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Word Analysis of 2020 U.S. Presidential Debates

Donald Trump vs. Joe Biden (3nd debate)

22 October 2020



Introduction

Coming soon.

Speaking Turns and Interruptions

Here, I look at the length of each turn of uninterrupted speech.

Table 1
length of sections in words
The number of uninterrupted deliveries (sections), mode/median/mean length of sections in words, and the shortest section length in words that composed 10%, 50% and 90% of the debate.
speaker sections section length debate contiguity (L10 L50 L90)
Donald Trump
191
191
5.0 19.0 42.2
5.00019.0000000042.230
23 89 185
23.00089.000185.000
Joe Biden
128
128
2.0 33.5 56.5
2.00033.5000000056.523
53 118 179
53.000118.000179.000

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

Table 1
legend
a b c
51025

a — section length (mode), shortest section length in 10% of debate

b — section length (median), shortest section length in 50% of debate

c — section length (mean), shortest section length in 90% of debate

bar — proportion of a:b:c

Table 1
commentary

Coming soon.

Flesch-Kincaid Reading Ease and Grade Level

The Flesch-Kincaid reading ease and grade level metrics are designed to indicate how difficult a passage in English is to understand.

This metric does not take repetition into account. A grade level 10 sentence that is repeated 100 times still generates the same metrics because the words per sentence and syllables per word remain constant. To measure how many times a speaker repeats themselves, I use my Windbag Index, below.

Reading ease ranges from 100 (easiest) down to 0 (hardest) and can be interpreted as follows

100 –905th gradeVery easy to read. Easily understood by an average 11-year-old student.
90 – 806th gradeEasy to read. Conversational English for consumers.
80 – 707th gradeFairly easy to read.
70 – 608th & 9th gradePlain English. Easily understood by 13- to 15-year-old students.
60 – 5010th to 12th gradeFairly difficult to read.
50 – 30collegeDifficult to read.
30 – 10college graduateVery difficult to read. Best understood by college/university graduates.
10 – 0professionalExtremely difficult to read. Best understood by college/university graduates.

The grade level corresponds roughly to a U.S. grade level. It has a minimum value of –3.4 and no upper bound.

Two sets of readability scores are calculated. One for the entire debate and one that only considers section with at least 9 words.

Table 2a
readability — entire debate
Flesch-Kincaid reading ease and grade level.
speaker grade level reading ease sections sentences words syllables
Donald Trump
4.14
100.0%
4.14
83.43
100.0%
83.43
191
100.0%
191
796
100.0%
796
8,066
100.0%
8066
10,785
100.0%
10785
Joe Biden
5.65
136.5%
5.65
77.63
93.0%
77.63
128
67.0%
128
558
70.1%
558
7,235
89.7%
7235
9,924
92.0%
9924

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

Table 2b
readability — excluding short sections
Flesch-Kincaid reading ease and grade level for sections with at least 9 words.
speaker grade level reading ease sections sentences words syllables
Donald Trump
4.32
100.0%
4.32
82.93
100.0%
82.93
139
100.0%
139
740
100.0%
740
7,835
100.0%
7835
10,480
100.0%
10480
Joe Biden
5.97
138.2%
5.97
76.74
92.5%
76.74
85
61.2%
85
515
69.6%
515
7,088
90.5%
7088
9,729
92.8%
9729

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

Table 2
legend
a
b
30

a — value for candidate

b — value relative to Donald Trump

bar — proportion of a

Table 2
commentary

Coming soon.

Sentence Size

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 stop non-stop
Donald Trump
797
797
10.2 14 31
10.20714.00031.000
5.9 8 18
5.9258.00018.000
4.3 6 13
4.2826.00013.000
Joe Biden
559
559
13.1 19 39
13.06619.00039.000
7.5 11 23
7.49411.00023.000
5.6 8 17
5.5728.00017.000
total
1,356
1356
13.4 17 36
13.38617.00036.000
8.6 11 22
8.57211.00022.000
6.8 8 16
6.8148.00016.000

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

Table 3
legend
a b c
51025

a — average sentence size

b — largest sentence size for 50% of content

c — largest sentence size for 90% of content

bar — proportion of a:b:c

Table 3
commentary

Coming soon.

Word Statistics

Debate Word Count

Summary Word Count

The summary word count reports the total number of words and the number of unique, non-stop words used by each candidate. Word number is expressed as both absolute and relative values.

