems, I pre-
sent a new approach to measuring news bias, taking Russia’s official news agency ITAR-
TASS’ English-language news coverage of the Ukraine crisis as an example. My case
selection was motivated not only by the significance of the crisis in Europe to interna-
tional politics but also by the severity of the above-mentioned methodological chal-
lenges; in this case, the challenges were made particularly severe by a rapidly changing
situation on the ground and a lack of non-media benchmarks with which to assess bal-
ance in the news coverage. In my approach to news bias, I will conceptualize objectivity
in news reporting as coverage of all possible newsworthy stories, and analyse ITAR-
TASS’ news coverage in relation to Interfax’s broader news coverage. In this setting,
Interfax serves as a benchmark unit, which helps us to measure bias in ITAR-TASS’
news reporting caused by the Russian government’s influence excluding the effects of
the inherently positive or negative nature of the events on the ground. I estimated the
amount of bias in ITAR-TASS’ news reporting using longitudinal data, which I produced
by content analysing all the news stories on Ukraine published by the two news agencies
over a 16-month period starting from January 2013.
My statistical analysis of the longitudinal data will clearly show that ITAR-TASS’
framing of democracy and sovereignty in Ukraine is systematically biased during the
crisis corresponding to the desirability of the situation in Ukraine for the Russian
regime. The main causes of bias were (1) highly critical comments made by Russian
officials on Ukraine, which the news agency quotes very frequently, and (2) profoundly
negative descriptions of events related to Ukraine by the news agency. However, ITAR-
TASS’ news articles tend to present the Russian government’s views on Ukraine in an
‘objective’ style of writing, blurring the distinction between opinions and facts. The
systematic bias in ITAR-TASS’ news coverage of the Ukraine suggests the importance
of ITAR-TASS in Russia’s ‘hybrid wars’, which utilizes non-military means to achieve
military goals.
4 European Journal of Communication 00(0)
Hypotheses
ITAR-TASS is a prominent example of a state-owned news agency. Its roots can be
traced back to the imperial era, when the first Russian news agency, the Russian Telegraph
Agency (RTA), was created by the tsar in 1866. The operation of the first news agency
was limited to domestic clients, but a more international agency, the St. Petersburg
Telegraph Agency, was established by the government in 1904 to overcome Russia’s
dependence on the German news agency, Wolf, for the international distribution of news.
After the 1917 October Bolshevik revolution, newspapers and magazines were obliged
to publish information received from a new central news agency, ROSTA, which inte-
grated all national and regional information agencies, and later became the Telegraph
Agency of the Soviet Union, known as TASS. This news agency was directly controlled
by the state and often used for propaganda during the Soviet era. According to Vartanova
and Frolova (2010), ‘TASS was different from other international agencies in that it
acted as a voice of the Soviet government which tended to speak to the peoples of the
world through its official spokesmen’ (p. 264). TASS survived the collapse of the Soviet
Union and was subsequently renamed ITAR-TASS.2 Today, it is the official news agency
of the Russian Federation and owned and administered by the government, enjoying
exclusive access to official information.
The influence of the Russian government as the owner of the news agency alone
might have caused bias in its news reporting of the Ukraine crisis, in which Russia has
vested interests, but it is also important to note that the general level of press freedom and
the journalistic culture in Russia are very different from those of Western countries. The
media system of Russia is characterized as Polarized model, in which journalists practise
partisan reporting, commercial news media experience frequent state interventions and
media figures are integrated into the elite political network (Dobek-Ostrowska and De
Smaele, 2010; Vartanova, 2011). This limited press freedom and partisan journalism in
Russia is expected to increase the degree to which ITAR-TASS reflects the wishes of
Russia’s political elites, and therefore, I expect to find consistent patterns in the framing
of mediated communication that promote the influence of Russia on Ukraine, which
indicates an existence of bias in ITAR-TASS’ news caused by the Russian government’s
influence.3 In fact, Horvit (2006), in his research on news agencies’ framing of the
debates around the US-led intervention into Iraq in 2003, found that 54% of the ITAR-
TASS stories sourced Russian government officials, and 53% of the paragraphs in its
stories were negative towards US policy. His finding predicts that the ITAR-TASS fram-
ing of the Ukraine crisis will reflect the desirability of pivotal events to the Russian
government, and therefore, I formulate my first two hypotheses:
H1. ITAR-TASS’ framing of the Ukraine crisis will become more positive when the
situation in Ukraine is desirable to the Russian government.
