Temporal network of information diffusion in Twitter

October 29, 2012
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(This article was first published on Implicit None » R, and kindly contributed to R-bloggers)

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. The data spans 10 days from 03/27 to 04/04 and using the RTs related to the general strike between twitter accounts we build up the following temporal network of information diffusion in Twitter.

Click here to view the embedded video.

Day/night human rhythms are clearly seen, and there is an increase of activity in the evening/night before March 29th, which ended in the burst of RTs during that day. Moreover, using community-finding algorithms over the static (weighted) network of RTs we could assign each twitter account to one of the communities found. Analyzing the text of tweets within those communities we found the nature of the biggest groups: one is in favor of the economic motivations behind the strike, the other is not. Those communities fight close to dominate information propagation in Twitter even some days after the strike.

This video highlights the importance of temporal networks in the analysis of information diffusion in online social networks.

Technical details: the video was done using the amazing igraph package in R and encoded using ffmpeg. Thanks to everyone that contributes to those open-source projects for their work.

Edit (11/9/2012): I have post a tutorial on how to make this kind of visualizations here. Spread the word!

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