Table 4a
all words
Number of all words and unique words used by each speaker.
set word count
Donald Trump
8,135 1,176
52.7% 14.5%
69591176
Joe Biden
7,304 1,293
47.3% 17.7%
60111293
total
15,439 1,874
100.0% 12.1%
135651874

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

Table 4b
exclusive and shared words
Words exclusive to speaker (e.g. speaker A but not speaker B) and shared by speakers (speaker A and B).
set word count
Donald Trump
1,044 581
12.8% 55.7%
463581
Joe Biden
1,062 698
14.5% 65.7%
364698
both candidates
13,333 595
86.4% 4.5%
12738595

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

Table 4
legend
a c
b d
3010

a — word count

b — word count, as fraction in total in debate

c — unique words in (a)

d — unique words in (a), as fraction in (a)

bar — proportion of (a-c):c

Table 4
commentary

Coming soon.

Stop Word Contribution

In the table below, the candidates' delivery is partitioned into stop and non-stop words. Stop words (full list) are frequently-used bridging words (e.g. pronouns and conjunctions) whose meaning depends entirely on context. The fraction of words that are stop words is one measure of the complexity of speech.

Table 5a
non-stop words
Counts of stop and non-stop words.
speaker all words stop words non-stop words
Donald Trump
8,135 1,176
100.0% 14.5%
69591176
4,722 135
58.0% 2.9%
4587135
3,413 1,041
42.0% 30.5%
23721041
Joe Biden
7,304 1,293
100.0% 17.7%
60111293
4,189 143
57.4% 3.4%
4046143
3,115 1,150
42.6% 36.9%
19651150
total
15,439 1,874
100.0% 12.1%
135651874
8,911 149
57.7% 1.7%
8762149
6,528 1,725
42.3% 26.4%
48031725

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

Table 5b
exclusive and shared non-stop words
Non-stop words exclusive to speaker (e.g. speaker A but not speaker B) and shared by speakers (speaker A and B).
set word count
Donald Trump
1,029 575
30.1% 55.9%
454575
Joe Biden
1,036 684
33.3% 66.0%
352684
both candidates
4,463 466
68.4% 10.4%
3997466

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

Table 5
legend
a c
b d
3010

a — total number of words, for a given category (all, stop, non-stop)

b — (a) relative to words in the debate if category=all, otherwise relative to words by the candidate

c — number of unique words with set (a)

d — (c) relative to (a)

bar — proportion of (a-c):c

Table 5
commentary

Coming soon.

Word frequency

The word frequency table summarizes the frequency with which words were used. I show the average word frequency and the weighted cumulative frequencies at 50 and 90 percentile. The average word frequency indicates how many times, on average, a word is used. For a given fraction of the entire delivery, the weighted cumulative frequency indicates the largest word frequency within this fraction (details about weighted cumulative distribution).

Table 6a
word use frequency
Average and 50%/90% percentile word frequencies.
speaker word frequency
all stop non-stop
Donald Trump
6.9 30 233
6.91830.000233.000
35.0 104 237
34.978104.000237.000
3.3 6 30
3.2796.00030.000
Joe Biden
5.6 25 176
5.64925.000176.000
29.3 96 293
29.29496.000293.000
2.7 4 41
2.7094.00041.000
total
8.2 49 404
8.23949.000404.000
59.8 193 526
59.805193.000526.000
3.8 8 53
3.7848.00053.000

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

Table 6b
exclusive and shared non-stop word use frequency
Average and 50%/90% cumulative percentile word frequencies. Non-stop words exclusive to speaker (e.g. speaker A but not speaker B) and shared by speakers (speaker A and B).
set word frequency
Donald Trump
1.79 2 7
1.7902.0007.000
Joe Biden
1.52 2 4
1.5152.0004.000
total
3.78 8 53
3.7848.00053.000

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Table 6
legend
a b c
51025

a — average word frequency

b — largest word frequency in 50% of content

c — largest word frequency in 90% of content

bar — proportion of a:b:c

Table 6
commentary

Coming soon.

All further word use statistics represent content that has been filtered for stop words, unless explicitly indicated.

Part of Speech Analysis

In this section, word frequency is broken down by their part of speech (POS). The four POS groups examined are nouns, verbs, adjectives and adverbs. Conjunctions and prepositions are not considered. The first category (n+v+adj+adv) is composed of all four POS groups.