H2. ITAR-TASS’ framing of the Ukraine crisis will become more negative when the
situation in Ukraine is undesirable to the Russian government.
Although the literature details theory largely based on studies of the news coverage of
elections, wars or foreign countries by retail news media (such as newspapers or TV), I
Watanabe 5
adopt this theoretical framework as a starting point, aiming to identify necessary changes
for wholesale news media (news agencies). D’Alessio and Allen (2000) identified three
types of bias in news reporting in their meta-analysis of election studies: ‘coverage bias’,
‘gatekeeping bias’ and ‘statement bias’. According to their definitions, coverage bias
stems from unbalanced amounts of news coverage allocated to particular subjects; gate-
keeping bias is a result of selection or deselection of particular kinds of stories; and state-
ment bias is caused by inclusion of journalists’ opinions. Coverage bias is expected to
increase the salience of a particular country for the international audience as concen-
trated media coverage has an agenda-setting effect; both gatekeeping and statement bias
are likely to cause attitude changes among audiences because the arbitrary selection of
stories and insertion of opinions have a second-level agenda-setting effect.
Considering ITAR-TASS’ status as an official news agency, I expect to find gatekeep-
ing bias caused by the prioritization of Russian official sources in its coverage of the
Ukraine crisis. Therefore, my third hypothesis is as follows:
H3. Bias in ITAR-TASS’ reporting of Ukraine is caused by high representation of
Russian government officials in its stories.
However, it is unlikely to find personal opinions in ITAR-TASS’ news coverage
because it adopts the ‘objective’ style of writing in newswires. Alternatively, I expect to
find ‘corporate bias’, in other words, one driven by the ideological, social and political
orientations of media organizations (Barkho, 2013a). This is as opposed to ‘personal
bias’, which would derive from the educational, religious, economic or racial back-
ground of individual journalists. Therefore, my fourth hypothesis is as follows:
H4. Bias in ITAR-TASS’ reporting of Ukraine is caused by its corporate views on
Ukraine, but not by the personal views of the journalists.
Methodology
In the studies on news coverage of national politics in the United States, unbalanced
volumes or tones of news stories were seen as indications of news bias, but such an
approach is not appropriate in measuring bias in ITAR-TASS’ news reporting of the
Ukraine crisis because (1) there is no ground to expect ITAR-TASS to cover different
sides of the conflict equally (i.e. Russian news agencies more likely to report the Russian
government’s views sympathetically, even without the influence of the Russian govern-
ment, because of their greater access to Russian sources and Russians’ psychological
attachment to the country), and (2) the rapidly changing situation on the ground affects
the tones of news reporting (i.e. a more negative tone in a story might be caused merely
by occurrences of more inherently negative events, such as violence confrontations or
social disruptions, not by it being negatively framed intentionally).
In order to overcome these problems, Interfax, a Russian news agency that is inde-
pendent from the Russian state (Boyd-Barrett, 2014), is included in the analysis as a
benchmark unit. Interfax was founded in a radio station in Moscow independently of the
government in the last days of the Soviet Union. Operating as a commercial enterprise,
6 European Journal of Communication 00(0)
it generates a significant portion of its revenues from its economic news service.
According to earlier studies, 85% of Interfax clients consisted of banks and financial
enterprises, 10% insurance and audit companies and 5% privatized enterprise; it has
developed a wide range of products that include providing electronic financial informa-
tion and analytical reports and has become a leading supplier of information on Russia
and Commonwealth of Independent States (CIS) countries. Thanks to its successful
commercial operation, Interfax maintains a high level of independence from the Russian
government (Boyd-Barrett, 2012; Rantanen and Boyd-Barrett, 2004; Vartanova and
Frolova, 2010).
