Politics

Migration - cross-fraction topoi

Deploy only Raw keywords WP20 vs WP21

Topic filter: speech_topics.topic_label = "Migration". Showing raw keyword density. No stance filtering. Mostly useful as a baseline against the deploy-only view.

Rhetorical pattern AfD
168 speeches in topic		 CDU/CSU
133 speeches in topic		 Linke
82 speeches in topic		 Grüne
139 speeches in topic		 SPD
118 speeches in topic
Gefahrentopos
Migration framed as a security threat. Crime, terror, danger.
 13.7 n=168 6.0 n=133 7.9 n=82 9.5 n=139 6.1 n=118
Belastungstopos
Migration as a burden on the welfare state, housing, schools.
 7.5 n=168 5.4 n=133 6.0 n=82 2.2 n=139 1.2 n=118
Nutzentopos
Migration as a benefit: skilled labour, growth, demographics.
 1.8 n=168 2.2 n=133 3.2 n=82 2.9 n=139 3.6 n=118
Schadentopos
Damage and decline caused by bad migration policy.
 3.3 n=168 1.7 n=133 2.5 n=82 2.8 n=139 1.2 n=118
Humanitätstopos
Humanitarian duty: asylum, refugees, human dignity.
 7.6 n=168 12.7 n=133 15.1 n=82 10.7 n=139 10.8 n=118
Kulturtopos
Cultural identity, integration, national values, language.
 3.1 n=168 1.2 n=133 1.9 n=82 1.3 n=139 2.7 n=118
Rechtsstaatstopos
Rule of law: laws, courts, enforcement, due process.
 33.5 n=168 20.4 n=133 13.4 n=82 11.7 n=139 12.0 n=118
Mißbrauchstopos
Abuse of the asylum system: bogus claims, fraud.
 3.3 n=168 3.1 n=133 0.2 n=82 0.6 n=139 2.7 n=118
FlutMetapher
Flood metaphors: wave, stream, mass. Dehumanising scale imagery.
 2.2 n=168 1.8 n=133 0.4 n=82 0.4 n=139 0.2 n=118
EigenVsFremdgruppe
Us vs them: our people against outsiders or foreigners.
 4.9 n=168 2.6 n=133 2.6 n=82 2.7 n=139 1.6 n=118

Cell value: average count of that rhetorical pattern per 10,000 words, computed only over the speeches where the stance pass labelled this pattern as deploy (speaker uses the frame as their own argument). The number under each value (n=X) is how many speeches contributed. Cells with n < 3 are shown as "n=X" without a density value because the average isn't trustworthy at that sample size. The thin bar under each cell shows the stance split across all speeches that triggered this pattern: orange = deploy, blue = critique, grey = unrelated. Click any cell to read the speeches behind it.