Part of Speech Count

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
3,158 976
38.8% 30.9%
91551586729620115114370
1,430 515
45.3% 36.0%
915515
1,163 296
36.8% 25.5%
867296
352 151
11.1% 42.9%
201151
213 70
6.7% 32.9%
14370
Joe Biden
2,936 1,081
40.2% 36.8%
7785887463382061596259
1,366 588
46.5% 43.0%
778588
1,084 338
36.9% 31.2%
746338
365 159
12.4% 43.6%
206159
121 59
4.1% 48.8%
6259
total
6,094 1,619
39.5% 26.6%
19158811746501466251234100
2,796 881
45.9% 31.5%
1915881
2,247 501
36.9% 22.3%
1746501
717 251
11.8% 35.0%
466251
334 100
5.5% 29.9%
234100

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

Table 7
legend
a c
b d
1535

a — total number of words for a given POS (all, noun, verb, adjective, adverb)

b — (a) relative to all words by candidate

c — unique words in (a)

d — (c) relative to (a)

bar — proportion of (a-c):c

Table 7
commentary

Coming soon.

Part of Speech Frequency

Table 8
part of speech frequency
Frequency of words categorized by part of speech (POS).
part of speech frequency
n+v+adj+adv nouns (n) verbs (v) adjectives (adj) adverbs (adv) pronouns (pro)
Donald Trump
3.24 6 32
3.2366.00032.000
2.78 4 22
2.7774.00022.000
3.93 9 119
3.9299.000119.000
2.33 3 11
2.3313.00011.000
3.04 6 17
3.0436.00017.000
33.69 133 261
33.691133.000261.000
Joe Biden
2.72 4 41
2.7164.00041.000
2.32 3 22
2.3233.00022.000
3.21 6 127
3.2076.000127.000
2.30 3 16
2.2963.00016.000
2.05 3 8
2.0513.0008.000
24.28 105 153
24.279105.000153.000
total
3.76 8 53
3.7648.00053.000
3.17 6 35
3.1746.00035.000
4.49 13 246
4.48513.000246.000
2.86 5 17
2.8575.00017.000
3.34 7 24
3.3407.00024.000
51.29 238 404
51.292238.000404.000

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Table 8
legend
a b c
51025

a — average word frequency

b — largest word frequency in 50% of content

c — largest word frequency in 90% of content

bar — proportion of a:b:c

Table 8
commentary

Coming soon.

Part of Speech Pairing

Through word pairing, I extract concepts from the text. The number of unique word pairs is a function of sentence length and is one of the measures of complexity.

Table 9a
part of speech pairing — Donald Trump
Word pairs (total and unique) categorized by part of speech (POS)
part of speech pairings - Donald Trump
noun verb adjective adverb
noun
158 89
  56.3%
6989
verb
70 60
  85.7%
1060
7 6
  85.7%
16
adjective
208 163
  78.4%
45163
1 1
  100.0%
01
12 9
  75.0%
39
adverb
4 4
  100.0%
04
35 32
  91.4%
332
8 8
  100.0%
08
4 4
  100.0%
04

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

Table 9b
part of speech pairing — Joe Biden
Word pairs (total and unique) categorized by part of speech (POS)
part of speech pairings - Joe Biden
noun verb adjective adverb
noun
160 114
  71.2%
46114
verb
74 69
  93.2%
569
11 11
  100.0%
011
adjective
202 162
  80.2%
40162
5 4
  80.0%
14
12 9
  75.0%
39
adverb
2 2
  100.0%
02
21 19
  90.5%
219
9 8
  88.9%
18
5 4
  80.0%
14

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

Table 9c
unique part of speech pairing — candidate comparison
Unique word pairs categorized by part of speech (POS)
unique part of speech pairings
noun (n) verb (v) adjective (adj) adverb (adv)
noun
89 114
  128.1%
89
114
verb
60 69
  115.0%
60
69
6 11
  183.3%
6
11
adjective
163 162
  99.4%
163
162
1 4
  400.0%
1
4
9 9
  100.0%
9
9
adverb
4 2
  50.0%
4
2
32 19
  59.4%
32
19
8 8
  100.0%
8
8
4 4
  100.0%
4
4

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Table 9 a,b
legend
a c
  d
3010

a — total number of pairs, for a given category (e.g. verb/noun)

c — number of unique pairs within set (a)

d — (c) relative to (a)

bar — proportion of (a–c):c

Table 9c
legend
a c
  d
50
45

a — unique pairs for Donald Trump

c — unique pairs for Joe Biden

d — (c) relative to (a) (i.e. Joe Biden relative to Donald Trump)

bars — (a) and (c)

Table 9
commentary

Coming soon.

