The GIP IG Barometer methodology presents a quantitative summary of the main developments in the IG arena based on computational text and data-mining approaches. The IG Terminological Model, encompassing almost 5,000 IG specific words and phrases curated by DiploFoundation’s IG experts and automatically tagged for IG Baskets and Issues was used to annotate all 66 WSIS+10 submissions for non-paper in the scope of this analysis. Then a Knowledge Model based on the statistical modeling of the IGF Session Transcripts 2006-2014 is used to project the typical semantic structure of the IG relevant debates onto the Corpus. The resulting semantically structured, annotated collection of WSIS+10 submissions is additionally statistically modeled to produce a set of quantitative measures that describe the status of the actual debate on the most relevant IG issues (e.g. Cybersecurity, E-Taxation, IoT, Capacity Development, Online Learning, E-Currencies, and many more).
The IG Barometer functions similarly to the human associative memory: not only what IG issues are prima facia present in the contents of the submissions, but all IG issues that are similar (associated) to those explicitly discussed are recalled by the application of the Knowledge Model. Thus, even if a particular IG issue was not explicitly discussed, if the language characteristic of it is present it will be recognized by our computational system.
The IG Barometer basic quantitative summary encompasses four scores:
Relevance. By relying on the IGF Knowledge Model, itself based on the cognitive model of the IGF Session Transcripts 2006-14, the collection of submissions for non-paper is first dissected into a number of IG relevant issues whose relevance relative to the whole IG debate is then assessed. The relevance score describes the relative importance of each IG issue in the present moment.
Specificity. The most important words and phrases used in the debate on each IG issue are studied in respect to their overall frequencies of use. The more unique their usage in a particular IG issue relative to the whole IG debate, the language used to discuss the respective IG Issue is considered to be more specific. The specificity score describes the degree of linguistic, semantic specialization of the debate in a particular IG issue.
Diversity. The diversity score measures the variation that is present in the use of the most specific IG relevant words and phrases used to discuss a particular IG issue. Imagine a debate in which all stakeholders use the same or similar terms in a more or less similar manner, and a parallel debate where one can detect two or more groups that differ in the way they make use of the most relevant words and phrases. While the later debate is considered to be diversified, the former is said to be less diversified.
Positivity. Sentiment analysis - a psychologically-based automated method of considering the presence of emotionally charged words in a collection of text documents to estimate the overall affective tone of the discourse - is performed over the collection of 66 WSIS+10 submissions for non-paper. The positivity score, indicating the degree upon which the present debate is charged with more positive emotions, is computed for each IG relevant issue.
All IG Barometer scores are expressed as percentile ranks, implying that there will be some particular IG issue that scores a maximum of 100% points on a particular scale each month (the percentile rank of a score is the percentage of scores that are equal to or lower than it).
Results for all 66 submissions for non-paper
The most relevant IG issues were: Digital development (97.8%), Online education (95.6%), Telecommunication infrastructure (93.3%), Access (91.1%), and Capacity development (88.9%).
The most specific language was used to discuss: Root Zone (97.8%), E-Money and Virtual Currencies (95.6%), Spam (93.3%), Digital signatures (91.1%), and Domain Name System (88.9%).
The most diversified discussions were on: Rights of people with disabilities (100%), Women's rights online (95.6%), Multilingualism (93.3%), Encryption (91.1%), and Copyright (86.75%).
The most positive tone of discussion was present on: E-Commerce (100%), The Internet of Things (97.8%), Privacy (95.6%), Root Zone (93.3%), and E-Money and Virtual Currencies (91.1%).
The results of sentiment analysis indicate that the affective tone of discussion in the analyzed submissions is generally positive, with an average positivity score of 58%. The most positive affective tone is found in the IEEE submission (81%), followed by Internet Rights and Principles Coalition (80%), Australia (78%), Japan Business Federation (77%), Argentina (76%), and Center for Democracy and Technology (75%). NOTE: this scores are absolute measures, not percentile ranks.
Relevance of various IG Issues for submissions to WSIS+10 Process per stakeholder group
The most relevant IG issues for Private Sector were: Digital development (97.8%), Taxation (95.6%), Online education (93.3%), Telecommunication infrastructrue (91.1%), and Access (88.9%);
the most relevant IG issues for Technical and Academia: Internet Protocol Numbers (97.8%), Online education (95.6%), The Internet of Things (93.3%), Digital development (91.1%), and Capacity development (88.9%);
the most relevant IG issues for Civil Society: Digital development (97.8%), Privacy (95.6%), Human Rights (93.3%), Rights of people with disabilities (91.1%), Access (88.9%);
the most relevant IG issues for Government Sector: Digital development (97.8%), Telecommunication infrastructure (95.6%), Access (93.3%), Human Rights (91.1%), Online education (88.9%); and,
the most relevant IG issues for International Organizations: Digital development (97.8%), Online education (95.6%), Taxation (93.3%), Copyright (91.1%), and Women's rights online (88.9%).
Sentiment analysis for submissions to WSIS+10 Process per stakeholder group
Almost no variation is present in respect to the affective tone used in the submissions made on behalf different stakeholder groups: Private Sector (55%), Technical and Academia (55%), Civil Society (57%), Government Sector (57%), and International Organizations (56%).
The Most Frequently Used Words per Stakeholder Group
Technical and Academia
How different stakeholders used different IG relevant words and phrases in their submissions
We study the way in which 30 most frequently used IG relevant words and phrases were used in the WSIS+10 submissions for non-paper on behalf of various stakeholder groups. Of course, the term WSIS itself is found among these 30 terms. Computational approaches to text-mining enable us to compute the similarity in usage of these words and phrases across the 66 submissions for non-paper that are found in the scope of our analysis. By performing this analysis for various stakeholder groups and tracking the relative positioning of the term WSIS to other IG relevant words and phrases we can illustrate the differences in the understanding of the WSIS process among the stakeholders.
The following conceptual trees depict the similarity in usage of words and phrases by encompassing the more similar among them under the same branches. In the Government tree, we can find the term WSIS grouped under the same branch with development, outcome, and document, while WSIS is grouped together with the words outcome, information society, development and access in the submissions made on behalf of the Civil Society organizations. In the Private Sector tree we can find the term WSIS standing rather isolated from other terms, indicating that the representatives of these sector somehow avoided to explicate the meaning that WSIS has for them. As of the Technical and Academia conceptual tree, WSIS stands close to Internet, stakeholder and development, and in the International Organization tree its closest neighbors are the terms ICT and implement.
By tracking any of the significant IG terms present in these conceptual trees one can similarly determine the context given to each of them on behalf of any stakeholder group.