Int J Polit Cult Soc
DOI 10.1007/s10767-010-9103-7
The Historical and Linguistic Analysis of Turkish
Politicians’ Speech
Baburhan Uzum & Melike Uzum
# Springer Science+Business Media, LLC 2010
Abstract The current study investigates Turkish politicians’ code mixing in their public
speeches and the variation among the speakers as well as the historical and linguistic factors
influencing this variation. It is hypothesized that the variation either consciously or
unconsciously outlines the identity of the speaker in the direction that s/he wants to be
identified by their audience. The findings are interpreted with reference to the language
reform (a linguistic change during the transition from the Ottoman Empire to the Turkish
Republic) that Turkish language went through in 1928.
Keywords Linguistic reform . Politics of language . Identity transformation . Speech
analysis . Language change
Introduction
In order to shed light onto the use of contemporary Turkish, it is obligatory to investigate
the historical and sociopolitical factors that paved the way for the language reform. After
the collapse of the Ottoman Empire during the First World War in 1918, the Turkish
Republic was founded in 1923. The Ottoman Empire, being the home of numerous
ethnicities for more than 600 years, did not have a strict language policy and granted the
public the independence to speak the language they chose. In the nineteenth century, there
was an enormous discrepancy among the citizens of the empire. While minorities used their
heritage language, the majority of Turkish people used Turkish, and the Ottoman language
was confined within the walls of the palace spoken only by the elite. Ottoman language was
heavily influenced by Arabic and Persian due to their prestigious status at the time.
Aydıngun and Aydıngun (2004) maintain that the language reform was imperative to
B. Uzum (*)
Second Language Studies, Michigan State University, East Lansing, MI 48823, USA
e-mail: baburhan@msu.edu
M. Uzum
Turk Dili ve Edebiyati, Konya Selcuk Universitesi, Meram, Konya, Turkey
Uzum and Uzum
maintain the unity of the empire which had been invaded by the greatest powers of Europe.
In order to diminish this discrepancy and unite the people of the infant republic with a
common ideal, Turkish Language Reform was adopted in 1928.
It is interesting to note that Turkish language was not a prestigious language either in the
Seljuq Dynasty (before the Ottoman Empire) or the Ottoman Empire. The Seljuq dynasty
greatly admired Persian for being the language of science and literature during the eleventh and
twelfth centuries, while the Ottoman Empire attributed much prestige to Arabic for the language
of Quran and Islam. Turkish language, formerly being perceived as a nomad language, was
chosen to be the language of the Republic. In addition to the language, “Turk” was the new
identity of the public. In order to refute the old beliefs about Turkish language and Turkish
identity and build a nation from its ashes, Mustafa Kemal Ataturk—the founder of the Republic
—emphasized the significance of the language in his speeches. Bear (1985) quotes Ataturk:
The language of the Turkish nation is Turkish. The Turkish language is the most
beautiful, the richest and the simplest language in the world. For that reason, every
Turk loves his language greatly and seeks to glorify it. Furthermore, the Turkish
language is a sacred treasure for the Turkish nation. Because, the Turkish nation
realizes that in the many difficult situations which it has experienced, it has preserved
its morals, its traditions, its memories, its interests, in short, everything which makes
up its own nationality through the medium of the language.
Turkish language reform, promoted by Ataturk with the goal of rescuing Turkish from the
influence of other languages such as Ottoman, Persian, and Arabic, included the process of
purification. This involved excluding non-Turkish words from its lexicon, replacing these with
Turkish words, coining new words from Turkish roots and affixes and adopting the Latin
alphabet. However, the course of years that followed brought about new sociopolitical changes
causing, some social and linguistic problems to emerge. First of all, nationalism which was
originally promoted for nation-building purposes was often associated with racism and
ethnocentrism. Ultimately, two extreme theories emerged in the 1930s, over-emphasizing the
Turkish identity. These two theories are “the sun-language theory” and “Turkish history thesis.”
