DATA LAB
An interactive overview of our work on NGI Engineroom mapping the technological and social issues shaping the future of the internet.
Stage 1: Topic identification and synthesis
We started this analysis by casting a wide net, collecting large data sets from lots of different sources, including academic journals, journalism and social media. We wanted to build a fuller picture of the emerging social and technological trends and topics shaping the future of the internet.
and Technological topics.
Stage 2 - Topic Synthesis
Based our topic identification and analysis, these are 10 key challenges we believe will significantly impact the health of the internet in years to come. They can be divided in three categories: topics that cover the key aspects contributing to the resilience of the internet, three that are key to make the internet more inclusive, and lastly aspects central to creating a net that is more democratic.
Keyword relationship
Based our topic identification and analysis, these are 10 key challenges we believe will significantly impact the health of the internet in years to come. They can be divided in three categories: topics that cover the key aspects contributing to the resilience of the internet, three that are key to make the internet more inclusive, and lastly aspects central to creating a net that is more democratic.
A deeper dive reveals the relationship between social issues and technology.
These keywords are frequently paired together.
Stage 3 - Issue mapping
Issue classification
Articles are classified in two dimensions: eu/us, social issue/technology EU axis: Articles from European sources or concerning Europe, residualized on the social issues axis
Social issues axis: Articles containing words from a pre-defined list of social topics based on latent dirichlet allocation, mapping trending words with article type based on number of occurrences.
Sentiment Analysis
The sentiment analysis resulted in a compound score for every paragraph containing a given phrase. The score is calculated from the mean of the valence scores of each word in the paragraph apart from the analysed words themselves, which have been removed from the paragraph's text.
Stage 4 - Trend identification
Application to explore relevant keywords by source. These terms are trending now or were trending in the past.
Common terms: compare keywords trending in all sources Wikipedia: browse normalized page views for multiple language versions
Stage 5 - Meetup analyisis
Inside the EU the largest communities are in the core tech hubs of London, Pais, Muniuch and Berlin.
Vibrant communities can also be found in LIsbon, Warsaw and Dublin.
Meetup Topics - The growth of AI
Meetup Topics - Accross the EU
The figures compare EU member states regarding their shares of meetup groups located in the EU across the 3 different technological areas. The top 3 countries across all categories include Germany (DE), Great Britain (GB), and France (FR). Other countries with strong communities in all areas are: Spain (ES - especially in AI), The Netherlands (NL), and Poland (PL). Ireland (IE) appears in AI communities, Italy in AI and Web, Romania in Web, Sweden (SE) and Belgium (BE) in Agile.
A general trend that can be observed is the expansion of meetup groups to new locations between 2011-2014 and relatively constant differences between countries since then.
Stage 6 - Exploring research hubs
Research hubs by country
Research hubs by institution
The top 2 institutions overall are located in the EU: Karlsruhe Institute of Technology and University of Toulouse. From the top 10, the sole US based institution is Google Inc, while the remaining 7 are all located in China.
Research clusters
These maps show the clusters of institutions whose affiliated researchers were likely to publish together
Research in countries over time
For a better assessment of the EU’s relative position in research, the aggregate results of the 28 EU member countries is compared to other countries.
The figures show that the overall EU28 research output is the largest across all categories.
The top 10 contributors to ACM are: EU28, US, China, Japan, India, Taiwan, Australia, South Korea, Canada, Taiwan and Brazil.
Stage 7 - Our trending keywords on Wikipedia
These maps show the networks of connected keywords”