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Found 6 papers, 1 papers with code
Improving Neural Political Statement Classification with Class Hierarchical Information
Many tasks in text-based computational social science (CSS) involve the classification of political statements into categories based on a domain-specific codebook.
Using Hierarchical Class Structure to Improve Fine-Grained Claim Classification
The analysis of public debates crucially requires the classification of political demands according to hierarchical claim ontologies (e. g. for immigration, a supercategory “Controlling Migration” might have subcategories “Asylum limit” or “Border installations”).
A Generalized Approach to Protest Event Detection in German Local News
However, in a second experiment, we show that our model does not generalize equally well when applied to data from time periods and localities other than our training sample.
DEbateNet-mig15:Tracing the 2015 Immigration Debate in Germany Over Time
DEbateNet-migr15 is a manually annotated dataset for German which covers the public debate on immigration in 2015.
An Environment for Relational Annotation of Political Debates
This paper describes the MARDY corpus annotation environment developed for a collaboration between political science and computational linguistics.
Who Sides with Whom? Towards Computational Construction of Discourse Networks for Political Debates
Understanding the structures of political debates (which actors make what claims) is essential for understanding democratic political decision making.
