The Flesch-Kincaid grade level for each of the Presidential debates in 1960, 2008, 2012, 2016 and 2020. Things are getting worse, not better.

Word Analysis of 2020 U.S. Presidential Debates

Trump vs. Biden / Harris vs. Pence


Introduction

2020 Debate Analysis

Trump vs. Biden (1st debate) 29 Sep 2020

Trump vs. Biden (town halls) 15 Oct 2020

Trump vs. Biden (3rd debate) 22 Oct 2020

Trump vs. Biden (combined debate)

Pence vs. Harris 7 Oct 2020

Detailed methods

Download data

2016 Debate Analysis

2016 Clinton vs. Trump Debate word analysis

2012 Debate Analysis

2012 Obama vs. Romney Debate word analysis

2008 Debate Analysis

2008 Obama vs. McCain Debate word analysis

1960 Debate Analysis — the gold standard

1960 Nixon vs. Kennedy Debate word analysis

Other Political Debate Analyses

What Romney's and Obama's Body Language Says to Voters. Watch them cut, point and tilt-and-nod.

He counts your words (even those pronouns), an article in the NYT about Pennebaker's approach to analysis of debates and Al Qaeda communication

Lexical Analysis of Obama's and McCain's Speeches by Jacques Savoy

Presidential word use in State of the Union addresses by Jonathan Corum.

Naming Names, a NYT article about candidates' reference to each other during debates (uses Circos).

Randomly Generated Trump Transcripts

If you want more, get more. The debate continues endlessly with Tripsum: Trump Lorem Ipsum—randomly generated text based transcripts from the 2016 Clinton vs Trump debates.

On these pages, I explore word usage in the 2020 U.S. Presidential debates between Donald Trump and Joe Biden and in the Vice-Presidential debate between Mike Pence and Kamala Harris.

Impatient? Skip to the full word analysis of the 1st U.S. Presidential Debate between Donald Trump and Joe Biden.

Formal debates present a unique opportunity to compare the speech patterns of candidates. The debate's format is controlled — though the debates have been thusfar unruly — and each speaker is subject to the same question (in principle) and is given the same amount of time to respond.

That being said, the dynamics of a debate can be greatly affected by one candidate, who can hijack the conversation and use interruptions to influence their opponent's natural style. Thus, the results of the debate analysis cannot be taken out of the context of the debate.

It's important to stress that this analysis is structural and not semantic. I look in detail of how things are said rather than what is said. However, there is a strong connection between the use of specific words (e.g. pronouns) and the speaker's inner dialogue (Your Use of Pronouns Reveals Your Personality).

I use transcripts from rev.com and explore themes such grade level, readability, sentence size, parts of speech usage, pronoun usage, unique and shared words and use of concepts. And I cannot help but draw some word clouds.

The analysis is fully automated and uses the Natural Language Toolkit for tokenizing, tagging and chunking. All data and word lists (tagged and chunked) are available for download in plain-text format — you are welcome to use these files in any manner.

past years

Results from past years are available: 2008 debate analysis, 2012 debate analysis and 2016 debate analysis. Each year's analysis is a collection of stand-alone pages. For a given year, each of the three Presidential debates and the Vice-Presidential debate results are structured identically.

The analysis for the 2020 debates uses a different part of speech tagging engine (NLTK) than used in previous years (Brill tagger). Keep this in mind if you're comparing the 2020 results to past years.

Methods

Transcripts by the Washington Post for each debate were parsed to extract sections for each speaker, chunk the text into sentences and words, tag each word with its part of speech (tagging), and identify noun phrases (chunking).

The tagged and chunked transcripts are analyzed to determine

I attempt to quantify the overall complexity and repetition by a metric I call the Windbag Index, which is a product of 8 terms each measuring uniqueness in different aspects of speech (more about Windbag Index).

A full description of each of the steps in the analysis is available in the detailed methods section.

The analysis has some limitations.

