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Corpus-based conceptualization in sociology: possibilities and limits/Conceptualizacion basada en el corpus en sociologia: posibilidades y limites.

1. Introduction

The problem of the quantitative interpretation of qualitative data is one of the most important in sociological research (Rubtcova & Pavenkov, 2016); Pavenkov & Pavenkova, 2016). Textual analysis has placed emphasis on deep and careful study of texts and how personal strategies embodied in the concepts. However, quantitative interpretation has always been problematic (Usiaeva et al., 2016).

Quantitative operationalization of concepts can improve the objectivity of the data and exclude some erroneous or obscure use of social categories, as well as determine the particular application in different contexts to identify the relevance of social problems (Rubtcova, Pavenkov & Varlamova, 2017). As stated by P. Lazersfeld, operationalisation of concepts can help to create a model that characterizes the social problems studied in the course of empirical social research (Lazarsfeld, 1962). This model is created on the stage of conceptualization of the concept and is used as a base for research operations.

We propose to use quantitative analysis as the basis for building empirical models, which allows identifying the meanings and trends using corpus linguistics: firstly for the count of key words to define the unit of account institutional contexts, secondly for a qualitative analysis of the identified contexts (Rubtsova & Vasilieva, 2016). It allows increasing the objectivity of the research (see e.g. McEnery & Hardie, 2012). Our paper deals with the corpus-based conceptualization method, which can be considered as a method of organizing data using linguistic corpora. The corpus-based conceptualization allows us to establish a closer link with the meaning and identify the whole spectrum of meanings (Halliday, 1975).

First of all, corpus linguistics studies a language not only as a remarkable social phenomenon (e.g. altruism, power, government). The most significant aim is to explain the language characteristics of a term (concept) (Corpora and Discourse, 2008). Corpus does not initially have such intention; however, it creates for sociology some interesting content that extends standard sociological frames of social categories' knowledge (Abulof, 2015). The attention of the linguistic corpus in natural language gives a path to answers of how people actually discuss social reality (Blei, 2012). If standard sociological content analysis requires conceptual study through the subjective descriptions of a sociologist, the material is already presented in a corpus, and sociologists can only use what is available. This increases the level of objectivity in a researche's operationalisation.

Second, a corpus has a meaningful size that allows us to make a big data statistical analysis. The most part of the national corpora contain more than 500 million words (Corpora and Discourse, 2008). Materials are described by years and months over a long period; for example National Corpus of the Russian Language includes the development of language from the 18th to the beginning of the 21st century (National Corpus of the Russian Language, 2015). A researcher can build the frequency of the use of categories over various periods of time (diachronic word frequency): e.g., the max. frequency of use of the concept 'mercy' is in years 2002-2004. The aggregate of phrases also supports the in-depth study of the dynamic context of the concept and its main topics (see, e.g.: Liu, 2012; Abulof, 2015).

Also the main part of national corpora has one principle of structuring that is crucial for cross-cultural studies (Corpora & Cross-linguistic Research, 1998). The corpus linguistics gives us the possibility to recognize differences in the understanding of the social terms in different cultures that should be done before comparative sociological studies (Rawoens, 2010).

Finally, the corpus research is not expensive and time-consuming. It can be done in a user-friendly search format for uploading and downloading data that is compatible with Word, Excel, SPSS etc. (McEnery & Hardie, 2012). So we can do the interpretation procedure quickly. Researchers are excited by the corpus linguistics possibilities because it allows to analyze a huge amount of text inexpensively (Schonhardt-Bailey, 2005)

In our opinion, sociology can lose essential meanings in the research process because of lack of in-deep immersion in the daily life and speech of the people. Context can be understood as institutional frame, which defines the status and role of the concept in social structure. We chose the concepts of "altruism" and "mercy" as examples to demonstrate the corpus-based conceptualisation and its place in sociological research methodology. "Altruism" is defined as the willingness to act selflessly for the benefit of others, without regard to their interests (Pavenkov, Pavenkov, & Rubtcova, 2015). A "mercy" - as the willingness to help someone or to forgive someone out of compassion, humanity (Pavenkov, Shmelev & Rubtcova, 2016).

Specifically, our study answer the following research questions:

- Are the concepts [much less than]altruism[much greater than] and [much less than]mercy[much greater than] interchangeable as synonyms in sociological research?

