Language is constantly changing, how can this be modeled and predicted.

1. Is this presentation going to be ‘fleek’?
2nd September 2016
Daniel Kershaw

2. “Language, never forget, is more like fashion than
science, and matters of usage, spelling, and
pronunciation tend to wander around like
hemlines”
- Bill Bryson, The Mother Tongue: English and How
It Got That Way

3. Language is in constant change
Online communication adds extra pressure though the merging of time and
space
– Awesomesauce
– Bants
– beer o’clock
– brain fart
– Brexit
– bruh
Language is Contently Changing

5. Metcalf’s Fudge
Frequency of the word
Unobtrusiveness of the rod
Diversity of users and situations
Generation of other forms and
meanings
Endurance of the concept
Grounded Models
Barnhart’s Vfrgt
(V) Number of forms
(F) Frequency of word
(R) Number of sources
(G) Number of genera
(T) Time Span of Word
Linguists and lexicographers aim to understand language
Developed heuristics to aid the decision to include words in dictionaries

6. The Data
Twitter Redditu
Users 3,108,844 ≈25,000,000
Posts 73,528,954 ≈500,000,000
Communities 3046 121,373
Words (n > 200) 373,217 2,712,629
Time Periods 283 days 880 days
uData gathered from the June 2015 Reddit Data Release https://goo.gl/j116ML

7. Variation in Frequency
Assess changed in raw frequency and user frequency over time
Diversity in Form
Assess users adoption of varying forms e.g. additions of ing
Diversity in Meaning
Over time can we see a convergence in meaning of the word
Measures

8. • Normalized word count
– Per time period
• Normalized user word count
– Per time period
Variation in Frequency

9. Assess the prefix and suffix addition of an
innovation
List of prefix and suffixes from the OED
– apple -> apples
– hero -> antihero
Diversity in Form

10. Diversity of meaning
Eyebrow's on fleek
Necklace on point
Makeup on fleek

11. Looking for innovations that have not
been seen before
No solidified meaning within existing
systems e.g. WordNet
Learns the embedding of words within a
corpus using word2vec
Developed by Google in 2009
Uses documents to train neural net
Diversity in Meaning - word2vec

12. Diversity in Meaning - word2vec
Time (t)
Community (c)
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13. Looking for statistically significant growth or
decay of an innovation
Presume language change happens in a
monotonic fashion
Fit Spearman's rank to each time series
X value is days since start of data
Y value is normalized frequency of word
Value range -1 to 1
Sampling the Data

14. Sampling the Data
Class statistically significate change as above
and bellow the 95% confidence interval.

15. Variation in Frequency - Reddit

16. Variation in Frequency – User vs. Frequency
Reddit

17. Variation in Form

18. Diversity in Form - Reddit

19. Diversity in Meaning - Twitter

21. Word of the Year
Collins
binge-watch, verb
clean eating, noun
contactless, adjective
Corbynomics, noun
dadbod, noun
ghosting, noun
manspreading, noun
shaming, noun
swipe, verb
Transgender, adjective
Oxford
😂
Ad blocker, noun
Brexit, noun
Dark Web, noun
On fleek, adjective phrase
Lumber serxual, noun
Refugee, noun
Sharing economy, noun
They (singular), pronoun

22. Word of the Year

23. Word of the Year

24. Word of the Year

25. You can say a lot with Emojis

26. Word of the Year - Emoji

27. Users adopt their language to those around them
They accommodate their language for a number of reasons,
• Impress people
• Dominate people
• Look sophisticated
Users and Language

28. Diffusions of Language happen over time
t1 t2 t3

29. Structural Holes
• The position of a user in a network
gives the user different levels of
power
• User whom span structural holes
have access to more varied
information
• Allow information to flow between
communities,

30. How to measure
Conceptually, constraint refers to how much room you
have to negotiate or exploit potential structural holes in
your network.
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Cij = Direct investment (Pij) + Indirect investment
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Less constraint

31. Method
• We know the size of the final diffusion (N)
• What is the average at each individual usage (n) of an innovation
• Classify/group diffusions base of final size

32. Network Constraint

33. • You could create the next viral word,
• A new word has to be:
– Be usable in many different contexts
– Used across different communities
– Not to different from existing language sounds
Take Home

34. Thank you
2nd September 2015
Daniel Kershaw