Results and Commentary

Each debate analysis report contains a lot of data but is shown in exactly the same format, which should help you with making comparisons between debates. To start, you may find these elements the most interesting

Results are shown in a tabular format. From each table you can download the word list used to generate it. This makes it easy to, for example, grab all the adjectives used by Biden or all the verbs that Trump used that Biden did not use.

detailed results – tables, word clouds and commentary

Analysis of Donald Trump vs. Joe Biden (1st debate)

Analysis of Donald Trump vs. Joe Biden (town halls)

Analysis of Donald Trump vs. Joe Biden (3nd debate)

Analysis of Donald Trump vs. Joe Biden (combined debates)

Analysis of Mike Pence vs. Kamala Harris

Visualizing the Debates

Each debate is visualized using tables and word clouds — there's obviously a ton more than can be done. The word clouds visually show the words and their frequency and tables provide detailed statistics. You can download each word list directly from the tables.

tables & basic word clouds

Word usage tables describe the structural characteristics of speech by frequency of words, sentence size, proportion of unique and exclusive words and breakdown of words by part-of-speech • see example
Word clouds for each candidate, categorized by parts of speech.
Word clouds, categorized by ownership.
Word clouds for concepts based on part-of-speech pairs.



Candidates's Word Usage Profiles

Below are a few of the tables available in the full results section.

Readability and Grade Level

The Flesch–Kincaid readability tests are designed to indicate how difficult a passage in English is to understand. There are two tests, the Flesch Reading Ease, and the Flesch–Kincaid Grade Level.

Table 2a
readability
Flesch-Kincaid reading ease and grade level.
speaker grade level reading ease sections sentences words syllables
Donald Trump
3.60
100.0%
3.60
85.88
100.0%
85.88
313
100.0%
313
815
100.0%
815
7,612
100.0%
7612
10,030
100.0%
10030
Joe Biden
4.27
118.6%
4.27
82.74
96.3%
82.74
249
79.6%
249
664
81.5%
664
6,813
89.5%
6813
9,155
91.3%
9155

Hover over fields with (e.g. 155) to download the corresponding data file.

The Flesch-Kincaid grade level is actually a periodic quantity.

Sentence Size

Sentence size with and without stop words.

Table 3
sentence size
Number of sentences spoken by each speaker and sentence word count statistics. Number of words in a sentence is shown by average and 50%/90% cumulative values for all, stop and non-stop words.
speaker number of sentences sentence size
all words stop words non-stop words
Donald Trump
815
815
9.5 12 29
9.45812.00029.000
5.4 7 18
5.3957.00018.000
4.1 5 12
4.0635.00012.000
Joe Biden
664
664
10.4 15 34
10.35115.00034.000
6.0 9 20
6.0329.00020.000
4.3 6 16
4.3196.00016.000
total
1,479
1479
11.9 14 33
11.85914.00033.000
7.7 9 20
7.6819.00020.000
6.2 7 14
6.1787.00014.000

Hover over fields with (e.g. 155) to download the corresponding data file.

Part of Speech

Total and unique nouns, verbs, adjectives and adverbs. The parts of speech are identified by their Penn Treebank tags.

Table 7
part of speech count
Count of words categorized by part of speech (POS).
part of speech
n+v+adj+adv nouns (n) verbs (v) adjectives (adj) adverbs (adv)
Donald Trump
3,042 934
39.5% 30.7%
85151284027520214415266
1,363 512
44.8% 37.6%
851512
1,115 275
36.7% 24.7%
840275
346 144
11.4% 41.6%
202144
218 66
7.2% 30.3%
15266
Joe Biden
2,695 991
39.2% 36.8%
77253061832016114510049
1,302 530
48.3% 40.7%
772530
938 320
34.8% 34.1%
618320
306 145
11.4% 47.4%
161145
149 49
5.5% 32.9%
10049
total
5,737 1,509
39.3% 26.3%
1820845157947442522728186
2,665 845
46.5% 31.7%
1820845
2,053 474
35.8% 23.1%
1579474
652 227
11.4% 34.8%
425227
367 86
6.4% 23.4%
28186

Hover over fields with (e.g. 155) to download the corresponding data file.

Pronoun usage

English has many pronouns. Here is an accounting of pronoun use by 1st (e.g. I, we, our), 2nd (e.g. you, yours) or 3rd (e.g. he, she, his, them) person.

Table 13a
Pronoun by person
Count of pronouns by first, second or third person.
pronoun person
all first second third
Donald Trump
1,188 19
100.0% 1.6%
3917309246910
398 7
33.5% 1.8%
3917
311 2
26.2% 0.6%
3092
479 10
40.3% 2.1%
46910
Joe Biden
812 19
100.0% 2.3%
2296126243811
235 6
28.9% 2.6%
2296
128 2
15.8% 1.6%
1262
449 11
55.3% 2.4%
43811

Hover over fields with (e.g. 155) to download the corresponding data file.

Pronoun contrasts

These tables break pronouns by interesting contrasts. For example, the ratio of singular to plural 1st person pronouns reveals the use of "I/my/myself" vs. "we/our/ours".

