Research data

Twitter accounts of the candidates in the 2023 German state election of Berlin

GESIS, Cologne. Data File Version 1.0.0, https://doi.org/10.7802/2532
Abstract: The research project SPARTA (Society, Politics and Risk with Twitter Analysis; funded by dtec.bw; dtec.bw is funded by the European Union - NextGenerationEU) collected tweets to analyse the 2023 state election campaign in Berlin. German-language tweets and retweets related to the election and its central actors were collected for training purposes of a NLP-model. The Dataset contains the Twitter handles and additional information about the candidates of six parties: CDU, SPD, Bündnis90/Die Grünen, FDP, AfD und Die Linke.
Availability: Free access (with registration)
Subject area: Political Science

Methodology

Date(s) of Data Collection: 2023-01; 2023-02
Geographic coverage: Germany / DE
Geographic coverage (free): [Berlin]
Universe: Candidates of CDU, SPD, Bündnis90/Die Grünen, FDP, AfD and Die Linke
Sampling Procedure: Total Universe / Complete enumeration
Temporal Research Design: cross-section
Mode of Data Collection: Self-administered writings and/or diaries:Web-based
Notes: The research project SPARTA (funded by dtec.bw; dtec.bw is funded by the European Union - NextGenerationEU) carries out real-time analysis of election campaigns as they unfold on Twitter.

Bibliographic information

Primärforschende, Institution: Steup, Johannes; Universität der Bundeswehr München | Riedl, Jasmin; Universität der Bundeswehr München | Neumeier, Andreas; Universität der Bundeswehr München | Drews, Wiebke; Universität der Bundeswehr München
Publication year: 2023
DOI: 10.7802/2532
Study number: SDN-10.7802-2532
Project funder: dtec.bw - Digitalization and Technology Research Center of the Bundeswehr
Publisher: GESIS, Cologne

Versions

Current Version: 1.0.0, https://doi.org/10.7802/2532

Cite

Steup, Johannes, Riedl, Jasmin, Neumeier, Andreas, & Drews, Wiebke (2023). Twitter accounts of the candidates in the 2023 German state election of Berlin. GESIS, Cologne. Data File Version 1.0.0, https://doi.org/10.7802/2532.

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