The app shows how German politicians associate words with "left" or "right" based on ideological in- and out-group narratives and contested concepts. For example, both ideological sides claim the term "freedom."

This question became the topic of my 2nd dissertation paper. I also considered creating an app to communicate the results efficiently and allow you to explore the patterns yourself. I’ve used Shiny for years, but "AI-assisted agentic engineering" (aka vibe coding 😂) really helped here a lot.

Congrats!!

Serienempfehlungen: Fargo, The Sopranos, vielleicht noch Narcos :)

Leben und Tod der DiD

Dissertation track? 😉

“You see, the endless renovation of the Stuttgart train station is a symbol of our late-capitalist condition: the project is always ‘in progress,’ yet nothing ever progresses. The construction site itself becomes the true destination.”

www.instagram.com/p/DQPf_pJiG8...

Is it a fit?

Implications for political behaviour, communication, and representation are manifold, as 'left' and 'right' are central categories in polarised public discourse – which is particularly evident in pejorative usage, such as labelling political opponents as 'racist' or 'socialist'.

Both in- and out-ideological associations are externally validated by serving as seed words to scale parliamentary speeches. The resulting ideal points reflect party ideology across different specifications in the German Bundestag.

The mapping is based on associations from open-ended survey responses in German candidate surveys. Words are mapped into a semantic space using word embeddings and weighted by frequency. Construct validity is ensured by using alternative embeddings and frequency weightings.

Words associated with both left and the right are mapped to the semantic centre, where connotations can vary: 'freedom' has a positive connotation (it is primarily used by the respective in-group to describe left and the right), while 'politics' has a rather neutral connotation.

This framework yields associations that are driven by positive (in-ideology) and negative (out-ideology) associations. Examples: 'justice' (left) and 'patriotism' (right) are in-ideological associations; 'socialism' (left) and 'racism' (right) are out-ideological associations.

Left and right are essential poles in political discourse. We know little about how they are associated across the spectrum. I propose a 2-dimensional model that accounts for both semantics – is a term left or right – and position – are associations coming from the left or right.

Ja, die goldene Twitterzeit ist leider over