Dataset Open Access

The #BTW17 Twitter Dataset - Recorded Tweets of the Federal Election Campaigns of 2017 for the 19th German Bundestag

Nane Kratzke

The German Bundestag elections are the most important democratic elections of Germany. This dataset comprises Twitter interactions related with German politicians of the most important political parties over several months in the (pre-)phase of the German election campaigns in 2017. The Twitter accounts of 364 politicians (that is approximately half of the German parliament, the German Bundestag) were followed for almost half a year. The collected data comprise of about 10 GB of Twitter raw data generated by more than 120.000 active Twitter users generating more than 1.200.000 tweets during the pre- and hot-phase of the election campaigns for the 19th German Bundestag. 
The dataset can be used to study how political parties, their followers and supporters make use of social media channels like Twitter in the context of political election campaigns and what kind of content is shared.

The following files contain relevant context information:

  • crawled-pages.json contains the URLs of the official party faction websites of the 18th German Bundestag that were crawled to identify the Twitter screennames of German politicians of all Bundestag factions. Because the Alternative für Deutschland (AfD) and the Freie Demokratische Partei (FDP) were not part of the 18th German Bundestag (but it was likely that they will enter the 19th German Bundestag) other official websites were selected to crawl for relevant and representative politicians for these both parties (in case of the AfD this was the website of the directorate of the AfD federal party and the list of members of the European Parliament, in case of the FDP this was the website of the executive committee of the FDP federal party of Germany).
  • followed-accounts.json contains the (manually checked and edited) crawling result of 327 Twitter screennames of  politicians that have been observed via the Twitter streaming API to collect this dataset.

Funded via general support for research by Lübeck University of Applied Sciences.
Files (1.1 GB)
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Views 2,3302,335
Downloads 501501
Data volume 342.1 GB342.1 GB
Unique views 2,1972,202
Unique downloads 282282


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