Detailed Part of Speech Tags

You can really get into the weeds here. Parts of speech are counted more granularly in these tables — nouns and verbs are split into classes and many other word types are shown, such as conjunctions and prepositions.

Table 10a
detailed POS tags — nouns and verbs
Count by part of speech tag: NN (noun, singular), NNP (proper noun, singular), NNPS (proper noun, plural), NNS (noun plural), VB (verb, base form), VBD (verb, past tense), VBG (verb, gerund/present participle), VBN (verb, past participle), VBP (verb, sing. present, non-3d), VBZ (verb, 3rd person sing. present)
Penn Treebank part of speech tag
NN NNP NNPS NNS VB VBD VBG VBN VBP VBZ
Donald Trump
783
9.63%
783
361
4.44%
361
9
0.11%
9
346
4.25%
346
436
5.36%
436
406
4.99%
406
195
2.40%
195
138
1.70%
138
454
5.58%
454
271
3.33%
271
Joe Biden
832
11.39%
106.3%
832
250
3.42%
69.3%
250
22
0.30%
244.4%
22
317
4.34%
91.6%
317
459
6.28%
105.3%
459
210
2.88%
51.7%
210
262
3.59%
134.4%
262
134
1.83%
97.1%
134
369
5.05%
81.3%
369
303
4.15%
111.8%
303

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

Table 10b
detailed POS tags — adjectives, pronouns, adverbs and wh-words
Count by part of speech tag: JJ (adjective), JJR (adjective, comparative), JJS (adjective, superlative), PRP (personal pronoun), PRP$ (possessive pronoun), RB (adverb), RBR (adverb, comparative), RBS (adverb, superlative), WDT (wh-determiner), WP (wh-pronoun), WP$ (possessive wh-pronoun), WRB (wh-abverb)
Penn Treebank part of speech tag
JJ JJR JJS PRP PRP$ RB RBR RBS WDT WP WP$ WRB
Donald Trump
346
4.25%
346
33
0.41%
33
27
0.33%
27
1,181
14.52%
1181
100
1.23%
100
545
6.70%
545
12
0.15%
12
2
0.02%
2
33
0.41%
33
66
0.81%
66
58
0.71%
58
Joe Biden
373
5.11%
107.8%
373
33
0.45%
100.0%
33
5
0.07%
18.5%
5
793
10.86%
67.1%
793
104
1.42%
104.0%
104
344
4.71%
63.1%
344
4
0.05%
33.3%
4
1
0.01%
50.0%
1
34
0.47%
103.0%
34
97
1.33%
147.0%
97
79
1.08%
136.2%
79

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Table 10c
detailed POS tags — prepositions, conjunctions, determiners and others
Count by part of speech tag: CC (coordinating conjunction), CD (cardinal digit), DT (determiner), EX (existential there), FW (foreign word), IN (preposition/subordinating conjunction), MD (modal), PDT (predeterminer), POS (possessive ending), RP (particle), TO (to), UH (interjection)
Penn Treebank part of speech tag
CC CD DT EX FW IN MD PDT POS RP TO UH
Donald Trump
315
3.87%
315
145
1.78%
145
659
8.10%
659
19
0.23%
19
747
9.18%
747
135
1.66%
135
7
0.09%
7
15
0.18%
15
47
0.58%
47
233
2.86%
233
11
0.14%
11
Joe Biden
211
2.89%
67.0%
211
115
1.57%
79.3%
115
681
9.32%
103.3%
681
24
0.33%
126.3%
24
755
10.34%
101.1%
755
107
1.46%
79.3%
107
14
0.19%
200.0%
14
21
0.29%
140.0%
21
51
0.70%
108.5%
51
293
4.01%
125.8%
293
7
0.10%
63.6%
7

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Table 10
legend
a
b
c
10

a — total number of words with a given tag

b — (a) relative to all tagged words

c — (a) relative to number of words with this tag used by Donald Trump

bar — proportion of a

Table 10
commentary

Coming soon.

