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Intra-party politics has long been neglected due to lacking data sources. While we have a good understanding of the dynamics of ideological competition between parties, we know less about how individuals or groups inside parties influence policy, leadership selection and coalition bargaining. These questions can only be answered if we can place individual politicians and sub-party groups like factions on the same dimensions as in inter-party competition. This task has been notoriously difficult, as most existing measures either work on the party level, or are in other ways determined by the party agenda. Social media is a new data source that allows analyzing positions of individual politicians in party-centered systems, as it is subject to limited party control. I apply canonical correspondence analysis to account for hierarchical data structures and estimate multidimensional positions of the Twitter accounts of 498 Members of the German Bundestag based on more than 800,000 tweets since 2017. To test the effect of intra-party actors on their relative ideological placement, I coded the faction membership of 247 Twitter users in the Bundestag. I show that Twitter text reproduces party positions and dimensions. Members of factions are more likely to represent their faction's positions, both on the cultural and the economic dimension.

Abstract
The influence of congressional primary elections on candidate positioning remains disputed and poorly understood. We test whether candidates communicate artificially "extreme" positions during the nomination, as revealed by moderation following a primary defeat. We apply a scaling method based on candidates language on Twitter to estimate positions of 988 candidates in contested US House of Representatives primaries in 2020 over time, demonstrating validity against NOMINATE (r > 0.93) where possible. Losing Democratic candidates moderated significantly after their primary defeat, indicating strategic position-taking for perceived electoral benefit, where the nomination contest induced artificially "extreme" communication. We find no such effect among Republicans. These findings have implications for candidate strategy in two-stage elections and provide further evidence of elite partisan asymmetry.

Temporality is an important aspect of political discourse. Politicians and policymakers attempt to construct the past and the future to gain power, legitimize their policies, claim success for themselves and blame others. To make computational analysis of temporality more accessible, we develop a new methodology using a semisupervised machine-learning algorithm called Latent Semantic Scaling. Only with a set of common verbs in the past perfect and future tense as seed words, the algorithm estimates the temporality of all other words. We demonstrate that it can identify temporal orientation of English and German sentences from election manifestos around 60–70% accurately, which is comparable to the results from a recent study based on supervised machine-learning algorithms. We also apply it to Twitter posts by German political parties to reveal temporal orientation of policy issues.

Soziale Medien nehmen gerade unter Corona-Bedingungen eine größere Rolle im Wahlkampf ein. Verbunden mit dem innerparteilichen Kampf um die Spitzenkandidaturen, der Zersplitterung des Parteiensystems und einer personalisierten Kanzlerwahl befördert das individualisierte Medium Twitter die Personalisierung des politischen Wettbewerbs. Für eine Analyse der Bundestagswahl stellt sich die Frage, ob es mit Twitterdaten auch möglich ist, Rückschlüsse auf die individuellen politischen Positionen von Bundestagskandidierenden zu ziehen. Anhand der politischen Kommunikation einiger prominenter Spitzenpolitiker:innen werden die zentralen Dimensionen des politischen Raums auf Twitter sichtbar, insbesondere die Neuausrichtung der traditionellen Links-Rechts Dimension.

The German Federal Election of 2021 will be the most digitalized ever: In times of Covid-19, a younger electorate and omnipresence of user generated content, social media has become an important tool and arena for politics. While collecting data from Twitter's Application Programming Interfaces (APIs) is simple, it is difficult to find meaningful information. To improve social science research with Twitter data, we published The German Federal Election Twitter dataset. It contains 1,556 Twitter accounts of all candidates running for the seven parties represented in the previous session of Bundestag. The data gives researchers, journalists and citizens the ability to observe the election campaign on Twitter. ; Die Bundestagswahl 2021 wird die am stärksten digitalisierte aller Zeiten: Angesichts der Covid-19-Pandemie, einer jüngeren Wählerschaft und der Omnipräsenz von nutzergenerierten Inhalten sind die sozialen Medien zu einem wichtigen Instrument und zu einem zentralen Schauplatz der Politik geworden. Während das Sammeln von Daten aus Twitters "Application Programming Interfaces (APIs)" einfach ist, ist es zugleich schwierig, aussagekräftige Informationen zu finden. Um die sozialwissenschaftliche Forschung mit Twitter-Daten zu verbessern, haben wir den Twitter-Datensatz zur Bundestagswahl veröffentlicht. Er enthält 1.556 Twitter-Accounts aller Kandidat*innen, die für die sieben im Bundestag vertretenen Parteien kandidieren. Die Daten geben Forscher*innen, Journalist*innen und Bürger*innen die Möglichkeit, den Wahlkampf auf Twitter zu beobachten.

GESIS und die German Longitudinal Election Study (GLES) haben für die Bundestagswahl 2021 in Zusammenarbeit mit dem Digital Society Project und Twitter die Social-Media-Accounts der Kandidierenden und deren Posts auf Twitter erhoben. Mit diesem Release veröffentlichen wir Daten für alle Kandidierenden der sieben großen deutschen Parteien (SPD, Bündnis 90/Die Grünen, FDP, CDU, CSU, Die Linke, AfD) und deren Twitter-Accounts. Die Informationen zu den Kandidierenden wurden bereits seit Anfang 2021 gesammelt und mit den vom Bundeswahlleiter veröffentlichten Daten der Kandidierenden abgeglichen.