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1. Introduction to the 2002-2004 research

The preparatory activities for the World Summit on Information Society (2003-2005) together with other multilateral initiatives in the ICT field initiated (e.g. UN ICT Task Force, Dot Force, Global Knowledge Partnership) initiated a process of development of a specialised language of ICT diplomacy. This language emerged through the interplay between different institutional and professional cultures (traditional diplomacy, information and telecommunication sector, civil society, business circles, etc.). The main aim of this research project - which ran from 2002 to 2004 - was to: (a) identify the main features of the ICT diplomatic language (b) identify linguistic patterns (c) analyse communication among different professional cultures. Our study on the Emerging Language of Internet Diplomacy continues with data analysis of IGF transcripts (2006 until 2015, with preparations for IGF 2016 analysis), analysis of Internet-related instruments, and that of online media.

In this 2002-2004 research, we focused on comparative analysis of the five reports from the WSIS regional preparatory conferences (Africa, Europe, Asia-Pacific, Americas and Western Asia). Through this analysis we tried to identify emergining language patterns and identify cultural differences among various world regions approaching the same issue. Besides various quantitative analysis, declarations were analysed through the use of our ICT methodology which at the time consisted of five baskets: governance and standardisation basket, legal basket, development basket, commercial basket, socio-cultural basket. Today, the taxonomy has evolved into seven baskets, grouping over 40 Internet governance issues. The five analysed declarations were:

 

2. Document length and word/paragraph ratio

Below are the results of the statistical analysis of the basic features of five declarations on Information Society development. We were interested in the length of the documents, number of words and paragraphs, etc. This kind of analysis may seem obvious and non-interestening at the first sight: on the contrary, we argued that even the basic features of diplomatic texts formulated in different world regions uncover some significant differences in the way that the concept of Information Society was being understood.

Quantitative information on the length of these different documents:

 

Africa

Europe

Asia

Americas

W. Asia

words

2675

1993

2993

3517

3810

paragraphs

94

27

70

46

47

characters

17485

13916

10764

24532

23148

 

Table 1. The length of the declarations in words, paragraphs and character

 

 

 

Among many different aspects of the texts of these four declarations, the first we are about to present is the ratio between the number of words and number of paragraphs per declaration. This ratio tells as about the average number of words per paragraph and is interesting because of the difference which appears between the declaration texts formulated in different cultures.

 

 
Words
Paragraphs
Words/Paragraphs
Africa
2675
94
28.46
Europe
1993
27
73.81
Asia
2993
70
42.76
Americas
3517
46
76.46
W. Asia
3810
47
81.06
Table 2. The word/paragraph ratio.

abla

Probably, this result has its roots in culturally determined linguistic habits in formal document writing. We can show that there is no systematic relationship between the length of the document and the number of words per paragraph.

Obviously, there is no systematic relationship between the two; the value of the Pearson' s R correlation coefficient is 0.33 and not statistically significant.

At the moment, It is hard to guess what particular factors influence the words per paragraphs ratio. It is clear from the depicted relationships that both of the declarations formulated in the "western world" (Europe and America) have a large number of words per paragraph, but the Western Asian declaration also do. On the other hand, the African and Asian declaration show significantly lower words per paragraph value. Along with the development of the WSIS preparatory activities, the sample of declarations and documents will grow, and more elaborate analyses will be possible. Since the words per paragraph ratio is not systematically related to the document length, we state again our assumption that it is related with cultural factors in the first place.

 

3. Key concepts

Now we will examine the content of the declarations. We counted the frequency of the occurrence of the key concepts and keywords in I-Society development in all five documents. First, we will present this data, and then present the multivariate analyses based on the observed correlations of occurrence of key concepts and keywords in the documents, followed by the appropriate interpretation.

Seven key concepts in I-Society development where chosen for the analysis: Information Society, Civil Society, Digital Divide, Human Rights, Capacity Building, Sustainable Development and Private Sector.

We present the frequency distributions of the key concepts occurrences in all five documents.
 

 

Africa

Europe

Asia

Americas

W. Asia

Information Society

22

20

16

37

13

Civil Society

29

3

3

4

4

Digital Divide

10

2

1

4

2

Human Rights

0

0

0

4

1

Capacity Building

0

0

1

2

0

Sustainable Development

0

2

0

0

0

Private Sector

12

9

5

3

9

Table 3: The frequency of the key concepts occurrences in five declarations.
 