Table 14a
1st person pronouns, by count
Count of singular and plural first person pronouns. This table contrasts use of I/my/myself vs. we/our/ours.
pronoun
first first singular first plural
Donald Trump
398 7
100.0% 1.8%
23831534
241 3
60.6% 1.2%
2383
157 4
39.4% 2.5%
1534
Joe Biden
235 6
100.0% 2.6%
12531043
128 3
54.5% 2.3%
1253
107 3
45.5% 2.8%
1043
Table 14b
3rd person pronouns, by count
Count of singular and plural third person pronouns. This table contrasts he/she/his/her/it vs. they/them/theirs.
pronoun
third third singular third plural
Donald Trump
479 10
100.0% 2.1%
29371763
300 7
62.6% 2.3%
2937
179 3
37.4% 1.7%
1763
Joe Biden
449 11
100.0% 2.4%
3487904
355 7
79.1% 2.0%
3487
94 4
20.9% 4.3%
904
Table 14c
Me and you — 1st person singular and second person pronouns
Count of 1st person singular and second person pronouns. This table contrasts me/my/myself vs you/yours/yourself.
pronoun
all 1st singular 2nd
Donald Trump
552 5
100.0% 0.9%
23833092
241 3
43.7% 1.2%
2383
311 2
56.3% 0.6%
3092
Joe Biden
256 5
100.0% 2.0%
12531262
128 3
50.0% 2.3%
1253
128 2
50.0% 1.6%
1262
Table 14d
I, me, myself and my — closer look at 1st person singular pronouns
Count of specific 1st person singular pronouns: I, me, myself and my.
pronoun
all I me myself my
Donald Trump
241
100.0%
188.00042.0000.00011.000
188
78.0%
188.000
42
17.4%
42.000
0
0.0%
0.000
11
4.6%
11.000
Joe Biden
128
100.0%
98.00012.0000.00018.000
98
76.6%
98.000
12
9.4%
12.000
0
0.0%
0.000
18
14.1%
18.000

Windbag Index

The Windbag Index is a compound measure that characterizes the complexity of speech. A low index is indicative of succinct speech with low degree of repetition and large number of independent concepts. A large number suggests a stream of repeating words.

Table 18
windbag index
Windbag Index for each speaker. The higher the value, the more repetitive the speech.
speaker Windbag Index
index value index terms
Donald Trump
1,200
+122.6%
1200.83498673744
0.430 0.301 0.376 0.247 0.416 0.303 0.561 0.984
+2.9% -17.4% -7.7% -27.7% -12.2% -7.9% -2.7% +0.7%
0.4295537104307210.3011174871639990.3756419662509170.2466367713004480.4161849710982660.3027522935779820.5606060606060610.983783783783784
Joe Biden
539
-55.1%
539.391711059201
0.417 0.365 0.407 0.341 0.474 0.329 0.576 0.977
-2.9% +21.1% +8.4% +38.3% +13.9% +8.6% +2.8% -0.7%
0.417285028371890.3647140864714090.4070660522273430.3411513859275050.4738562091503270.3288590604026850.5761589403973510.977011494252874

Word Clouds

Word clouds below are colored by part of speech:   noun   verb   adjective   adverb  

Words exclusive to Joe Biden (not spoken by Donald Trump) in the first debate, colored by part of speech.
Words exclusive to Donald Trump (not spoken by Joe Biden) in the first debate, colored by part of speech.

Word clouds below are colored by speaker:   Trump   Biden   both  

All nouns in debates, colored by contributing speaker (Trump: red, Biden: blue, spoken by both: grey).
All verbs in debates, colored by contributing speaker (Trump: red, Biden: blue, spoken by both: grey).

Discussion

Let's hope that after things get worse, they get better.

Downloads

Content of word list archive and data structure syntax is described in the methods section.

Donald Trump vs. Joe Biden (1st debate) transcript word lists and tag clouds data structure

Donald Trump vs. Joe Biden (town halls) transcript word lists and tag clouds data structure

Donald Trump vs. Joe Biden (3nd debate) transcript word lists and tag clouds data structure

Donald Trump vs. Joe Biden (combined debates) transcript word lists and tag clouds data structure

Mike Pence vs. Kamala Harris transcript word lists and tag clouds data structure

updates

1 Oct 2020. Expanded pronoun analysis to include interesting contrasts.

30 Sep 2020. Results and commentary of 1st debate are posted.

29 Sep 2020. It starts now.

26 Sep 2020. Working on preparing the system for Tuesday's debate.