I can identify bias caused by the Russian government’s influence (‘state-ownership
effect’) while excluding the effect of ITAR-TASS being based in Russia (‘home-country
effect’) by using Interfax as a benchmark unit. This benchmarking also allows us to con-
trol for inherently negative or positive events that affect news content of ITAR-TASS
(‘real-event effect’). In this approach, I focus on changes in ITAR-TASS’ news coverage
relative to Interfax’s corresponding coverage, and relative changes after pivotal events are
treated as bias caused by the influence of the Russian government. This is an application
of the difference-in-differences technique, which is widely used in econometrics to esti-
mate the impact of policy interventions (cf. Card and Krueger, 1994), although it is much
more limited for a number of reasons. First, I cannot assume a high stability in benchmark
units (media outlets) in studies of media since the spread of information is much less
restricted than it is in policy interventions. Second, I often cannot find multiple bench-
mark units on which to base my statistical estimation of the uncertainty of observed news
bias: this is because there are few media outlets comparable to those in which I am inter-
ested. Third, the occurrence of media bias can proceed pivotal events when they are pre-
dictable (staged events). However, unlike other social scientists, who only have access to
numeric data, I can scrutinize original texts produced by the news media and supplement
the quantitative data with rich textual information to overcome the limitations.
Pivotal events
In the early days of the crisis, there were events with which I can relatively easily associ-
ate Russia’s political interests, but as soon as the fight between Kiev’s military forces and
separatists began, the Russian regime’s wishes became increasingly obscure. Therefore,
I restricted my analysis to the period from 1 January 2013 to 21 April 2014, the day
before the Kiev government relaunched its anti-separatist operations. Table 1 presents
pivotal events in the Ukraine crisis with their desirability to the Russian regime.4
Data collection
For my content analysis, I downloaded the English-language news stories covering Russia
and CIS countries published by ITAR-TASS and Interfax, respectively, from the Nexis
and Integrum databases between 2013 and 2014.5 I collected 103,236 stories for Interfax
and 87,725 for ITAR-TASS, after removing duplications. I also downloaded 21,718
Reuters reports from the Factiva on Ukraine, but they were used solely for manual reading
and dictionary construction, as explained in Section 2 in the online appendix.6
Watanabe 7
Content analysis
To perform a statistical analysis of news reporting by the news agencies, I content ana-
lysed the downloaded news stories in terms of their geographical focus and positive–
negative framing of the state of democracy and sovereignty in Ukraine. Both geographical
classification and framing analysis were accomplished by employing computerized con-
tent analysis, which relies on dictionaries constructed by lexicon expansion techniques
(cf. Pang and Lee, 2008; Turney and Littman, 2003). The geographical dictionary com-
prises not only names of places but also of institutions and persons related to the crisis
for a higher classification accuracy. The framing dictionaries contain words related to
democracy and sovereignty and scored in terms of their positive–negative sentiments.
Construction of these dictionaries was based on statistical analysis of the corpus of news
stories that I downloaded to avoid arbitrary choices of words.
The adoption of computerized techniques is not only for efficiency in analysing the
large volume of news stories published over 16 months but also for consistency, which is
usually difficult for human coders to achieve. The geographical classifier removed
almost all the news articles not about Ukraine, accomplishing .94 in precision and .83 in
recall. The framing analysis could replicate human judgements, achieving strong correla-
tion between machine and human coding both in democracy (r = .77) and in sovereignty
(r = .70) (see Online Appendix 2 for detailed explanation and validation of the computer-
ized method).
Statistical model
To estimate news bias in ITAR-TASS’ news reporting, the continuous sentiment scores
(Y) were regressed on indicators for time period following the pivotal events
()
,
ee
16
a dummy variable for ITAR-TASS
()
g and their interactions
()
eg eg
16
with a random
intercept
()
u clustered by day
Table 1. Pivotal events in the early stage of the Ukraine crisis.