 
The frequency of key concepts occurences in five analyzed documents is now used as the central information in the description of their content. The graph above represents these frequency distributions by using a different colour for each document. The coloured profiles at the graph can be understood as a representations of each document according to the frequency distributions of key concepts in their content.

We will now present the results obtained from multivariate analyses of the correlation data based on the occurrences of the key concepts in these five different documents. We describe the procedure in details:

  • Two correlation matrices were calculated. The first correlation matrix contains the correlations between the frequencies of key concepts occurrences in the same declaration (the correlations between columns in Table 3.). The second correlation matrix is based on the same data from Table 3, but contains the correlations between the frequencies of key concepts occurrences across different documents (the correlations between rows in Table 3.).
  • These correlation matrices will present the input for statistical analyses by means of multivariate statistical methods. These correlations can be thought of as a meassure of similarity among the objects of the analyses. Take for an example the declarations correlation matrix (Matrix 1, shown bellow). What does the numbers tell? The exact interpreation is the following: the more similar two key concepts distributions are, the higher the apsolute value of the correlation in the declarations matrix. The statistic which is used to express the correlation among variables is Pearson's R coefficient of linear correlation (Pearson's R can not be directly interpreted as a measure of similarity and in some cases must be proprely transformed prior to multivariate analyses).
  • Exactly the same logic can be applied if instead of calculating the correlations among the distributions of key concepts per declaration one decides to caculate the correlations among the distributions of declarations per key concept. These correlations are presented in Matrix 2. The objects of analysis are now different: in Matrix 1, the objects of the analysis are documents (declarations), while in Matrix 2 the objects are key concepts.
  • Hereby we present both correlation matrices: 

 

 
Africa
Europe
Asia
Americas
W. Asia
Africa
1
0.56
0.60
0.49
0.65
Europe
0.56
1
0.98
0.90
0.96
Asia
0.60
0.98
1
0.95
0.93
Americas
0.49
0.90
0.95
1
0.80
W. Asia
0.65
0.96
0.93
0.80
1
Matrix 1: Correlations - the declarations matrix

 

 
I-Society
Civil Society
Digital Divide
Human Rights
Capacity Building
Sustainable Development
Private Sector
I-Society
1
0.04
0.29
0.82
0.76
-0.10
-0.52
Civil Society
0.04
1
0.96
-0.29
-0.36
-0.27
0.68
Digital Divide
0.29
0.96
1
-0.04
-0.18
-0.28
0.55
Human Rights
0.82
-0.29
-0.04
1
0.81
-0.32
-0.69
Capacity Building
0.76
-0.36
-0.18
0.80
1
-0.38
-0.92
Sustainable Development
-0.10
-0.27
-0.28
-0.32
-0.38
1
0.22
Private Sector
-0.52
0.68
0.55
-0.68
-0.92
0.22
1
Matrix 2: Correlations - the concepts matrix

 

  • The multivariate methods of data analysis - multidimensional scaling and cluster analysis - were performed in order to determine the latent data structures underlying these correlation matrices. The results of both analyses will enable us to properly classify the objects of the analyses - e.g. declarations and concepts. Multidimensional scaling and cluster analysis were chosen for two reasons: (a) clear and understandable data representations can be obtained from these analyses, and (b) the sample was too small to perform a valid principal components analysis of the correlation matrices.

 

Results

First we will present the result obtained from multivariate analyses of the correlation matrices described above, than the interpretation of these results. Hereby we present a 3D conceptual space obtained from the multidimensional scaling of the properly (1-r) transformed declarations correlation matrix (Matrix 1). 
 

 
Now we present a joining-tree diagram obtained from cluster analysis of the properly (1-r) transformed declarations correlation matrix (Matrix 1).
 

 
Interpretation
: The cluster analysis of the "declarations matrix" produced the joining-tree diagram shown above. The groupings of the declarations at the diagram can be compared to the distances among them in the conceptual space generated by the procedure of multidimensional scaling. The African declaration is isolated both at the joining-tree and in the conceptual space, while the most similar among the five documents are the European and the Asian document. From the profiles of key concepts distributions shown above, it is clear that the African document is idiosyncrtaic to some extent. It's main distinctive characteristic is the frequent usage of the concepts of civil societydigital divide and private sector. The Americas declaration is also separeted in the conceptual space. In the joining-tree diagram, it is sub-categorized under the same branch as West Asian, Asian and European documents, but at the higher level of linking. When we take a look at the distirbution of key concepts in this declaration we found that it is essentially similar to those declarations, but uses the concept of Information Society more frequently than any other document in the analysis.