According to the former, Turkish language had been the most ancient language, and it had
provided crucial elements for the creation of other languages (Ayturk, 2004). Turkish history
thesis, on the other hand, claims that all nations within Anatolia were of Turkish origin as the
people who came from the central Asia were the first residents of the region. Neither of these
theories could be supported by scientific evidence; however, they were suggested with the
aim of increasing pride in Turkish culture so that Turkish people could claim a respected
status in the world history.
In addition to the nationalism boosted by these theories, some other problems emerged
in the following years due to modernization and westernization. In the 1920s, Ataturk
aimed to create a Turkish identity escorted by modernization. The Ottoman Empire had
fallen behind other European countries in terms of military, social, and educational
improvements. During the speedy transformation, social, political, and educational reforms
brought linguistic influences. New concepts were adopted along with their foreign lexicon.
French, the lingua franca of the time, penetrated into the Turkish language through such
concepts. As the process of modernization brought along its vocabulary, Turkish language
was, yet again, under the massive influence of foreign languages. Özen (2003) put his
disagreement with the following quotation:
Once Arabic and Persian, and then French, in the age of modernization and the
Westernization. Everything in the country became ‘German’, including Wilhelm style
The Historical and Linguistic Analysis of Turkish Politician
Moustache. When train came into the country, it came along with cars filled with
French words! Imagine, anything pertinent to trains in this country is not Turkish! Do
Germans do that? There was only Atatürk who wanted to lead this train into national
lands, and achieved this in his time
The current statistics show that Turkish language was heavily influenced from the process of
modernization. Modern Turkish Dictionary (2005) includes 104,481 entries, of which about
14% are of foreign origin. Among the word donors, Arabic comes first with 6,463 words
followed by French (4,974), Persian (1,374), Italian (632), English (538), and Greek (399).
As a result of Turkish Language Reform, Persian and Arabic lost their dominance in the
lexicon of the contemporary Turkish language; yet, other European languages penetrated in
to the language with the process of modernization. In the contemporary Turkey of the
2000s, the influences of language reform and the subsequent social and linguistic changes
could still be observed in language use. Given that speech is always subject to variation and
otherwise interpersonal communication would not be possible (Williams 1992), Turkish
language along with its history and politics is allowing much room for variation. Thanks to
the contributions of different languages to Turkish lexicon, speakers enjoy the independence to switch to different codes or borrow words from different codes depending on their
education, political orientation, or background.
To clarify, a speaker who identifies himself with the religion Islam may tend to borrow
words from Ottoman, Arabic, and Persian, while another speaker, identifying himself with
the Turkish identity, follows a more nationalist approach and do not use any borrowed
words being loyal to the pure Turkish lexicon. Finally, a speaker believing modernization
and westernization thus keeping himself away from both Islamic conservatism and Turkish
nationalism tends to borrow words from European languages. To illustrate, to mean
“specific,” the first speaker says “mustesna,” the second “ozel,” and the latter “spesifik.”
The frequency and the contexts of their lexical choices might be revealing their social
identity, political orientation, and educational background.
Related Studies
Having a similar focus to the current study, the study of Amara (1996) investigated the
lexical borrowings from English and Hebrew in Palestinian Arabic. Exploring how social
and political pressures are related to the pattern of sociolinguistic change in a speech
community, he concluded that language contact with Hebrew speakers triggered an effect
on Palestinian Arabic due to its prestigious status stemming from the association of Israeli
culture with progress.
Studies investigating identity (re)-construction have significant relevance to the current
study. Amara and Spolsky (1996) carried out a noteworthy study exploring the construction
of identity in a divided Palestinian village. They conducted a sociolinguistic survey of a
village for 2 years. A total of 81 villagers were interviewed and recorded creating contexts
for careful and more informal speech. They concluded that the construction of Israeli
identity is through some borrowed qualities. The Arab–Palestinian identity, on the other
hand, encourages standard Arabic features. According to the findings of Amara and
Spolsky, the most influential identity is Palestinian, probably due to the linguistic change
towards Standard Arabic and the major sensitivity to its use.
