partycoloR is now on CRAN! Started as a simple idea 6 years ago, now it's a full-featured package. Extract party colors and logos from Wikipedia with one line of code. It's already powering ParlGov Dashboard.

install.packages("partycoloR")

Words like "patriotism" and "racism" are often associated with the right, while "solidarity" and "socialism" are associated with the left.

But who uses these associations, and how do political positions matter?

📊 App: lukas-warode.shinyapps.io/lr-words-map/
📄 Paper: www.nature.com/articles/s41...

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

Is it a fit?

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.

My 2nd dissertation paper is out in @nature.com Humanities and Social Sciences Communications: www.nature.com/articles/s41...

I study and explore how associations with 'left' and 'right' vary systematically by semantic and political position.

1/2 Another year, another @epsanet.bsky.social This year I had two papers accepted, but no visual proof that I presented joint work with Thomas Bräuninger on (the problems of) dynamic scale usage. I hope some nice people will capture 📸 me again next year in the Post-EPSA era @epssnet.bsky.social

I measure attitudes towards left and right using sentiment analysis based on 4 models (dictionary and transformer classifiers) that provide highly correlated estimates.

I use data from 3 GLES candidate surveys (2013, 2017, 2021) using open-ended survey response associations with left and right, as well as party affiliation and left-right self-placement as central variables.

I find that left ideology is viewed more positively by left-leaning elites (SPD, Greens, The Left) than right ideology is viewed by right-leaning elites (CDU and FDP), while left-leaning elites also view right ideology more negatively than right-leaning elites view left ideology.

The first paper of my dissertation has been published in West European Politics!
@wepsocial.bsky.social

See the thread below:

Can Bluesky also replace Instagram?

Feeling funny, might delete later

Shameless repost from evil X:

I finally found some time to finish the introduction for
@OpenDiscourseDE
's R package: open-discourse.github.io/opendiscouRs... The package is technically still under (final) development, while a majority of functions are working consistently.