The same analyses were performed on the properly transformed correlations from the concepts matrix (Matrix 2). We present the 3D conceptual map produced by the multidimensional scaling procedure, and a joining-tree diagram obtained from cluster analysis.


 

 

Interpretation: Both the inspection of the conceptual space and of the joining-tree diagram reveal two obvious groupings: (a) the concepts of I-Society, Human Rights and Capacity Building, on one side, and (b) the concepts of Civil Society, Digital Divide, Private Sector nad Sustainable Development, on the other. Sustainable Development seems to be less connected to the concepts of Civil Society, Digital Divide and Private Sector than these three concepts appear to be linked among each other. The concept of Sustainable Development is isolated because it appears in only one document - the European declaration. The concepts of Civil Society, Digital Divide and Private Sector are those concepts for which we already know that are frequntly used in the African declaration. The frequency profiles of the concepts of I-Society, Human Rights and Capacity Building seem to be systematically related accross these documents. 

Comments regarding multivariate analyses

The interpretation of the 3D conceptual spaces generated by the multidimensional scaling procedure is dependent on the meaning of the data entered in the analysis. Let's us remind that our data are the correlations between the frequencies of key concepts occurrences in the texts of five declarations on the I-Society development. The closer two points representing the declarations in the space are, the stronger the tendency that the similar distribution of frequencies of the key concepts exists in both documents.

In the case of concepts matrix, the conceptual space contains the I-Society development concepts, not declarations. The objects of the analysis are different, and the interpretation changes accordingly: the closer two points representing the concepts stand, the stronger the tendencies that the concepts they represent appear together in the same documents.

The goal of the cluster analysis is to "search" the representational space of some objects of analysis in order to determine the underlying data structure relying on the distances between the objects in the search space. One can think of the cluster analysis as a mean for the optimal "slicing" of conceptual spaces that we obtain from procedures such is multidimensional scaling. However, cluster analysis performs its search for structure in higher-dimensional spaces than those which result from the multidimensional scaling procedures (the later analysis also starts in a higher-dimensional space, but its goal is to reduce it to a lower-dimensional one, which is then treated as a resulting solution and is prone to interpretation). The interpretation of the joining-tree diagrams is straightforward: they depict the optimal categorization of the analyzed objects (declarations or concepts). It is also directly related to the multidimensional scaling in a following manner: the closer the points are in a conceptual space, the more probably will the objects represented by them be found under the same branch of the hierarchical joining-tree.

 

4. Key words

Beside the key concepts there are certain terms that make considerable language impact. We selected mixture of terms with different purposes: term “internet” was chosen to indicate link between information society and the key dynamical element of this society – Internet. Terms hardware, infrastructure and software were chosen to show level of technical language in five declarations. We also included three prefixes/adjectives which are usually used to describe modern ICT-developments: “e”, cyber and virtual. The analysis of these five documents show that prefix “e-“ has become dominant after its introduction in the Bucharest declaration. “Cyber” and “virtual” are almost non-existing. It is interesting to notice that term “cyber” is used in only international treaty dedicated to the ICT issue – Council of Europe Convention on Cybercrime.

Hereby we present the frequency distributions of keywords occurrences:
 

 
Africa
Europe
Asia
Americas
W. Asia
Internet
1
0
3
1
14
should
37
22
14
12
49
hardware
1
0
1
0
0
infrastructure
8
3
7
5
9
software
4
0
2
4
1
e-
1
18
11
14
32
cyber
1
2
3
3
0
virtual
0
0
0
0
1
Table 4: The frequency of the key words occurrences in five declarations.
 