Another study, investigating Turkish language as a symbol of survival and as an identity
marker in the Balkans and in Turkey, was carried out by Balim in 1996. Balim (1996)
Uzum and Uzum
provided a brief history of the current situation and wrote of the resistance of Turkish people
living in the Balkans, before they were forced to migrate to Turkey by the Bulgarian
government in the 1950s. As for the language policies in Turkey, there is also a considerable
effort to prevent foreignization by translating words and terms of foreign origin and thus make
new Turkish words. For example: sabun operası—the direct translation of “soap opera”;
doğalgaz—“natural gas”; masaüstü yayıncılık—“desktop publishing.” Reporting the recent
attempts of Turkish government to control the penetration of foreign words into Turkish,
she concluded that the language to be used can no longer be regulated by law or by
government decree. It is the younger generations who will determine the pattern of use.
Abu Haidar (1996) had a similar scope to the current study using the opposite direction.
Recording the Baghdadi speakers from different ages in England, he studied the Turkish
borrowings in the respondents’ speech. According to his research findings, the lexical items
found in their speech were mostly (1) substantives (such “fault,” etc.), adjectives (mawi
“blue”), and particles (ham “also,” balki “perhaps”). He concluded that Turkish and
Baghadi Arabic communities showed a significant similarity in the way they regard
Turkish, as a symbol of their ethnoreligious identity. Though Turkish borrowings are
decreasing through generations, they continue to be used due to their symbolic value. In
addition to the emphasis on religious affiliation, Turkish borrowings create an in-group
code encouraging group solidarity and identity in the face of increasing interference from
other varieties of Arabic.
As for the major events in the history of Turkey that brought about sociolinguistic
changes, Turkish Language Reform has a major role. One of the earliest studies exploring
the effects of Turkish Language Reform on individual perception is that of Cuceoglu and
Slobin (1980). In this study, they investigated the attitudes of the audience towards the
speakers with varying frequencies of different codes. They found that learners were able to
identify the speakers as leftist and rightist, evaluating their speech, and thus favor or
dismiss them depending on their own political orientation.
On the other hand, the claim of this study for an imaginary or a third space through
code-switching was also reflected in a recent study by Bhatt (2008) which investigated the
use of Hindi in English newspapers in India. In this study, he argues that code-switching
creates a third space where two systems of identity reflection come together in response to
global/local tensions and identities formed through resistance and appropriation. He
concludes that speakers in this space have the capability to synthesize and to transform.
Therefore, code-switching serves as a marker of this transformation, the inventive
adaptation to communicate a kind of multiplicity which is highly contextual and a new
“habitus” for its speakers with a slightly changed representation of social order.
Method
The current study hypothesizes that the politicians’ lexical choice is not arbitrary and may
follow a pattern because of conscious or unconscious choices they make between the
languages. The data for this study come from unplanned public speeches made by the
politicians belonging to different parties from September 2006 and July 2007 during the
election campaigns that happened in Turkey. The sampled parties are (1) AKP and (2) SP
(Islamic-conservative groups-right wing), (3) MHP and (4) BBP (Turkish nationalist
groups-right wing), and (5) CHP and (6) DSP (secular-modernist groups-left wing). One or
two speakers will be selected from each party. The reason for selecting politicians are (1)
the parties they are from represent the group they belong to, so that they will be associated
The Historical and Linguistic Analysis of Turkish Politician
to a social group in the society, (2) if there is a conscious effort to choose a code, this must
be most marked in their case as they would like to be perceived in a certain way by their
interlocutors, (3) the data they provide are publicly available and share similar contexts
among the speakers including topics formality and interlocutors.
Data Analysis
The public speeches are transcribed and coded for analysis. A coding scheme, including the
structure of the borrowed word (noun, verb, etc.), the origin of the borrowed word (Arabic,
English, Persian) the preceding and following words, the position in the sentence, the
context of utterance, the topic of utterance (law, education, etc.), the frequency of the word
in this data set, stress, and phonological variation (following and preceding pause) is
created. According to the preliminary analysis of the coding scheme, token words are
selected for in-depth analysis. The findings are interpreted using quantitative and qualitative
analysis (content analysis) methods. For qualitative analysis, the frame of reference of
Johnstone (2000) is used for design and methodology issues. Some tokens which have
political meanings (democracy, republic, election) and are used without variation among all
the speakers, are excluded from analysis.