Date Label Event Desirability
3 September 2013 E1 Yanukovych demands legal reforms to
MPs for EU association plan
Negative
21 November 2013 E2 The trade agreement with the EU is
abandoned by Yanukovych
Positive
16 January 2014 E3 Protest against the pro-Russian regime
in Kiev intensifies
Negative
22 February 2014 E4 Yanukovych is removed from
presidency by the parliament
Negative
16 March 2014 E5 Crimea referendum is held and 95%
support accession
Positive
15 April 2014 E6 Military operations against separatists
are launched
Negative
MP: member of parliament; EU: European Union.
8 European Journal of Communication 00(0)
Yeegeg eg u=+ ++ ++ ++++
ββ βδγγ
011661166

ε
The inclusion of random intercept is to accurately estimate differences between
ITAR-TASS and Interfax by controlling for variance caused by time-dependent hetero-
geneity. In this model,
δ
captures time-independent institutional heterogeneity,
ee
16
are real-event effects and the coefficients
are Russian government-ownership
effects, in which I am most interested.
Analysis
The data produced by my content analysis is visualized in Figures 1 and 2, where red
circles represent sentiment scores of individual ITAR-TASS news articles, and black and
red curves, respectively, show average sentiment scores of news articles published by
Interfax and ITAR-TASS. The average sentiment scores are interpreted as representing
the positive–negative framing of democracy and sovereignty in Ukraine by the Russian
agencies at particular points of time during the crisis.
In Figure 1, the red curve runs higher than and parallel to the black line before E1,
showing that the framing of Ukraine’s democracy was normally more positive by ITAR-
TASS than by Interfax. However, ITAR-TASS’ coverage shifts towards negative after
E1, when the president called for legal reforms to join the European Union (EU), but it
returns to the normal level of positivity relative to Interfax over E2-E3, following the
abandonment of the trade agreement with the EU. A sharp negative shift occurs after E3,
and its framing becomes almost as negative as Interfax’s over E4–E5. Finally, its framing
moves sharply negative after E5, reaching peak negativity around E6, coinciding with
the launch of the anti-separatist operation by the Kiev government.
In Figure 2, the difference in the framing of sovereignty between ITAR-TASS and
Interfax over E1–E2 remains approximately the same as the pre-E1 period. A negative
shift of framing starts only after E2, and the relatively positive framing by ITAR-TASS
disappears in E3–E4, when the anti-government protests intensify in Kiev. Nevertheless,
its framing rapidly improves from E4 towards E5 when the Crimean referendum was
held, but it, again, becomes as negative as Interfax after E6.
Amount of bias
The amount of bias in the framing of the Ukraine crisis by ITAR-TASS was estimated
using the statistical model, the results being presented in Table 2. In the table, the most
important coefficients are found next to the interactions between the time indicators
(E1–E6) and the dummy variable for ITAR-TASS (TASS), which measures effects of
Russian government’s ownership. The estimated state-ownership bias is also summa-
rized in Figure 3 with 95% confidence intervals.
As summarized in Figure 3, ITAR-TASS’ coverage of democracy in Ukraine becomes
statistically significantly more negative (−31.7, p < .05) than during pre-crisis after
Yanukovych’s speech (E1), indicating the Russian government’s influence on ITAR-
TASS. Its framing of Ukraine then becomes as positive as the pre-crisis period after the
abandonment of negotiation (E2). The change following the intensified anti-regime
Watanabe 9
−100 −50 050 100
Sentiment score
Jan
2013
FebMar AprMay JunJul AugSep OctNov DecJan
2014
FebMar Apr
Interfax
TASS
E1 E2 E3 E4 E5 E6
Figure 1. Framing of democracy.
10 European Journal of Communication 00(0)
−100 −500 50 100
Sentiment score
Jan
2013
FebMar AprMay JunJul AugSep OctNov DecJan
2014
FebMar Apr
Interfax
TASS
E1 E2 E3 E4 E5 E6
Figure 2. Framing of sovereignty.