Exclusive and Shared Usage

This section enumerates words that were exclusive to a candidate (e.g. used by one candidate but not the other). This content provides insight into what the candidates' priorities are and reveals differences in perspective on similar topics.

For a given part of speech, the table breaks down the number of words that were spoken by only one of the candidates or both candidates (intersection). The last row includes words spoken by either candidate (union).

Table 11
exclusive word usage
Total and unique words used exclusively by a candidate, or by both.
part of speech
n+v+adj+adv nouns (n) verbs (v) adjectives (adj) adverbs (adv)
Donald Trump
971 538
100.0% 55.4%
15.9% 33.2%
433538
20927911015177862039
488 279
50.3% 57.2%
17.5% 31.7%
209279
209279
261 151
26.9% 57.9%
11.6% 30.1%
110151
110151
163 86
16.8% 52.8%
22.7% 34.3%
7786
7786
59 39
6.1% 66.1%
17.7% 39.0%
2039
2039
Joe Biden
975 643
100.0% 65.9%
16.0% 39.7%
332643
1713478019649951126
518 347
53.1% 67.0%
18.5% 39.4%
171347
171347
276 196
28.3% 71.0%
12.3% 39.1%
80196
80196
144 95
14.8% 66.0%
20.1% 37.8%
4995
4995
37 26
3.8% 70.3%
11.1% 26.0%
1126
1126
both candidates
4,148 438
100.0% 10.6%
68.1% 27.1%
3710438
152222215381333335919929
1,744 222
42.0% 12.7%
62.4% 25.2%
1522222
1522222
1,671 133
40.3% 8.0%
74.4% 26.5%
1538133
1538133
392 59
9.5% 15.1%
54.7% 23.5%
33359
33359
228 29
5.5% 12.7%
68.3% 29.0%
19929
19929
total
6,094 1,619
100.0% 26.6%
100.0% 100.0%
44751619
19158811746501466251234100
2,796 881
45.9% 31.5%
100.0% 100.0%
1915881
1915881
2,247 501
36.9% 22.3%
100.0% 100.0%
1746501
1746501
717 251
11.8% 35.0%
100.0% 100.0%
466251
466251
334 100
5.5% 29.9%
100.0% 100.0%
234100
234100

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

Table 11c
legend
a d
b e
c f
4030
40302015105

a — total number of words in set (e.g. obama \ romney, obama ∩ romney, obama ∪ romney , for a given part of speech

b — (a) relative to all exclusive words in n+v+adj+adv

c — (a) relative to all words in n+v+adj+adv

d — unique words in (a)

e — (d) relative to (a)

f — (d) relative to all unique words in n+v+adj+adv

bar1 — normalized ratio of (a-d):d

bar2 — absolute ratio of (a-d):d for all POS groups (first column) or POS group (other columns)

Table 11
commentary

Coming soon.

Pronoun Usage

This section explores pronoun use in detail. Refer to the methods section for details.

Pronoun Count

Fraction of all words that were pronouns.

Table 12a
pronoun fraction
Fraction of words that were pronouns.
speaker all pronouns
Donald Trump
8,135 1,176
100.0% 14.5%
69591176
1,853 55
22.8% 3.0%
179855
Joe Biden
7,304 1,293
100.0% 17.7%
60111293
1,481 61
20.3% 4.1%
142061
total
15,439 1,874
100.0% 12.1%
135651874
3,334 65
21.6% 1.9%
326965

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

Table 12b
exclusive and shared pronouns
Pronouns exclusive to speaker (e.g. speaker A but not speaker B) and shared by speakers (speaker A and B).
set word count
Donald Trump
5 4
0.1% 80.0%
14
Joe Biden
19 10
0.6% 52.6%
910
both candidates
3,310 51
99.3% 1.5%
325951

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

Pronoun by Person, Gender and Count

Pronoun usage by person (1st, 2nd, 3rd), gender (masculine, feminine, neuter) and count (singular, plural).