Results and Discussion
According to the statistical findings, the speakers did online borrowings from other
languages including Arabic, Persian, French, English, etc. during the elapsed time of their
speech. In order to investigate which speaker refers to which language for borrowing a
word, variable rule analysis has been conducted. The distribution of borrowings according
to the languages and the speakers are investigated within the framework of eight factors
which are shown in Table 1.
According to the variable rule analysis: Binominal up and down (which is a logistic
regression test identifying the factor groups contributing to the model, and eliminating those
without any significant contribution), factors 2–8 were eliminated and factor 1, part of the
speech, was identified to be the only factor with most contribution (likelihood=−139.048,
p<0.05). In addition to the binominal up and down test, descriptive analyses are used to
show the differences between the variables, which might have an influence on the
dependent variable not yet statistically large enough to be detected by the logistic regression
tests. Figure 1 shows the scatter gram for probability of influence.
The descriptive analysis of the factors is given in the order they have appeared in
Table 1. According to the statistical findings, the percentage of the speakers’ use of
borrowed words is 41.2% (N=93), while the use of Turkish-origin words has a percentage
of 58.8% (N=133). Table 2 illustrates the percentage and frequency of borrowed and
Turkish-origin words within this data set.
An example of the use of Turkish words is given in sentence (1), and an example of the
use of borrowed words is given in sentence (2) below:
1. Bu ne anlama gelir biliyorsunuz. (Speaker ZS: secular modernist group)
You know what this means.
2. Bunun manası bir merkezden yönetilir. (Speaker NE: the conservative group)
Uzum and Uzum
Table 1 The distribution of borrowings according to the languages
and the speakers
Factors
Focus of the factors
Factor 1
Part of speech of the word
Factor 2
Topic of the utterance
Factor 3
Stress
Factor 4
Position in the sentence
Factor 5
Pauses
Factor 6
The language of origin
Factor 7
Speakers
Factor 8
Area of utterance
This means that it is organized from a single source.
In sentence (1), the speaker-ZS uses a Turkish-origin word which means (meaning);
whereas NE uses an Arabic word, though there is a Turkish equivalent to give the same
meaning. These two words co-exist in contemporary Turkish language with varying degrees
of frequency. In this example, the borrowed word is a noun. There are also other instances
with verbs, adjectives, and adverbs. In this data set, nouns, verbs, adjectives, and adverbs
are sampled for statistical analysis. Table 3 shows the distribution of Turkish and borrowed
words according to the structure of the token.
As Table 3 illustrates, nouns have the greatest frequency (N=154) in this dataset and
shows a similar distribution in the categories of borrowed (N=78) and Turkish-origin (N=
76) words. The second-most frequent part of speech is the verbs (N=52). The use of
Turkish-origin verbs is observed in 46 tokens, while a borrowed verb is used only six times.
In the category of adjectives and adverbs, there is also a similar distribution between
borrowed (N=8) and Turkish-origin (N=10) words.
Another variable is the topic of the speech in which the token is analyzed. Table 4 shows
the topics included in this analysis.
As can be seen from Table 4, the most common topic of speech is politics (N=187),
followed by history (N=23), business and economy (N=13), and religion (N=3). There are
Fig. 1 The scattergram of probability of application/nonapplication
The Historical and Linguistic Analysis of Turkish Politician
Table 2 The frequency of
Turkish-origin and borrowed words
The use of Turkish words
The use of borrowed words
Total
58.8% (N=93)
41.2% (N=133)
100% (N=226)
not significant differences among the categories in terms of the use of borrowed or Turkishorigin words. However, the greatest difference can be observed within the categories of
business and economy, religion, and politics.
The following variable is the phonological variable to investigate whether the speakers
stress the word they borrow. Table 5 shows the frequency of borrowed and Turkish-origin
words according to the stress (stress, no stress) made on the token word.
As could be seen from Table 5, the instances of stress on the token word (N=31), are
significantly less than no stress instances (N=195). The speakers stressed on the token
word more, when it is a borrowed word (N=19), than when it is a Turkish-origin word
(N=12).
In addition to the phonological variables that might have influenced the onlineborrowing, the position of the word in the sentence (beginning, middle, end) is analyzed.