 
As in the previous analysis of key concepts frequency distributions, we see that the profile of the African declaration is somewhat different than the other declarations' profiles. These difference will be noted following the multivariate analysis. Again, two correlation matrices were calculated, the declarations matrix and the keywords matrix:
 

 
Africa
Europe
Asia
Americas
W. Asia
Africa 
1
0.71
0.74
0.55
0.77
Europe 
0.71
1
0.95
0.94
0.95
Asia 
0.74
0.95
1
0.93
0.94
Americas
0.55
0.94
0.94
1
0.86
W. Asia 
0.77
0.95
0.94
0.86
1
Matrix 3: Correlations - the declarations matrix
 
 
Internet
Should
Hardware
Infrastructure
Software
e-
Cyber
Virtual
Internet
1
0.73
-0.28
0.69
-0.33
0.79
-0.70
0.98
Should 
0.73
1
-0.07
0.68
-0.19
0.40
-0.99
0.78
Hardware
-0.28
-0.07
1
0.42
0.41
-0.74
0.14
-0.41
Infrastructure
0.69
0.68
0.42
1
0.27
0.10
-0.61
0.60
Software
-0.33
-0.19
0.41
0.27
1
-0.66
0.24
-0.38
e- 
0.79
0.40
-0.74
0.10
-0.66
1
-0.42
0.83
Cyber
-0.70
-0.99
0.14
-0.61
0.24
-0.42
1
-0.77
Virtual
0.98
0.78
-0.41
0.60
-0.38
0.83
-0.77
1
Matrix 4: Correlations - the words matrix

Results

Hereby we present a 3D conceptual space obtained from the multidimensional scaling of the properly (1-r) transformed declarations correlation matrix (Matrix 3). 

Now we present a joining-tree diagram obtained from cluster analysis of the properly (1-r) transformed declarations correlation matrix (Matrix 3).

Interpretation: the results of the multivariate analyses of the keywords declarations matrix are consisent with the prior analysis performed on key concepts. The African declaration is isolated from others since it is characterized with the very low frequency of the "e-" prefix when compared to other declarations.

The same analyses were performed on the properly transformed correlations from the words matrix (Matrix 4). We present the 3D conceptual map produced by the multidimensional scaling procedure, and a joining-tree diagram obtained from cluster analysis.


 

Interpretation: Two major groupings of key words occure following the multivariate analysis of the keywords matrix: (a) a group containing the keywords Cyber, Hardware and Software, and (b) a group containg other keywords. The keywords Internet and Virtual are highly connected since the only document containing the keyword Virtual is at the same time the one with the most frequent usage of the keyword Internet (W. Asian declaration).

 

5. Content analysis - Analysis of Semantic Patterns

The content analysis we present here is specific in a way. We used a text-analysis software tool, designed to analyze texts and represent its content through five master variables (semantic factors, so to say): Activity, Optimism, Certainty, Realism and Commonality. These master variables are calculated as linear combinations of a larger number of variables, each calculated on a lexical basis of typical words occurrences. This analysis is performed in order to identify prevailing rhetoric of the declarations. Research in the field of diplomatic language indicates high correlation between existence of certain semantic patterns and effectiveness of the document. For example Vienna Convention on Diplomatic Relations (1961) which is considered to be one of the most applied international treaty shows high level of realism and commonality. In the early stage of the development of the international regime, as it is the case of the WSIS-process, it is expectable to have the current level of distribution of semantic patterns (more optimism – less realism).

The text analysis software used to perform this analysis utilizes a sort of a rather typical discourse processing, based on frequency counts of typical words and subsequent categorization based on previously established semantic criteria. This method utilizes a number of different dictionaries generated from a 20,000 texts sample as a normative basis for its content analysis. The main idea of this sort of discourse processing is to analyze the occurrence of words semantically related to the wider, descriptive categories. 
 

Categories
Africa
Europe
Asia-Pacific
Americas
W. Asia
Activity (language indicating resoluteness, inflexibility and completeness)
51.83
46.2
47.97
48.31
49.68
Optimism (language endorsing some concept, person, group or event and highlighting their positive entailments)
51.25
54.77
51.74
50.88
52.9
Certainty (language indicating resoluteness, inflexibility and completeness)
46.04
50.54
49.56
42.59
51.71
Realism (language describing tangible, immediate, recognizable matters that affect everyday life)
44.7
41.91
42.29
42.66
42.67
Commonality (language highlighting the agreed-upon values of a group and rejecting idiosyncratic modes of engagement)
52.06
49.54
50.79
51.43
50.12


Authors:
Jovan Kurbalija (content analysis)
Goran Milovanovic (statistical analysis)
DiploFoundation, 2003.

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