Table 13a
Pronoun by person
Count of pronouns by first, second or third person.
pronoun person
all first second third
Donald Trump
1,282 21
100.0% 1.6%
5088201255211
516 8
40.2% 1.6%
5088
203 2
15.8% 1.0%
2012
563 11
43.9% 2.0%
55211
Joe Biden
896 18
100.0% 2.0%
3576123239810
363 6
40.5% 1.7%
3576
125 2
14.0% 1.6%
1232
408 10
45.5% 2.5%
39810

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

Table 13b
Pronoun by gender
Count of pronouns by masculine, feminine or neuter gender.
pronoun gender
all masculine feminine neuter
Donald Trump
406 8
100.0% 2.0%
15641022322
160 4
39.4% 2.5%
1564
12 2
3.0% 16.7%
102
234 2
57.6% 0.9%
2322
Joe Biden
258 6
100.0% 2.3%
1393521081
142 3
55.0% 2.1%
1393
7 2
2.7% 28.6%
52
109 1
42.2% 0.9%
1081

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Table 13c
Pronoun by number
Count of pronouns by singular or plural.
pronoun number
all singular plural
Donald Trump
1,475 41
100.0% 2.8%
9842545016
1,009 25
68.4% 2.5%
98425
466 16
31.6% 3.4%
45016
Joe Biden
1,146 40
100.0% 3.5%
7202438616
744 24
64.9% 3.2%
72024
402 16
35.1% 4.0%
38616

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Table 13
legend
a b
c d
153

a — total number of pronouns, by type

b — unique pronouns in (a)

c — (a) as fraction of all pronouns

d — (b) as fraction in (a)

bar — proportion of (a – b):b

Table 13
commentary

Coming soon.

First and third person pronouns — a closer look

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
516 8
100.0% 1.6%
30742014
311 4
60.3% 1.3%
3074
205 4
39.7% 2.0%
2014
Joe Biden
363 6
100.0% 1.7%
18331743
186 3
51.2% 1.6%
1833
177 3
48.8% 1.7%
1743
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
563 11
100.0% 2.0%
39881543
406 8
72.1% 2.0%
3988
157 3
27.9% 1.9%
1543
Joe Biden
408 10
100.0% 2.5%
25261464
258 6
63.2% 2.3%
2526
150 4
36.8% 2.7%
1464
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
514 6
100.0% 1.2%
30742012
311 4
60.5% 1.3%
3074
203 2
39.5% 1.0%
2012
Joe Biden
311 5
100.0% 1.6%
18331232
186 3
59.8% 1.6%
1833
125 2
40.2% 1.6%
1232
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
311
100.0%
261.00037.0001.00012.000
261
83.9%
261.000
37
11.9%
37.000
1
0.3%
1.000
12
3.9%
12.000
Joe Biden
186
100.0%
143.00018.0000.00025.000
143
76.9%
143.000
18
9.7%
18.000
0
0.0%
0.000
25
13.4%
25.000
Table 14
legend
a b
c d
153

a — total number of pronouns, by type

b — unique pronouns in (a) (if more than one)

c — (a) as fraction of all pronouns

d — (b) as fraction in (a) (if less than 100%)

bar — proportion of (a – b):b

Table 14
commentary

Coming soon.

Pronouns by Category

This table tallies the use of pronoun by category. The categories are personal, demonstrative, indefinite, object, possessive, interrogative, others, relative, reflexive. Note that some pronouns that belong to multiple categories are counted in only one. For a list of pronouns for each category, see the pronoun methods section.

Table 15
Pronouns by cateogry
Count of pronouns by category.
pronoun category
all personal demonstrative indefinite object possessive interrogative others relative reflexive
Donald Trump
1,853
100.0%
1090.000201.000221.00090.00097.00075.00042.00036.0005.000
1,090
58.8%
10837
201
10.8%
1974
221
11.9%
19823
90
4.9%
855
97
5.2%
925
75
4.0%
723
42
2.3%
384
36
1.9%
351
5
0.3%
14
Joe Biden
1,481
100.0%
714.000234.000159.00079.000102.000102.00065.00025.0002.000
714
48.2%
7077
234
15.8%
2304
159
10.7%
13326
79
5.3%
745
102
6.9%
975
102
6.9%
984
65
4.4%
578
25
1.7%
241
2
0.1%
02
Table 15
legend
a b
15

a — total number of pronouns, by category

b — (a) as fraction of all pronouns

bar — proportion of (a)

Table 15
commentary

Coming soon.

Noun Phrase Usage

Noun phrases were extracted from the text and analyzed for frequency, word count, unique word count and richness. Single-word phrases were not counted.