Watanabe 11
protest (E3) is only marginal (−16.7, p = .054), but the collapse of the regime (E4) (−29.5,
p < .01) and Crimea referendum (E5) (−38.2, p < .01) is strongly significant. The framing
of democracy in Ukraine becomes increasingly negative, reaching −52.3 points (p < .01)
after the start of anti-separatist military operations (E6). This result clearly shows that all
the events, other than E5, are followed by changes in framing towards the same direction
as predicted by their desirability for the Russia regime.
ITAR-TASS’ framing of sovereignty becomes significantly negative (−32.2, p < .01)
only after anti-regime protests intensify (E3) because earlier events did not have serious
implications for Ukraine’s sovereignty. Framing starts shifting towards the positive
(−24.7, p < .05) from the collapse of the regime (E4) and then negativity completely dis-
appears (p = .21) after the Crimea referendum (E5), but Kiev’s military operations against
pro-Russian separatists (E6) brings it to the most negative level (−48.7, p < .01). These
changes also match the patterns that the author expected based on the desirability of
events for the Russian government.7
Source of bias
The statements of Russian officials frequently quoted in ITAR-TASS’ news articles are
one of the main sources of bias. In my statistical analysis, a dummy variable for mentions
of Russian entities (Russia in Table 2) created from the secondary-country category by
the geographical classifier shows that articles mentioning Russian entities are 23.6 points
(p < .01) more negative about the democracy in Ukraine, and higher proportions of quotes
in articles (Quote in Table 2) lead to more negative framing of the country (β = −70.0,
p < .01). The effect of mentions of Russian entities also appeared to be statistically sig-
nificantly negative (β = −18.7, p < .01) on framing of sovereignty (Model 5), but propor-
tions of quotations have no significant effects in this subject (p = .64). Yet, further
exploration of the data revealed that quadratic terms of the proportions (Quote2) have
very strongly significant effects in both democracy (β = −41.8, p < .01 in Model 3) and
sovereignty (β = −105.2, p < .01 in Model 6).
Figure 4 presents sentiment scores predicted by the Models 3 and 6 for news articles
which mentioned Russian entities and were published by ITAR-TASS after E6. These arti-
cles clearly show a non-linear association between sentiment scores and proportions of
quotations, which suggests that there are, at least, three types of biased news stories. The
first type simply describes situations regarding democracy and sovereignty in Ukraine
negatively with little or no quotation of sources (less than 30% of wordage), while the
second largely relies on negative comments on Ukraine made by Russian officials or pro-
Russian Ukraine leaders (more than 70%). In the third type, relatively positive comments
on Ukraine made by foreign actors, who are important in stories on sovereignty, are quoted
(30–70%), but these are followed by very negative descriptions of the situation in the coun-
try, which are barely relevant to the quotes, to make the overall framing in the news articles
more negative (examples of these three types are presented in Online Appendix 5).
Discussion
In my analysis of the framing of democracy and sovereignty in Ukraine by ITAR-
TASS’ English-language service, I found that the news agency’s framing reflected the
12 European Journal of Communication 00(0)
Table 2. Framing of the Ukraine crisis by ITAR-TASS.
