We record the digital traces of Swiss parliament members in Twitter, quantifying their behavior over time. Brief description goes here. Add sections below if you need more room.

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  • David Garcia,
  • Emre Sarigol

We started from the repository of account data from SoMePolis, and we completed the dataset with a crawl of all the historical activity and the network across 127 members of the parliament. We combined the SoMePolis data provided by the Twitter API to do the following:

1- Measure a time series of party activity in Twitter. We measured activity as the total amount of tweets produced by each party, and popularity as the total amount followers they had at a given date. Rescaling the time series data, we noticed some interesting findings, for example that both activity and popularity growth rates homogeneously grew for most of the parties after the referendum on Feb 9th, 2014.

2- Multilayer analysis of the social network between politicians. We reconstructed the networks of follow, retweet, and mention links among politicians. We can also perceive changes in the breadth of behavior since early 2014. We will use this dataset to measure polarization with respect to party alignment and test if referenda can be causes for higher polarization.

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  • project/the_twitter_parliament.txt
  • Last modified: 2015/09/05 14:39
  • by kohlera