2. Data and Methodology

2.1 Research Design

The proposed methodology of the study of social categories is the combination of quantitative and qualitative analysis of the encoded array of a Corpus. Quantifying the frequencies of word used was based on expert's context encoding. In accordance with the method, three independent experts performed the encoding. All of them were representatives of St. Petersburg universities, sociologists and were not the article's authors. They offered the following seven contexts: people's actions, humans themselves, quality of relations, state, social institutes, organisations, and conception-ideology. Data processing was carried out using SPSS.

2.2 Sampling Procedures

Data comes from the Russian National Corpus. In Fig.1, 2 we can see the frequency of references to the category of "mercy" in the period of 1800-2014 years. The Russian National Corpus consists of 1802 relevant words, with 775 for altruism and 1047 for mercy. Data processing was carried out by SPSS 19.0. Because Russian has two concepts denoting the social phenomena - 'altruism' and 'mercy' (mutual help), which are considered as synonymous, we will explain their most frequent use in the main, newspaper and spoken Russian Corpora (2016).

3. Results

Both categories are mentioned in the following institutional contexts: "People's actions", "Human himself", "Relations' quality", "State", "Social institutes", "Organisations", "Conceptions and ideology". The studying of the words "altruism" and "mercy", according to these contexts, led to the results discussed below.

As we can see in fig.1 and fig.2 the concept "altruism" is less popular in Russian language, there are 1047 mentions of the mercy and only 309 mentions of altruism in corpora. The statistical analysis showed that the differences in use of these concepts have statistical significance: Pearson Chi-Square p < 0,000 (see Table 1).

The analysis of mentioning of the words "altruism" and "mercy" in distinct contexts shows, that these concepts has different semantic load. The altruism is more often used in context of "relations' quality" and "people's actions", i.e. as personality characteristic; in opposition, the mercy is used in context of "organisations" and "conceptions and ideology", i.e. as social feature (see Table 2 and Figure 3).
Fig. 3. The frequency of references to the category of "altruism" and
"mercy" in different contexts (%)

                           Altruism  miloserdie-mercy

people's actions           23.9%     12.2%
                           19.7%      3.0%
relations' quality         32.7%      4.5%
                            2.3%      1.1%
social institutes           1.6%      5.2%
conceptions and ideology    4.9%     38.7%
                           14.9%     35.3%


Also there are different forms of using of analyzed concepts in spoken corpora and in newspaper: the altruism more typical for speeches, while mercy more frequently used in written sources (see Figure 4).

The altruism in spoken corpora is used in context of "conceptions and ideology" (56% of all mentions in spoken corpora) and "human himself (44%), while in newspapers it increasingly used in context of "relations' quality" (35.6%) and "people's actions" (26.1%). This could means that people treat this concept as a part of ideals, beliefs and principles of social life, but in official sources it is handled as an impulsive cause.

The mercy in spoken corpora is also used in context of "conceptions and ideology" (58%), but in newspapers it is frequently mentioned in context of "organisations" (40%). It possibly means, that this word in written sources is used as reflection of action of charities. At the same time, a significant level of mentions of mercy in newspapers in context of "conceptions and ideology" (40%), shows that this concept has big value for morality and ethical basis of society (see Table 3).

4. Discussion and conclusion

We explored the everyday using of the two Russian words, 'altruism' and 'mercy', which originally had the same meanings. The concepts can be considered as synonymous in questionnaires and guides of interview. Based on an analysis by the Russian National Corpus, we have described seven contexts of word use for 'altruism' and 'mercy' in the following institutional contexts: "People's actions", "Human himself, "Relations' quality", "State", "Social institutes", "Organisations", "Conceptions and ideology". The quantitative analysis shows differences in the use of these concepts. The differences between 'mercy and 'altruism' are statistically significant for all three Russian corpora. Thus, their use as a synonym in sociological research is wrong.

The analysis shows both advantages and disadvantages of the proposed method of corpus linguistics for the operationalization of social categories. The positive side is our possibility to give a description of the context of its usage. We put emphasis on the noun (concept) that avoids the loss of meaning, which the respondents may use in answering the survey questions, and especially in the case of in-depth semi-structured interviews.