Dependent variable
Democracy Sovereignty
(1) (2) (3) (4) (5) (6)
TASS 31.033*** (6.953) 22.735*** (6.946) 22.416*** (6.943) 34.783*** (9.695) 27.411*** (9.954) 27.231*** (9.910)
E1 14.575** (6.655) 17.458*** (6.525) 17.663*** (6.517) 16.757* (8.916) 20.562** (8.949) 20.538** (8.874)
E2 −42.680*** (6.007) −40.624*** (5.920) −40.111*** (5.913) −19.738** (7.990) −19.243** (8.012) −18.363** (7.928)
E3 −45.593*** (5.883) −45.494*** (5.828) −44.906*** (5.821) −27.975*** (8.108) −30.279*** (8.149) −29.495*** (8.053)
E4 −24.071*** (6.628) −16.678** (6.560) −15.750** (6.554) −4.876 (8.844) −2.373 (8.895) −0.714 (8.780)
E5 −17.029*** (6.492) −7.679 (6.467) −7.175 (6.458) −16.221* (8.511) −10.020 (8.624) −10.128 (8.518)
E6 −27.771** (11.119) −15.346 (11.047) −14.695 (11.027) −19.681 (14.534) −12.285 (14.658) −11.271 (14.460)
Russia −23.663*** (3.052) −23.481*** (3.050) −18.714*** (3.757) −18.516*** (3.741)
Quote −70.038*** (4.066) −34.341*** (12.456) −2.375 (5.204) 89.584*** (16.076)
Quote2−41.860*** (13.807) −105.207*** (17.413)
TASS:E1 −31.737** (12.422) −31.808*** (12.110) −31.787*** (12.102) −27.482 (18.747) −30.023 (18.768) −31.778* (18.688)
TASS:E2 −1.067 (9.320) −0.390 (9.085) 0.172 (9.081) −16.277 (12.990) −15.150 (12.987) −14.970 (12.930)
TASS:E3 −16.794* (8.741) −17.785** (8.522) −17.681** (8.517) −32.245*** (12.456) −30.403** (12.434) −30.872** (12.381)
TASS:E4 −29.571*** (8.744) −34.481*** (8.594) −34.003*** (8.590) −24.722** (11.887) −25.576** (11.987) −25.141** (11.937)
TASS:E5 −38.267*** (9.185) −41.076*** (9.205) −39.884*** (9.208) −14.107 (12.094) −17.361 (12.454) −15.024 (12.408)
TASS:E6 −52.389*** (13.013) −54.172*** (12.887) −53.512*** (12.881) −48.739*** (16.583) −53.991*** (16.825) −52.927*** (16.757)
TASS:Russia 10.958** (5.118) 10.944** (5.115) 13.335* (7.048) 14.804** (7.024)
Constant 8.998*** (3.264) 48.364*** (3.840) 43.364*** (4.176) 12.560*** (4.426) 22.348*** (5.205) 9.795* (5.572)
Observations 6723 6723 6723 4180 4180 4180
Log likelihood −40,066.280 −39,871.670 −39,863.540 −24,936.590 −24,916.190 −24,894.250
Akaike information
criterion
80,164.550 79,781.340 79,767.070 49,905.170 49,870.380 49,828.500
Bayesian
information
criterion
80,273.530 79,910.750 79,903.280 50,006.530 49,990.730 49,955.180
*p < .1; **p < .05; ***p < .01.
Watanabe 13
desirability of the preceding events for the Russian government, that is, only the aban-
donment of the trade agreement with the EU and the Crimean referendum was framed in
as positive a manner as news on Ukraine had been in the pre-crisis period. Apart from the
periods following these two events, framing of the Ukraine crisis was profoundly nega-
tive, the most negative framing appearing after the launch of military operations against
pro-Russian separatists. In this period, ITAR-TASS’ framing of democracy and sover-
eignty shifted 1.88 and 2.47 times greater than Interfax’s framing towards the negative,
whereby I estimated the amount of bias in ITAR-TASS’ coverage to be as large as −52.3
points regarding democracy and −48.7 points regarding sovereignty. These findings sup-
port my first and second hypotheses (H1 and H2), and thus, I argue that ITAR-TASS’
news coverage of Ukraine was biased, reflecting the interests of the Russian government
in the country.
