#Brexitdeal text visualization

OK, not that I am bored, I have probably better things to do. However, given the Brexit agreement between the UK and the EU of today (dd.10/17/2019), I decided to visualize N-grams (word pairs) from the agreement.
Below is the network of these word pairs in the agreement as published here. It represents word pairs that occur more than six times in the agreement. Some obvious word pairs are of course “United Kingdom” and “European Union”, as well as Directives with a particular year mentioned.

More interesting word pairs are “competent authorities”, “animal health”, “motor vehicles”, “national reference laboratories”, “north south cooperation”, and “marketing standards”.

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And now for something completely different: Football and Twitter

So, my studies mostly focus on political communication, journalism and ethnic minorities and migrants in the media. But some time ago, my former student, Leon Mulder, collected cool data on football players’ positions, their scoring capabilities and their basic track record. We then collected their Twitter data. The study attempts to answer the question of what explains online popularity: performance on the football pitch or performance on Twitter? To be honest, we were surprised nobody ever took on this question before. But here we are, a new publication:

Vergeer, M. & Mulder, L. (2019). Football players’ popularity on Twitter explained: Performance on the pitch or performance on Twitter? International Journal of Sport Communication.

The link to the article is https://doi.org/10.1123/ijsc.2018-0171. Below is the abstract.

Abstract:

This study tested football players’ performance on the pitch against their performance on Twitter as explanations for Twitter popularity. Guided by network theory, social-identity theory, and basking in reflective glory and using data of all players of all teams in the Dutch premier league (“Eredivisie”), the multilevel models show that players with a Twitter account were more popular when they scored more goals, were non-Dutch, were on loan at another club, and were networkers actively following others on Twitter. The findings also show that context matters. Players under contract with a successful club receive an automatic bonus: Irrespective of their performance on the pitch or on Twitter, they automatically acquire more followers on Twitter. Players in general do not need to put a lot of effort into communicating on Twitter because sending tweets is unrelated to having more followers. Advertisers’ best options to reach larger and homogeneous audiences through football players are to choose attackers, scoring players, those out on loan, and foreign players, as well as players from successful teams in general. The study also identified which player characteristics do not add to a larger audience reach, such as tweeting behavior and experience on Twitter.

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R workshop in Trondheim, Norway

Recently, I was invited to run a workshop on Quantitative Content Analysis with R and RStudio, thanks to Peter Maurer (NTNU). This all started when we both invited to Canada. Ending up at the hotel bar, talking about political communication, research and everything related. One thing led to another, and last week I ended up in Trondheim. I did my keynote about topic modeling of newspaper articles and social media texts, and an afternoon and morning session on R and quantitative content analysis with a very nice group of (PhD) students.

Again, thanks to Peter Maurer and all the others that made this possible. It was a joy to be in Trondheim!

PS. be sure to check out Rockheim museum, showing the history of Norwegian pop music. Two of my favorite artists are among them: Terje Rypdal and Jan Garbarek. Be sure to check them out.

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