However, the proposed method has several disadvantages. Despite the fact that it is aimed at avoiding subjectivity and it is trying to raise objectivity in the operationalization of social categories, the encoding process itself continues to depend on the will of researchers. Existing techniques to reduce subjectivity, such as the coding of several (usually three) independent researchers has the elements of subjectivity because they are usually representatives of one culture and affiliated (related) persons. One attempt to avoid subjectivism is appeal to the Systemic Functional Linguistics (SFL) (Halliday, 1985; Rubtcova & Pavenkov, 2016) and Role and Reference grammar (RRG) (Van Valin, & LaPolla, 1997)

In our example with [much less than]altruism[much greater than] and [much less than]mercy[much greater than], we used conceptualization of a noun, whereas SFL and especially RRG offer us to change the focus on the process (verbal group). Verbal group exists regardless and independent of the investigator and can show the context of the roles of participants. In this case, we can avoid subjectivity in coding and may analyse grammatical structure of sentence. Our suggestions can be used in the preparation of questionnaires, guides, in an analysis of interview transcripts at the first stage. In the following studies, we need to further explore the possibilities of RRG and conduct double coding: with emphasis on nouns (as it is shown in this article) and with an emphasis on verbs (as suggested by RRG).

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Mariia Rubtcova (*), Elena Vasilieva (**) y Vladimir Pavenkov, Oleg Pavenkov (***)

Aceptado: 14-11-2017 / Recibido: 21-02-2017

(*) St. Petersburg State University. Russia

(**) Academy of Science of Republic of Sakha. Yakutia, Russia.

(***) University of Film and Television. Russia.
Table 1. Chi-Square Tests

                              Value         df  Asymp. Sig. (2-sided)

Pearson Chi-Square             424,507 (a)  6   ,000
Likelihood Ratio               409,543      6   ,000
Linear-by-Linear Association   252,619      1   ,000
N of Valid Cases              1356

(a.) 1 cells (7,1%) have expected count less than 5. The minimum
expected count is 4,33.

Table 2. The frequency of references to the category of "altruism" and
"mercy" in contexts

                                      concept            Total
                                      altruism  mercy

                    Count              74        128      202
people's actions
                    % within concept   23,9%      12,2%    14,9%
                    Count              61         31       92
human himself
                    % within concept   19,7%       3,0%     6,8%
relations'          Count             101         47      148
quality             % within concept   32,7%       4,5%    10,9%
                    Count               7         12       19
context state
                    % within concept    2,3%       1,1%     1,4%
                    Count               5         54       59
social institutes
                    % within concept    1,6%       5,2%     4,4%
                    Count              15        405      420
organisations
                    % within concept    4,9%      38,7%    31,0%
conceptions and     Count              46        370      416
ideology            % within concept   14,9%      35,3%    30,7%
Total               % within concept  309       1047     1356
                    Count
                                      100,0%     100,0%   100,0%

Table 3. The frequency of references to the category of "altruism" and
"mercy" in different corpora

                                                 corpora
concept                                          spoken   newspaper

altruism  context   people's      Frequency         0      74
                   actions        % in corpora     ,0%     26,1%
                   human          Frequency      11        50
                   himself        % in corpora   44,0%     17,6%
                   relations'     Frequency         0     101
                   quality        % in corpora     ,0%     35,6%
                                  Frequency         0       7
                   state          % in corpora     ,0%      2,5%
                   social         Frequency         0         5
                   institutes     % in corpora     ,0%      1,8%
                                  Frequency         0      15
                   organisations  % in corpora     ,0%      5,3%
                   conceptions    Frequency      14        32
                   and ideology   % in corpora   56,0%     11,3%
                                  Frequency      25       284
Total                             % in corpora  100,0%    100,0%

concept                   corpora             Total
                  Total   spoken   newspaper
altruism  context
                   74       2      126         128
                   23,9%    2,9%    12,9%       12,2%
                   61       5       26          31
                   19,7%    7,2%     2,7%        3,0%
                  101       4       43          47
                   32,7%    5,8%     4,4%        4,5%
                    7       1       11          12
                    2,3%    1,4%     1,1%        1,1%
                    5       3       51          54
                    1,6%    4,3%     5,2%        5,2%
                   15      14      391         405
                    4,9%   20,3%    40,0%       38,7%
                   46      40       30         370
                   14,9%   58,0%    33,7%       35,3%
Total             309      69      978        1047
100,0%  100,0%   100,0%      100,0%
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Author:Rubtcova, Mariia; Vasilieva, Elena; Pavenkov, Vladimir; Pavenkov, Oleg
Publication:Espacio Abierto: Cuaderno Venezolano de Sociologia
Article Type:Ensayo
Date:Apr 1, 2017
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