The strategic coverage of the Ukraine crisis by ITAR-TASS is indicative of the impor-
tance of the news agency in Russia’s ‘hybrid wars’, which utilizes non-military means to
attain military goals. In recent years, researchers have paid special attention to Russia’s
satellite news channel, Russia Today (RT), as a medium for public diplomacy (Galeotti,
2015; Nelson et al., 2015), but very few studies on ITAR-TASS have been conducted
from this perspective. The findings of this research suggest that the soft power strategy
of Russia, which has been advanced by Vladimir Putin since 2012 (Light, 2015), is more
comprehensive than previously thought, namely, in addition to the dissemination of news
stories directly to foreign audiences via RT, the Russian government utilizes ITAR-TASS
to reach foreign news media, bypassing the Western media’s foreign correspondents in
Moscow, who tend to be negative about the regime (Evans, 2005). To achieve this goal,
ITAR-TASS even mixes its own very negative descriptions on Ukraine with positive
comments of Western leaders, who are generally more newsworthy than Russian offi-
cials for Western audiences, in its news coverage, creating the non-linear relationship
between the sentiment scores and the numbers of quotation. This is a sophisticated prop-
aganda technique to increase the chance of its news stories to be accepted and redistrib-
uted by foreign news media.
By scrutinizing the three types of biased news stories, I have discovered that the main
sources of bias in ITAR-TASS’ coverage of Ukraine were (1) statements of Russian
Figure 3. Estimated state-ownership effect.
14 European Journal of Communication 00(0)
officials, to which the Russian news agency grants higher prominence, and (2) negative
descriptions of the situation in Ukraine, supporting my third and fourth hypotheses (H3
and H4). These causes are to a large extent consistent with the typology developed by
D’Alessio and Allen (2000), but not entirely so because ITAR-TASS’ news articles are
written in an ‘objective’ style without making clear distinction between opinions and
facts as required in Western journalism. In other words, the typology of news bias devel-
oped in research on the Western media does not fully apply to the non-Western media, in
which opinions are disguised as facts.
Based on the findings, I propose three changes in its definitions of news bias to extend
the scope of the typology. First, D’Alessio and Allen have defined statement bias as a
result of inclusion of journalists’ opinions, but it should not be restricted to direct expres-
sion of opinions (e.g. expressly support or criticize actors or ideas) because opinions can
be blended into news stories in various forms, some of which are very difficult to distin-
guish from ‘objective’ description of events or issues. In fact, much of the bias in ITAR-
TASS’ news stories on Ukraine was caused by descriptions with excessive emphasis on
their negative aspects of events. Second, as Barkho (2013a) pointed out that sources of
bias are not only backgrounds of individual journalists (personal bias) but also ideologi-
cal, social and political orientations of media organizations (corporate bias), statement
bias should encompass insertion of opinions of media organizations as well as of indi-
vidual journalists because personal opinions of journalists were not found in ITAR-
TASS’ news stories at all. Third, gatekeeping bias was very broadly defined as it is
caused by selection or deselection of particular kinds of stories, but it should be rede-
fined as bias caused by prioritization of particular sources since quotation of news
sources is the most significant source of bias, which can be easily distinguished from
statement bias. These proposed definitions of news bias are summarized in Table 3.
Finally, the revelation of the systematic bias in ITAR-TASS’ news coverage of
Ukraine demonstrates that the new methodology is an effective approach to measuring
Figure 4. Non-linear relationship between sentiment and quotes.
Watanabe 15
news bias. Although I have focused on ITAR-TASS in this research, the new approach is
not limited to studies of news agencies or international news media: It is particularly use-
ful in research on media bias in countries with a multi-party or authoritative political
system, where estimation of news bias has been very difficult due to the lack of non-
media benchmarks. In research on the news bias in multi-party political systems, one can
choose a news organization with a particular characteristic (e.g. ownership, political
affiliation, etc.) that is expected to cause bias in its news content. Then, the news content
should be compared with news content produced by other news organizations lacking
that characteristic. Even if partisan journalism is widely practised, inclusion of multiple
benchmark units selected from the entire political spectrum should allow estimation of
news bias. Authoritative media systems usually have very few independent or anti-
regime media outlets, but comparison between the state-controlled media should show
relative sizes of news bias correspondingly to media outlets’ susceptivity to the media
control as I have shown elsewhere (Lankina and Watanabe, in press). I invite readers to
research on objectivity of news in some of the most problematic media systems, where
biased news reporting is the pressing issue to democracy.
