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Article . 2017 . Peer-reviewed
License: CC BY
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License: CC BY
Data sources: UnpayWall
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Article . 2017
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The #BTW17 Twitter Dataset–Recorded Tweets of the Federal Election Campaigns of 2017 for the 19th German Bundestag

Authors: Nane Kratzke;

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

Abstract

The German Bundestag elections are the most important elections in Germany. This dataset comprises Twitter interactions related to German politicians of the most important political parties over several months in the (pre-)phase of the German federal election campaigns in 2017. The Twitter accounts of more than 360 politicians were followed for four months. The collected data comprise a sample of approximately 10 GB of Twitter raw data, and they cover more than 120,000 active Twitter users and more than 1,200,000 recorded tweets. Even without sophisticated data analysis techniques, it was possible to deduce a likely political party proximity for more than half of these accounts simply by looking at the re-tweet behavior. This might be of interest for innovative data-driven party campaign strategists in the future. Furthermore, it is observable, that, in Germany, supporters and politicians of populist parties make use of Twitter much more intensively and aggressively than supporters of other parties. Furthermore, established left-wing parties seem to be more active on Twitter than established conservative parties. The dataset can be used to study how political parties, their followers and supporters make use of social media channels in political election campaigns and what kind of content is shared.

Keywords

election campaign, 19th German Bundestag, Bundestag, Germany, Twitter, dataset, Bibliography. Library science. Information resources, Z

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Average
Average
gold