The document explores the concept of a future dictionary by examining word usage trends, contestations, and collocations supported by various linguistic resources and case studies. It highlights the evolving meanings of words like 'wholesome' and 'entrepreneur' across different contexts and demographics, particularly in social media. The document also discusses the application of topic modeling to analyze shifts in conversations on themes such as 'Black Lives Matter.'

1. T O WA R D S A D I C T I O N A R Y O F T H E F U T U R E
COUNTS, COMPARISONS,
COLLOCATIONS, CONTESTATIONS

2. DICTIONARY OF THE FUTURE?

5. SOME OTHER PLACES TO CHECK OUT
• The Google Ngram Viewer helps you understand
trends across a bazillion books that Google has
digitized. It’s an amazing resource:
• So are the Corpus of Historical American English:
http://corpus.byu.edu/coha/ (COHA)
• And the Corpus of Contemporary English:
http://corpus.byu.edu/coca/ (COCA)

6. TO COHA!

7. TO COCA!

8. TAKING CARE WITH COUNTS
• The counts in the last two slides are too small to be
anything more than interesting
• The next slide shows us tracking the collocates of
future
• Collocates are the words that appear near a given
word—one of the chief collocates of salt is pepper,
for example

9. COUNTS COUNT

10. DISCUSSIONS, DEMOCRACIES AND
DICTIONARIES

11. What’s going
on in Urban
Dictionary?
• Identity
• Play
• Politics

12. KEYWORDS
• What are the words
that are most
contested?
• How do they
change?
• Who controls the
future?
• Liberty vs. Freedom

13. JACK GRIEVE FINDING WOTY’S
• See also http://idibon.com/quantifying-word-year/

14. • p.s.—in my
ideal
Dictionary of
the Future, we
understand
the geography
of how a word
is used

15. MEANING IS IN THE USE
• “For a large class of
cases of the
employment of the
word ‘meaning’—
though not for all—
this way can be
explained in this way:
the meaning of a
word is its use in the
language” —
Wittgenstein,
Philosophical
Investigations

16. MEANING IN THE USE
• Tumblr moms use
over 4 x’s as many
and
as Twitter peeps
• What are the
collocates?
• Blue: his he him
• Purple: she’s she
• No pink heart option!
• See also http://www.washingtonpost.com/sf/opinions/2015/02/12/why-moms-love-emoji/ and
http://idibon.com/emomji-emoji-new-moms-use/

17. CO-OCCURRENCES MATTER (MOVIE
REVIEW RATINGS AND WORDS)
• The idea here is that if you’re writing a review and use the word wow, you’re being very positive
or very negative. You don’t say Wow, I have a balanced and neutral opinion on this very often.
• If you’re using however, however, you’re likely to be in the middle of your movie review rating or
travel summary—not at the very positive/negative extremes.
• See also http://web.stanford.edu/~cgpotts/manuscripts/potts-schwarz-exclamatives08.pdf and
http://web.stanford.edu/~cgpotts/papers/constant-davis-potts-schwarz-expressives.pdf

18. FOUR CASE STUDIES
• Wholesomeness: http://idibon.com/wholesome-
branding-campaign-effectiveness/
• Entrepreneur: http://idibon.com/entrepreneurs-
french-spanish-english/
• Because X: http://idibon.com/innovating-
innovation/
• #BlackLivesMatter:
http://idibon.com/blacklivesmatter-events-change-
conversations/

19. WHOLESOMENESS
HTTP://I DI BON.COM/WHOLESOME -BRA NDI NG -
CA MPA IGN -EFFECTI VENESS /

20. BRANDS LOVE WORDS

21. DEEP HISTORY
• The first uses of wholesome tended to be about
‘virtuous teachings’.
• In Wycliffe’s Bible way back in 1382:
The..holsum wordis of oure Lord Jhesu Crist. (1 Timothy 6:3)
(Modern versions treat wordis as ‘words’, ‘teachings’, or
‘instructions’.)

