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The EPINetz Twitter Politicians Dataset 2021. A New Resource for the Study of the German Twittersphere and Its Application for the 2021 Federal Elections
This research note introduces the EPINetz Twitter Politicians Dataset, a comprehensive dataset of 2449 Twitter accounts of German parliamentarians, minsters, state secretaries, parties, and ministries on a state, federal, and European Union level for the year 2021. This hand-curated dataset not only...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Fachmedien Wiesbaden
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9199468/ https://www.ncbi.nlm.nih.gov/pubmed/35730060 http://dx.doi.org/10.1007/s11615-022-00405-7 |
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author | König, Tim Schünemann, Wolf J. Brand, Alexander Freyberg, Julian Gertz, Michael |
author_facet | König, Tim Schünemann, Wolf J. Brand, Alexander Freyberg, Julian Gertz, Michael |
author_sort | König, Tim |
collection | PubMed |
description | This research note introduces the EPINetz Twitter Politicians Dataset, a comprehensive dataset of 2449 Twitter accounts of German parliamentarians, minsters, state secretaries, parties, and ministries on a state, federal, and European Union level for the year 2021. This hand-curated dataset not only provides up-to-date information on elected officials, but it also includes additional variables such as their party affiliation, age, and gender. Furthermore, it provides linkages to additional data sources by providing the accounts’ Wikidata and Abgeordnetenwatch (Parliamentwatch) IDs. While it does not provide actual tweet data, the dataset will be a valuable resource for researchers by providing easy access to elected German politicians. We demonstrate some of the dataset’s uses with an analysis of the 2021 German Federal Elections. The full dataset can be accessed via 10.7802/2415. SUPPLEMENTARY INFORMATION: The online version of this article (10.1007/s11615-022-00405-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-9199468 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Fachmedien Wiesbaden |
record_format | MEDLINE/PubMed |
spelling | pubmed-91994682022-06-17 The EPINetz Twitter Politicians Dataset 2021. A New Resource for the Study of the German Twittersphere and Its Application for the 2021 Federal Elections König, Tim Schünemann, Wolf J. Brand, Alexander Freyberg, Julian Gertz, Michael Polit Vierteljahresschr Research Note This research note introduces the EPINetz Twitter Politicians Dataset, a comprehensive dataset of 2449 Twitter accounts of German parliamentarians, minsters, state secretaries, parties, and ministries on a state, federal, and European Union level for the year 2021. This hand-curated dataset not only provides up-to-date information on elected officials, but it also includes additional variables such as their party affiliation, age, and gender. Furthermore, it provides linkages to additional data sources by providing the accounts’ Wikidata and Abgeordnetenwatch (Parliamentwatch) IDs. While it does not provide actual tweet data, the dataset will be a valuable resource for researchers by providing easy access to elected German politicians. We demonstrate some of the dataset’s uses with an analysis of the 2021 German Federal Elections. The full dataset can be accessed via 10.7802/2415. SUPPLEMENTARY INFORMATION: The online version of this article (10.1007/s11615-022-00405-7) contains supplementary material, which is available to authorized users. Springer Fachmedien Wiesbaden 2022-06-15 2022 /pmc/articles/PMC9199468/ /pubmed/35730060 http://dx.doi.org/10.1007/s11615-022-00405-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Note König, Tim Schünemann, Wolf J. Brand, Alexander Freyberg, Julian Gertz, Michael The EPINetz Twitter Politicians Dataset 2021. A New Resource for the Study of the German Twittersphere and Its Application for the 2021 Federal Elections |
title | The EPINetz Twitter Politicians Dataset 2021. A New Resource for the Study of the German Twittersphere and Its Application for the 2021 Federal Elections |
title_full | The EPINetz Twitter Politicians Dataset 2021. A New Resource for the Study of the German Twittersphere and Its Application for the 2021 Federal Elections |
title_fullStr | The EPINetz Twitter Politicians Dataset 2021. A New Resource for the Study of the German Twittersphere and Its Application for the 2021 Federal Elections |
title_full_unstemmed | The EPINetz Twitter Politicians Dataset 2021. A New Resource for the Study of the German Twittersphere and Its Application for the 2021 Federal Elections |
title_short | The EPINetz Twitter Politicians Dataset 2021. A New Resource for the Study of the German Twittersphere and Its Application for the 2021 Federal Elections |
title_sort | epinetz twitter politicians dataset 2021. a new resource for the study of the german twittersphere and its application for the 2021 federal elections |
topic | Research Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9199468/ https://www.ncbi.nlm.nih.gov/pubmed/35730060 http://dx.doi.org/10.1007/s11615-022-00405-7 |
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