Tag: R rss


14 December 2018 / / Science / R
I started using Twitter more than 10 years ago (!). I open an account in this social network in 2008 and although I was not using it too much for the first year, I become a frequent user after that. It has helped me to get news, information both for my personal and professional interests. But not only that, Twitter has been also the data source for our research, that helped us to investigate the relationship between human behavior in the social platform and paramount problems in our society as information propagation, unemployment, disaster damage, political opinion.
21 December 2015 / / R / Science
A while ago, I wrote a post about how to create animations of temporal networks using R and the amazing package igraph package. The post was written in 2012 and the code does not work with the most recent versions (1.0) of igraph. Here I revisited that post, improving its performance and also making it consistent with the new versions of the package and R. First of all, let me remind you the basic idea: we want to get an animated evolution of a network in which nodes/edges appear (and/or disappear) dynamically.
10 November 2012 / / R / Science
UPDATE: the version of the R code in this post does not work with newer versions of the igraph package (> 1.0). I have posted an updated version of this post here: Temporal networks with R and igraph (updated). Please visit the new post to use the new code and follow the discussion there. In my last post about how a twitter conversation unfolds in time on Twitter, the dynamical nature of information diffusion in twitter was illustrated with a video of the temporal network of interactions (RTs) between accounts.
29 October 2012 / / R / Science
Millions of tweets, retweets and mentions are exchanged in Twitter everyday about very different subjects, events, opinions, etc. While aggregating this data over a time window might help to understand some properties of those processes in online social networks, the speed of information diffusion around particular time-bound events requires a temporal analysis of them. To show that (and with the help of the Text & Opinion Mining Group at IIC) we collected all tweets (750k) of the vibrant conversation around the disputed subject of the general strike of March 29th in Spain.