22. “WHOLESOME” [NOUN] OVER TIME

23. HOW ABOUT IN SOCIAL MEDIA?
• You have to deal with spam (11% of data in this
case; another 36% of data is “Wholesome Radio”,
which is probably irrelevant)
• In 2014 tweets:
• Food: 23% (but mostly not about Honey Maid)
• Humans: 23% (and how they can/should live; church-
related mentions are prominent)
• Entertainment: 13% (movies, TV)
• Now let’s compare this to 2011 tweet uses:
• Humans: 32%
• Entertainment: 12%
• Food: 9%

24. WORDS ARE CONTESTED

25. MORE ON CONTESTED WORDS
• In the next slide, you’ll see an image from Monroe
et al (2008)
• This is work that takes the basic thing we know:
Republicans and Democrats speak about the same
issue differently.
• In the next slide, they are showing methods that
can pull about how the parties speak about
abortion when they take the floor.
• The words at the top are the Democratic party
words, the ones at the bottom are the Republican
party words.
• http://languagelog.ldc.upenn.edu/myl/Monroe.pdf

27. ENTREPRENEUR
HTTP ://I DI BON.COM/ENTREPRENEURS -FRENCH -SPA NI SH-
ENGLI SH/

28. ENTREPRENEUR IN ENGLISH, FRENCH,
SPANISH
• Tycoon, mogul, industrialist
• A flavor of ‘ill-gotten gains’
• Entrepreuneur doesn’t seem to have this—in English right now
• Collocates have to do with:
• Advice
• Success
• Investors
• Marketing
• Social (media/services/topics/techniques)
• Failure (especially fear-of)
• Lots of named entities (SXSW, Dubai, #KSA, Twitter, Google, LinkedIn,
Etsy)
• The people using entrepreneur identify themselves as
• Authors, speakers, writers, bloggers, strategists, (life) coaches,
consultants, moms, wives, husbands, fathers, food-lovers, music-lovers

29. KEY: GET COMPARISON SETS
Group/Context
A
Group/Context
B

30. INTERCONNECTED AXES OF
DIFFERENCE
• Genre (State of the Unions vs. Reddit comments)
• Time (1940s vs. the last ten years)
• Geography (hella vs. wicked)
• Traditional demographics (age, gender, education)
• Personal identity/style (nerd, goth, bro, mom)

31. BECAUSE X
HTTP://IDIBON.COM/INNOVATING -INNOVATION/

32. INNOVATIONS AND THEIR
COMMUNITIES
• Because X’ers
disporportionately like:
• YouTube
• Tumblr
• One Direction (especially
Harry)
• Justin Bieber
• Ariana Grande
• “bands”
• pizza
• sex
• cats
• books
• They are decidedly less likely
to talk about
• software
• basketball
• NASCAR
• business
• words associated with African-
American Vernacular English

33. THEXINBECAUSEX

34. Part of speech Word counts ≥ 50
Noun (people, spoilers) 32.02%
Compressed clause
(ilysm)
21.78%
Adjective (ugly, tired) 16.04%
Interjection (sweg, omg) 14.71%
Agreement (yeah, no) 12.97%
Pronoun (you, me) 2.45%
PART OF SPEECH TAGGERS ARE GOOD
• There’s even a pretty good one for Twitter POS

35. INNOVATIONS CLUMP

36. #BLACKLIVESMATTER
HTTP://I DI BON.COM/BLA CKLI VESMA TTER -EVENTS -
CHA NGE -CONVERSA TI ONS /

37. TOPIC MODELING
• In the previous sections, I’ve been noting what you can
do when you have two or more comparison sets
• How is wholesome used in time x vs. time y vs. time z
• What are the differences between English speakers talking
about entrepreneurship vs. French speakers and Spanish
speakers?
• How are people who use the innovative Because X
construction different than people who don’t use it?
• In this section, we talk about topic modeling, which is a
way to automatically identify clusters within a data set,
even if you don’t have a comparison set.
• We’ll use this to explore conversations around
#blacklivesmatter, but we’ll also see how these
conversations shift before/after a particular moment in
time

38. TIME MATTERS

39. TOPICS (EVEN WHEN YOU DON’T HAVE
AN A PRIORI COMPARISON SET)

40. UNKNOWN UNKNOWNS
• In general, topic modeling is a way of addressing
the limits of our knowledge. If you’re asking a
question about data, you probably know
something about the data going in.
• But what we hear from people is that they are keenly aware
that they don’t know what they don’t know.
• Topic modeling is meant to help that.
• In the next slides, another use of topic modeling:
identifying the themes of Martin Luther King Jr.’s
major speeches and sermons

41. • Topic modeling Dr.
King’s major
speeches and
sermons gets
these topics
• Which change
over time
• See also
http://idibon.com/
topic-detection-
mlk/