During the last weeks we saw all the different communities generated analyzing the MEPs’ connections on Twitter.
You can find our old posts HERE.
Now it’s time for some conclusions.
In order to do so, we will briefly expose some of our findings on:
- EU media outlets
- EU politicians
- Differences in the communities’ composition
We already explained our methodology in the previous posts.
In case you missed it, this time you will find it at the end of the article.
EU Media outlets
In the table below we compare the number of MEPs following some of the most popular EU media outlets. Data are the ones we analyzed at the beginning of October, and remember that nothing (especially on Twitter) is set in stone. These media outlets are quite different one to each other, but they all represent specialized news sources on what’s happening at the EU level.
Among EU journalist, the first one is Peter Spiegel (@SpiegelPeter) with 171 MEPs following him, with newsletter-man @PoliticoRyan in second place (164). No other journalist has more than 100 MEPs among his/her followers.
Analyzing the geographic distribution: the map shows the most central EU-media-account in each community.
The match here is between EurActiv (France and Germany) and POLITICO Europe (Netherlands and Austria), but it’s also interesting to see how Spanish, Italian and Danish MEPs are relying on local EU-specialized media.
British MEPs, instead, prefer reading the Parliament Magazine (@parlimag). Are they going to miss the MEP Awards after the Brexit? 🙂
Who are the most central EU politicians inside the different country-based-communities? Of course we are excluding MEPs here (so no Martin Schulz in the map).
Well, the President of the European Commission Jean-Claude Juncker almost takes it all, but the British and Dutch MEPs prefer Donald Tusk in his institutional version.
Mr. Tusk is also a prophet in his own home Poland, with his bilingual account @donaldtusk.
Interesting to note, Denmark and Sweden are instead more interested in following what their own Commissioners Margrethe Vestager and Cecilia Malmström have to say.
Differences in the communities’ composition
Analyzing the top 30 central accounts in the different communities, we’ve already seen how some of these groups are focused on media outlets, some on journalists, some others are full of national politicians, while others are not. Here is a summary.
Note: the sum for each communty is not 30 because we are excluding think tanks, EU politicians, EU official accounts etc. from this visualization.
Isn’t SNA (social network analysis) overused?
We are using Twitter as one of the sources to discover informal networks and as a tool to analyze how politicians represent themselves publicly. In cases like those ones we think Twitter overcomes Facebook, since it is the media preferred by politicians, media and journalists.
Our idea is to give useful information to people working with politics, in institutional relations and communications, so if you wish to have a deeper analysis don’t hesitate to contact us.
Social Network analyses have also proven to be still very popular. When we started this research, we thought that since they are now a bit overused, no one would care about this one. But still, it looks like you people can’t get enough of analyzed social network data 🙂
We are then a bit surprised by the success of this MEPs’ connections series (huge thanks to everyone who helped us spreading the word around).
Please remember, social network data are interesting and immediate to understand, but they are not the only interesting data you can work on when analyzing how politicians behave (or, even more, how people is going to vote).
Let us help you discover more interesting information, working together in analyzing less mainstream data 🙂
We are Elif Lab, a data company that provides analyses on politics and institutions, and we are working on a project called ThinkingAbout.EU that analyzes various data coming from the European institutions.
Contact us at email@example.com
We collected a list of 643 Twitter accounts of the Members of the European Parliament (available on our Github) and decided to study who they follow.
Among these accounts we took the first 8000 ones MEPs follow the most and on this network we applied the Louvain algorithm that helps us discover “hidden” communities (a community is a group of accounts with a density of connections higher than the average; the most followed account by the other members of the community is considered the most central one).
We noticed that the MEPs tend to follow accounts from their country and in many cases 2 MEPs from the same country have more accounts in common than 2 MEPs from different countries. This results in nation-based communities emerging from the network analysis.