Science Daily had this really interesting research from the folks at Carnegie Mellon University:

In the study, the researchers used machine learning to scan the Congressional Record (2012 to 2017) and the presidential debate corpora to isolate linguistic variation between the two political parties. They identified 8,345 words that were part of the Republican corpus and 7,873 with the Democratic corpus.

The results of the four studies showed that even controlling for the dictionary definition of the word, the participants are more likely to associate “Republican language” with Republicans.

Oppenheimer believes the results of the study may skew more Republican because the five-year period of the study coincided with Republican control of the White House and Congress. He also noted that the majority of participants in the four studies self-identified as liberal, and the verbal cues may be stronger and more easily identifiable to those outside the party. In addition, the Congressional Record may not be representative of the variety of political speech people hear on a daily basis, which is more complex and adds context to the language used.

Read the full article here.