Table 6 shows the distribution of tokens according to the position of the word.
As shown in Table 6, most of the token words are located in the middle of the sentence
(N=196), very few of them are positioned at the beginning (N=13), or at the end of the
sentence (N=17). When the token word is at the beginning of the sentence, it can be either a
borrowed word (N=6) or a Turkish origin word (N=7). The token words in the middle of
the sentence are generally in Turkish (N=112) and less frequently in other languages (N=
84). When the token word is at the end of the sentence, where the verb is located in
Turkish, it tends to be in Turkish-origin (N=14) rather than other languages (N=3). This
could be explained with the fact that Turkish is a verb-final language, and most of the
borrowings are in the noun category as shown in Table 3, whereas it is less often in the verb
category.
Another phonological variable that might have influenced the online-borrowing is the
pauses that take place before or after the token word. Table 7 shows the distribution of the
token words according to the preceding and following pauses.
As Table 7 shows, there are 20 instances of preceding pauses. Speakers paused (more
than 2 s) before a borrowed word (N= 15), and a Turkish-origin word (N= 5). While the
following pauses are more often after a Turkish-origin word (N= 14) than a borrowed
word (N =8), most of the tokens do not indicate any pause before or after a token word
(N= 184).
One of the most significant factors in this study is the language from which the online
borrowings are made. Speakers mix words from different languages such as Arabic,
Persian, French, English, etc. in their speech. Although these words exist in the Modern
Table 3 The distribution of tokens according to the part of speech
Nouns
Borrowed
Verbs
Adjectives and adverbs
Turkish
Total
Borrowed
Turkish
Total
Borrowed
Turkish
Total
50.6%
49.4%
70.8%
11.5%
88.5%
23%
42.9%
57.1%
6.2%
N=78
N=76
N=154
N=6
N=46
N=52
N=8
N=10
N=14
Uzum and Uzum
Table 4 The frequency of borrowed and Turkish-origin words according to the topic
Religion
Borrowed
Politics
Turkish
History
Borrowed
Turkish
Borrowed
Business and economy
Turkish
Borrowed
Turkish
66.7%
33.3%
40.6%
59.4%
47.8%
52.2%
30.8%
69.2%
N=2
N=1
N=76
N=111
N=11
N=12
N=4
N=9
Turkish Dictionary (2005), they actually have their Turkish-origin equivalents which coexist with the borrowed ones. Table 8 shows the distribution of tokens according to the
languages.
Figure 2 shows the visual illustration of the distribution of tokens according to the
languages of origin and the speakers.
According to the findings shown in Table 8, Islamic conservative group speakers have
the tendency to use Arabic-origin words (42.4%) almost as often as Turkish-origin ones
(48.4%). Turkish nationalist group speakers show a preference to use Turkish origin words
(63.4%) more than other languages. Secular modernist group speakers show a similar
pattern to the latter exhibiting a tendency to use Turkish origin words (78.2%) more often
than the borrowed words. According to these findings Arabic is the most common code to
borrow lexical items from; whereas, borrowings from Persian, French, and other languages
are not as frequent in this dataset.
Another significant factor which is hypothesized to influence the online-borrowings, is
the political orientation of the speaker. As mentioned in the previous section, speakers are
using the opportunity to have access to different codes within the same language. These
codes have given hundreds of words to the Turkish language. However, these borrowed
words were either modified with Turkish affixes or replaced by their Turkish equivalents
during the language reform in 1928. Therefore, the speakers are making choices to choose
Turkish origin words or borrowed ones. Thus, the speakers have the freedom to use
different codes according to their political orientation or other possible variables which are
out of the scope of this study. Table 9 shows how often the speakers do online borrowings
during their speech.
As can be seen from Table 9, there are no significant differences between the speakers in
terms of the frequency they do online-borrowing. While the Islamic conservative speakers
use borrowed words (N=43) slightly less often than Turkish-origin words (N=56), Turkish
nationalist group speakers show a similar pattern with almost equal use of borrowed words
(N=27) and Turkish origin (N=28) words. Unlike these two right wing groups, both the
secular modernist group which includes the left-wing parties, show significant differences
in terms of their preference for Turkish origin words (N=49), rather than borrowed words
(N=23) in their speech.