Top-level noun phrases are those without a parent noun phrase (a parent phrase is one that a similar, longer phrase). Derived noun phrases are those with a parent (more details about noun phrase analysis).

The top-level noun phrases can be interpreted as independent concepts. Derived noun phrases can be interpreted as variants on concepts embodied by the top-level phrases.

Noun Phrase Count and length

This table reports the absolute number of noun phrases, which is related to the number of nouns, and their length.

Table 16a
noun phrase count
Counts of noun phrases in words and per noun.
speaker noun phrase count
all top-level
Donald Trump
394 184
100.0% 46.7%
0.00 0.00
210184
382 182
97.0% 47.6%
0.00 0.00
200182
Joe Biden
379 205
100.0% 54.1%
0.00 0.00
174205
355 201
93.7% 56.6%
0.00 0.00
154201

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

Table 16b
noun phrase length
Average and 50%/90% cumulative length of noun phrases, in words.
speaker noun phrase length
all top-level
Donald Trump
2.11 2 3
2.1142.0003.000
2.12 2 3
2.1182.0003.000
Joe Biden
2.12 2 3
2.1212.0003.000
2.13 2 3
2.1302.0003.000

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Table 16a
legend
a d
b e
c f
1070

a — number of noun phrases

b — (a) relative to number of all noun phrases

c — number of noun phrases per noun

d — number of unique phrases

e — (c) relative to (a)

f — number of unique noun phrases per unique noun

bar — normalized ratio of (a–c):c

Table 16b
legend
a b c
102080

a — average noun phrase size, in words

b — largest noun phrase size in 50% of content

c — largest noun phrase size in 90% of content

bar — proportion of a:b:c


Table 16
commentary

Coming soon.

Exclusive and Shared Noun Phrase Count and length

Table 17a
exclusive and shared noun phrase count
Counts of exclusive and shared noun phrases in words and per noun.
speaker noun phrase count
all top-level
Donald Trump
317 156
41.0% 49.2%
161156
324 156
102.2% 48.1%
168156
Joe Biden
312 182
40.4% 58.3%
130182
308 179
98.7% 58.1%
129179
both candidates
144 31
18.6% 21.5%
11331
105 26
72.9% 24.8%
7926

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

Table 17b
exclusive and shared noun phrase length
Average and 50%/90% cumulative length of noun phrases, in words.
speaker noun phrase length
all top-level
Donald Trump
2.14 2 3
2.1422.0003.000
2.14 2 3
2.1392.0003.000
Joe Biden
2.15 2 3
2.1472.0003.000
2.15 2 3
2.1492.0003.000
both candidates
2.00 2 2
2.0002.0002.000
2.00 2 2
2.0002.0002.000

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Table 17a
legend
a c
b d
1070

a — number of noun phrases

b — (a) relative to number of all noun phrases

c — number of unique phrases

d — (c) relative to (a)

bar — normalized ratio of (a–c):c

Table 17b
legend
a b c
102080

a — average noun phrase size, in words

b — largest noun phrase size in 50% of content

c — largest noun phrase size in 90% of content

bar — proportion of a:b:c


Table 17
commentary

Coming soon.

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.

Unlike the Flesch-Kincaid readability metrics, the Windbag Index does not take into account the length of sentences or complexity (e.g. number of syllables) of individual 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
0
+0.0%
0
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
+0.0% +0.0% +0.0% +0.0% +0.0% +0.0% +0.0% +0.0%
<div>3413.000 8135.000</div><div>1041 3413.000</div><div>515 1430.000</div><div>296 1163.000</div><div>151 352.000</div><div>70 213.000</div><div>184 394.000</div><div>182 184</div>
Joe Biden
0
+0.0%
0
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
+0.0% +0.0% +0.0% +0.0% +0.0% +0.0% +0.0% +0.0%
<div>3115.000 7304.000</div><div>1150 3115.000</div><div>588 1366.000</div><div>338 1084.000</div><div>159 365.000</div><div>59 121.000</div><div>205 379.000</div><div>201 205</div>
Table 18
legend
The Windbag Index is 1/(t1*t2*...*t9) where t1,t2,...,t8 are

t1 — fraction of words that are non-stop

t2 — fraction of non-stop words that are unique

t3 — fraction of nouns that are unique

t4 — fraction of verbs that are unique

t5 — fraction of adjectives that are unique

t6 — fraction of adverbs that are unique

t7 — fraction of noun phrases that are unique

t8 — fraction of noun phrases that are top-level


Large individual terms t1...t9 contribute to a smaller index.