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship,
and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship and/or publication of this
article.
Notes
1. Entman is still dependent on his own knowledge of objective reality in identifying which
aspects are not covered by the news when applying this approach.
Table 3. Types and definitions of news bias.
Type Cause Structure Example Measurement
Statement
bias
Insertion of opinions
of journalists or media
organizations
No quote Stories emphasizing
social disruption
caused by pro-EU
protesters Positive–negative
framing of events,
issues or actors
in relation to
benchmark units
Description of events
or issues with focus on
particular aspects
Gatekeeping
bias
Quotation of particular
type of sources
Direct or
indirect
quotes with
attribution
Stories quoting
Russian officials
who criticize
military operations
against pro-Russian
separatists
EU: European Union.
16 European Journal of Communication 00(0)
2. ITAR-TASS was renamed TASS in September 2014 again to emphasize its connection to the
predecessor (TASS, n.d.).
3. This statement was originally ‘consistent patterns in the framing of mediated communica-
tion that promote the influence of one side in conflicts over the use of government power’
(Entman, 2010: 166).
4. See Section 1 in the online appendix for more detailed timeline of the crisis.
5. The sources were the World service wire of ITAR-TASS; Commonwealth of Independent
States (CIS) and Russia General Newswires; and Kazakhstan, Belarus, Ukraine and Asia
Newswires of Interfax.
6. Online appendix is available at http://koheiw.net/wp-content/uploads/2017/02/Measuring-
news-bias-06-appendix.pdf.
7. Confirmation of the statistical findings by manual reading of the news stories is presented in
Section 4 in the online appendix.
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Russia has issued an unusually large number of official documents on various aspects of its foreign policy since the country became an independent state in 1991. Andrey Kozyrev, the first foreign minister, was reluctant to compose a document defining Russian foreign policy, arguing that as that policy would be based on the country’s national interest, the underlying principles would be self-evident. Those in favour of a formal document claimed that working out Russia’s foreign policy would assist in defining the country’s identity. Kozyrev relented, and the first Foreign Policy Concept was adopted in 1993, followed soon after by a Military Doctrine.1 Both were replaced by new versions in 2000, and the Foreign Policy Concept was updated again in 2008. Russia’s most recent foreign policy statement, Concept of the Foreign Policy of the Russian Federation, was approved by President Vladimir Putin in 2013. The latest Russian Military Doctrine was adopted in December 2014. Since 1997 Russia has also had a series of national security concepts. A second version was adopted in 2000 and a third in 2009, this last being called the Strategy of National Security of the Russian Federation until the year 2020. Russia also has an Information Security Doctrine, a Concept of Participation in International Development Assistance and, most recently, a Concept of Participation in BRICS.2
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Studies of Russian society have traditionally highlighted the particularity of the country's historical path and the irregular, irrational nature of Russian culture (Kangaspuro, 1999). Scholars have described Russia as characterized by numerous contradictory features. Not surprisingly, the nature and structure of the modern Russian media system reflect political, economic, and sociocultural developments deeply rooted in the country's history. During the two last centuries Russia has experienced a permanent transition:. from an agrarian society and imperial monarchy in the early nineteenth century. to a rapid, though uneven growth of capitalism and rise of a diverse party system under the rule of an authoritarian ruler (tsar) in the second half of the nineteenth century (interrupted by World War I early in the twentieth century). to a short-lived bourgeois multiparty democracy in February-October 1917. to the socialist revolution (early twentieth century), resulting in the emergence of the Communist Party monopoly and state-controlled planned economy and eventually to years of “mature socialist democracy” characterized by economic recession and degradation of political communication to propaganda. to a “perestroika” (reconstruction), as a policy of top-down Communist Party reforms that resulted in the collapse of the USSR. to the establishment of Russia as an independent state accompanied by a liberalization process and the introduction of a market economy and political systems inspired by “Western” models of liberal democracies.