Wahlkampf im Netz: Policy oder Person? Eine inhaltsanalytische Untersuchung der Twitter-Accounts im Bundestag vertretener Parteien vor der Bundestagswahl 2021

Wurthmann, L. Constantin; Hagemann, Daniel C.

doi:10.1007/978-3-658-39008-2_2

Search citation statements

Order By: Relevance

Paper Sections

Select...

Orcid Id1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Book1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…11. The data was created for other recent studies (Wurthmann, 2023;Wurthmann and Hagemann, 2023). We are thankful to the authors for making the data available.…”

Section: Orcid Idmentioning

confidence: 99%

Semantic temporality analysis: A computational approach to time in English and German texts

Watanabe,

Sältzer

2023

Research & Politics

View full text Add to dashboard Cite

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.

show abstract

“…11. The data was created for other recent studies (Wurthmann, 2023;Wurthmann and Hagemann, 2023). We are thankful to the authors for making the data available.…”

Section: Orcid Idmentioning

confidence: 99%