Word Analysis of 1960 U.S. Presidential Debates

Richard Nixon vs. John F. Kennedy (2nd debate)

7 October 1960

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Introduction

Speaking Turns and Interruptions

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

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.

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

100 –90	5th grade	Very easy to read. Easily understood by an average 11-year-old student.
90 – 80	6th grade	Easy to read. Conversational English for consumers.
80 – 70	7th grade	Fairly easy to read.
70 – 60	8th & 9th grade	Plain English. Easily understood by 13- to 15-year-old students.
60 – 50	10th to 12th grade	Fairly difficult to read.
50 – 30	college	Difficult to read.
30 – 10	college graduate	Very difficult to read. Best understood by college/university graduates.
10 – 0	professional	Extremely 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.

Sentence Size

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.

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.

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).

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

Part of Speech Frequency

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.

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.

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).

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.

Pronoun by Person, Gender and Count

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

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".

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.

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.

Exclusive and Shared Noun Phrase Count and length

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.

Because the index uses the ratio of unique words to all words, it will be larger for longer debates because the fraction of unique words shrinks. Therefore, Windbag Index across debates can only be compared if the number of words is similar.

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 Richard Nixon - all words

Debate Word Cloud for John F Kennedy - all words

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 Richard Nixon

Words exclusive to John F Kennedy

Pronouns for Each Candidate

Word clouds based on only pronouns.

Pronouns for Richard Nixon

Pronouns for John F Kennedy

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

Cloud of verb words, by speaker

Cloud of adjective words, by speaker

Cloud of adverb words, by speaker

Cloud of all words, by speaker

Word Pair Clouds for Each Candidate

Pairs used only once during the debate are not shown.

word pairs for Richard Nixon

word pairs for John F Kennedy

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.