Table 5 The distribution of tokens
according to stress
Stress on the word
Borrowed
No stress on the word
Turkish
Borrowed
Turkish
61.3%
38.7%
37.9%
62.1%
N=19
N=12
N=74
N=121
The Historical and Linguistic Analysis of Turkish Politician
Table 6 The distribution of tokens according to the position of the word in the sentence
At the beginning
In the middle
Borrowed
At the end of the sentence
Borrowed
Turkish
Turkish
Borrowed
Turkish
46.2%
53.8%
42.9%
57.1%
17.6%
82.4%
N=6
N=7
N=84
N=112
N=3
N=14
As the data were collected from the public speeches of the politicians, the speakers
tended to refer to other political parties from time to time in their speech. When the
speakers are talking about other political parties or their leaders, the pattern of their
borrowing shows some interesting results which are briefly discussed in this paper. Table 10
shows the distribution of tokens according to the area of speech. The categories are: (1)
general area, (2) conservative group, (3) nationalist group, and (4) secular-modernist group.
According to the findings shown in Table 10, the speakers talked about general issues
staying within a general area using more Turkish-origin words (N=43) than borrowed
words (N=34). However, when they get out of the general area and start talking about other
political leaders, the pattern of borrowing shows some differences which were identified
through qualitative and quantitative methods of analysis. Most of the speakers made more
references to the conservative group switching into their imaginary area. When the speakers
from modernist and nationalist groups are within the area of the conservative group, they
used slightly more Turkish words (N=72) than borrowed words (N=54). When the
speakers are talking about the nationalist group, they are showing more preference for
Turkish origin words (N=7) than borrowed words (N=2). In the case of secular-modernist
group, there is also more preference for Turkish-origin words (N=11) than borrowed words
(N=3). This study hypothesizes that the speakers create an imaginary area in which they
reflect their identity through their preferences for Turkish origin or borrowed lexical items.
While some of the speakers show a more obvious pattern which could also be detected
through qualitative analysis, some can only be identified using statistical methods. In the
sentence below (1), there is a sample transcription in which the speaker shifts from the
general area and starts talking about another political leader, thus getting into his imaginary
area and using the borrowings that this political leader is claimed to use more often.
(1)
Bu sorunların altından kalkamayan, bu sorunların altında ezilen ruh haliyle de bu
sorunları kaldıramayacağını açık açık gösteren gösteren başbakanı 22 Temmuz’da
evine göndereceksiniz, istirahat edecek. Tabi bugün yaşanan yolsuzlukların cezasini
çekecek, hesap sorulacak. Bundan kimsenin şüphesi olmasın. (Italicized words are
borrowed from Arabic).
This speaker belongs to the secular-modernist group, and thus is normally using more
Turkish-origin words (68.1%) than borrowed words (31.9%) in this data set. However, his
Table 7 The distribution of the tokens according to the pauses
Preceding Pause
Borrowed
Following Pause
No pause
Turkish
Borrowed
Turkish
Borrowed
Turkish
75%
25%
36.4%
63.6%
41.7%
58.3%
N=15
N=5
N=8
N=14
N=75
N=109
Uzum and Uzum
Table 8 The distribution of tokens according to the languages and the speakers
Arabic
Turkish
Persian
French
Other
Islamic conservatives
42.4%
48.4%
2.4%
4.8%
1.8%
Turkish nationalists
25.2%
63.4%
5.6%
4%
1.6%
Secular modernists
15.6%
78.2%
2.6%
4.3%
1.7%
use of Arabic words shows a drastic increase when he enters into the imaginary area of the
Islamic-conservative leader who tends to use more Arabic words in his speech.
Another example (2) is from the speech of the Turkish nationalist leader who has a
tendency to use borrowed words at 49% in this dataset.
(2)
Yoksa, aklını, inancını ve milli duygularını mı kaybettin? Avrupa bile ciddiyeti gördü.
Peşmerge bile akıbetini fark etti. Sen hala kavramadın. (Italicized words are borrowed
from Arabic).
