A study assessed the usage of words used by politicians in the United States and the United Kingdom to determine the person who uses fewer words to get their message across.
When it comes to speaking, being concise is more effective, revealed a 2021 Microsoft study that showed human attention span has dwindled down to nine seconds.
According to Word Tips, Republican Marjorie Taylor Greene (380.4) and Democrat Nancy Pelosi (394) use the least amount of words when communicating.
Republican J.D. Vance (401.6) comes in third place and president-elect Donald Trump (408.9) clocks in at fourth place, likely due to his “hit first, think later” persuasion skills, said the findings.
Word Tips
The survey found that Republican Ted Cruz (484.9) has the biggest vocabulary with his aptitude for coining terms and using archaic words such as “incendiary.” California Gov. Gavin Newsom (470.4) trails closely behind Cruz.
Kamala Harris (426.6), who’s known for her “word-salad” answers per Yahoo! News, outranks Republican Mike Pence with her lexicon. But she’s no match for Ron DeSantis (428.1), Marco Rubio (429.4) and Mitt Romney (434.3).
Only Democrats Elizabeth Warren (434.4), President Joe Biden (434.4), Pete Buttigieg (442.6) and Republican Mitch McConnell (447.2) outranked Harris with their word count.
Romney’s communication style indicated the most stressed out of U.S. politicians, with 30.86% of his statements showing signs of tension.
Word Tips
Politicians across the pond used more words per average, compared to their U.S. counterparts, the survey showed.
Conservative member of Parliament Suella Braverman leads with the largest vocabulary (480.5), while Labour Party member Diane Abbott (471.4) comes in second place.
There are more U.K. politicians between the 450 and 470 range, while that same range’s American counterpart has only one person: Robert F. Kennedy Jr. (460.5).
In the U.K., Michael Gove of the Labour Party and Braverman are the least stressed figures, known for their composed communication style.
The survey’s results were assessed by calculating the number of unique words used per 1,000 words and using HuggingFace, a natural language processing AI algorithm.