The percentage values below the index and each term are relative differences to the other speaker's corresponding term (i.e. 100*(a-b)/b where a is the value for one speaker and b for the other).
Table 18
commentary

Coming soon.

Word Clouds

In the word clouds below, the size of the word is proportional to the number of times it was used by a candidate (method details).

Not all words from a group used to draw the cloud fit in the image — less frequently used words for large word groups may fall outside the image.

All Words for Each Candidate

Each candidate's debate portion was extracted and frequencies were compiled for each part of speech (noun, verb, adjective, adverb), with words colored by their part of speech category.

The distribution of sizes within a tag cloud follows the frequency distribution of words. However, word size cannot be compared between clouds, since the minimum and maximum size of the words is fixed.

Debate Word Cloud for Donald Trump - all words

Debate tag cloud for Donald Trump
Size proportional to word frequency. Color encodes part of speech: noun verb adjective adverb

Debate Word Cloud for Joe Biden - all words

Debate tag cloud for Joe Biden
Size proportional to word frequency. Color encodes part of speech: noun verb adjective adverb
commentary

Coming soon.

Exclusive Words for Each Candidate

The clouds below show words used exlusively by a candidate. For example, if candidate A used the word "invest" (any number of times), but candidate B did not, then the word will appear in the exclusive word tag cloud for candidate A.

Words exclusive to Donald Trump

Debate tag cloud for Donald Trump
Size proportional to word frequency. Color encodes part of speech: noun verb adjective adverb

Words exclusive to Joe Biden

Debate tag cloud for Joe Biden
Size proportional to word frequency. Color encodes part of speech: noun verb adjective adverb
commentary

Coming soon.

Pronouns for Each Candidate

Word clouds based on only pronouns.

Pronouns for Donald Trump

Debate tag cloud for Donald Trump
Size proportional to word frequency. Color encodes pronoun type: masculine feminine neuter 1st person 2nd person singular plural other

Pronouns for Joe Biden

Debate tag cloud for Joe Biden
Size proportional to word frequency. Color encodes pronoun type: masculine feminine neuter 1st person 2nd person singular plural other
commentary

Coming soon.

Part of Speech Word Clouds

In these clouds, words from each major part of speech were colored based on whether they were exclusive to a candidate or shared by the candidates.

The size of the word is relative to the frequency for the candidate — word sizes between candidates should not be used to indicate difference in absolute frequency.

Cloud of noun words, by speaker

Words unique to each candidate (Trump, Biden) and those spoken by both.
commentary

Coming soon.

Cloud of verb words, by speaker

Words unique to each candidate (Trump, Biden) and those spoken by both.
commentary

Coming soon.

Cloud of adjective words, by speaker

Words unique to each candidate (Trump, Biden) and those spoken by both.
commentary

Coming soon.

Cloud of adverb words, by speaker

Words unique to each candidate (Trump, Biden) and those spoken by both.
commentary

Coming soon.

Cloud of all words, by speaker

Words unique to each candidate (Trump, Biden) and those spoken by both.
commentary

Coming soon.

Word Pair Clouds for Each Candidate

Pairs used only once during the debate are not shown.

word pairs for Donald Trump

JJ/JJ by Donald Trump
JJ/RB by Donald Trump
JJ/N by Donald Trump
JJ/V by Donald Trump
RB/RB by Donald Trump
RB/N by Donald Trump
RB/V by Donald Trump
N/N by Donald Trump
N/V by Donald Trump
V/V by Donald Trump

word pairs for Joe Biden

JJ/JJ by Joe Biden
JJ/RB by Joe Biden
JJ/N by Joe Biden
JJ/V by Joe Biden
RB/RB by Joe Biden
RB/N by Joe Biden
RB/V by Joe Biden
N/N by Joe Biden
N/V by Joe Biden
V/V by Joe Biden
commentary

Coming soon.

Downloads

Debate transcript

Parsed word lists and word clouds (word lists, part of speech lists, noun phrases, sentences) (word clouds)

Raw data structure

Please see the methods section for details about these files.