In sentences (1) and (2), the speakers address the islamist-conservative leader who was
the former prime minister. The speakers address this leader like talking to him in person and
ask him why he acted passively in national security issues as well as economic concerns.
As can be seen in the statistical findings and the example sentences, there is a tendency to
adapt to the addressed speaker’s area and style of speech. This study suggests evidence for
the existence of an imaginary area in which a speaker reflects his identity, and the other
speakers adapt to that once they are involved. This interesting finding will be investigated
using a larger dataset in a subsequent study.
Conclusion
The above discussion shows that the main source for online lexical borrowings is
linguistically structure of the word, and historically the language reform in 1928 and the
linguistic diversity that followed. Linguistically speaking, speakers do most lexical
borrowings in the category of nouns probably due to two facts: (1) the grammatical
structure of Turkish is different from Arabic, Persian, and French, which makes it less likely
to fit a borrowed verb into a Turkish sentence; (2) these nouns represent some concepts that
came into the language long ago with its language attached. Historically speaking, although
these words were replaced with their Turkish equivalents in 1928, some speakers still show
Fig. 2 The bar graph illustration of
the tokens according to languages
of origin and the speakers
Distribution Among Languages
Percentages
90
80
DB Nationalist
70
ZS Modernist-Secular
60
NE Religious-Conservative
50
40
30
20
10
0
Arabic
Turkish
Persian
Languages
French
Other
The Historical and Linguistic Analysis of Turkish Politician
Table 9 The frequency of borrowings according to the speakers
Islamic conservatives
Borrowed
Turkish nationalists
Turkish
Borrowed
Secular modernists
Turkish
Borrowed
Turkish
43.4%
56.6%
49.1%
50.9%
31.9%
68.1%
N=43
N=56
N=27
N=28
N=23
N=49
preference to use them as evidenced with the descriptive findings above. The speakers do
online lexical borrowings from the languages they associate with their political orientation.
These findings are parallel to the assertion of Balim (1996):
The more conservative or religious press and authors abstain from using Western
terminology and stay within the rules of a more conservative word order, strictly subjectobject-verb. However, the majority of Turkish authors seek to use Turkish words…the
language now reflects the more pluralistic identity of Turkey and Turkish culture. (p.112)
Applying this insight to the current study and merging it with the recent study of Bhatt
(2008) which suggests a third space created with code mixing, it could be argued that the
speakers in this study create an imaginary space for themselves in which they position
themselves with regard to the associated community and the outside community. Other
speakers/actors who trespass this area are also adapting themselves to this private area and
follow the sociolinguistic pattern of the owner. The imaginary space, in which the speakers
negotiate their identities and participate in the meaning making process through political
speech, is undoubtedly built at the nexus of history and linguistics as the analysis of current
study attempted to evidence.
The findings presented in this paper are in the direction to support the assertion that
sociolinguistic patterns reflect and transport social and political changes. This notion is also
reflected in the seminal work of Trudgill (2002). Due to a recently reformed society, the
ongoing process of modernization in Turkey seems to coincide with the resistance from
more conservative grounds. The power relations between these political groups take several
forms and are observed in several aspects of life. The same play has been performed on the
same stage with different actors since the language reform in 1928. Having been established
with modernist goals and started by a secular government in 1923, Turkey has seen
different groups of leaders with different political orientations over the past 85 years. Given
the fact that historical and political changes are inevitably mirrored in sociolinguistic
changes, Turkey and Turkish language is going through another process of change due to
the current conservative government as well as its future integration into the European
Union. Further studies could investigate the sociolinguistic change in the society led by
major historical events, political leaders, or the attitudes of society towards these leaders
with respect to the current sociopolitical conjuncture.
Table 10 The distribution of tokens according to the area of speech
General area
Conservative group
Nationalist group
Borrowed
Borrowed
Turkish
Secular modernist
Borrowed
Turkish
Turkish
Borrowed
Turkish
44.2%
55.8%
42.9%
57%
22.2%
77.8%
21.4%
78.6%
N=34
N=43
N=54
N=72
N=2
N=7
N=3
N=11
Uzum and Uzum
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