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The Language that Gets People to
Give:
Phrases that Predict Success on
Kickstarter
Tanushree Mitra & Eric Gilbert
What makes some projects
succeed while others fail ?
Predictive Features of
success and failure ?
QUANTITATIVE APPROACH
Independent Variables
Predictive Features
Statistical Model
Dependent Variable:
Project outcome (Funded or Not)
QUANTITATIVE APPROACH
Independent Variables
Predictive Features(?)
Statistical Model
Dependent Variable:
Project outcome (Funded or Not)
Category
Video
Present
Goal
Duration
Facebook
Connected
Pitch
DATA
45,815K Kickstarter projects
all projects as of June 2012
51.53% funded
48.47% not
funded
45K Kickstarter project
URLs
[uni,bi,tri]-grams
Fetch project end date
phrase frequency > 50?
Project reached end
date?
phrase in all 13
categories?
Scrape project description
Scrape control variables
Lowercase text
Remove stop words
Penalized Logistic
Regression
45K Kickstarter project
URLs
[uni,bi,tri]-grams
Fetch project end date
phrase frequency > 50?
Project reached end
date?
phrase in all 13
categories?
Scrape project pitch
Scrape control variables
Lowercase text
Remove stop words
Statistical Model
45K Kickstarter project
URLs
[uni,bi,tri]-grams
Fetch project end date
phrase frequency > 50?
Project reached end
date?
phrase in all 13
categories?
Scrape project pitch
Scrape control variables
Lowercase text
Remove stop words
Statistical Model
45K Kickstarter project
URLs
[uni,bi,tri]-grams
Fetch project end date
phrase frequency > 50?
Project reached end
date?
phrase in all 13
categories?
Scrape project pitch
Scrape control variables
Lowercase text
Remove stop words
Statistical Model
45K Kickstarter project
URLs
[uni,bi,tri]-grams
Fetch project end date
phrase frequency > 50?
Project reached end
date?
phrase in all 13
categories?
Scrape project pitch
Scrape control variables
Lowercase text
Remove stop words
Statistical Model
45K Kickstarter project
URLs
[uni,bi,tri]-grams
Fetch project end date
phrase frequency > 50?
Project reached end
date?
phrase in all 13
categories?
Scrape project pitch
Scrape control variables
Lowercase text
Remove stop words
Statistical Model
~20K unigrams, bigrams,
trigrams
Pitch
45K Kickstarter project
URLs
[uni,bi,tri]-grams
Fetch project end date
phrase frequency > 50?
Project reached end
date?
phrase in all 13
categories?
Scrape project pitch
59 control variables
Scrape control variables
Lowercase text
Remove stop words
Statistical Model
Category
Video
Present
Goal
Duration
Facebook
Connected
45K Kickstarter project
URLs
[uni,bi,tri]-grams
Fetch project end date
phrase frequency > 50?
Project reached end
date?
phrase in all 13
categories?
Scrape project pitch
59 control variables
Scrape control variables
Lowercase text
Remove stop words
Statistical Model
STATISTICAL TECHNIQUE
Independent Variables
Phrases (20K) + Controls (59)
Statistical Model
Dependent Variable:
Project outcome (Funded or Not)
STATISTICAL TECHNIQUE
Independent Variables
Phrases (20K) + Controls (59)
Penalized Logistic
Regression
Dependent Variable:
Project outcome (Funded or Not)
Friedman et al. 2010
Results: MODEL FITS
Baseline
Model
|
|
Error
48.47
%
Controls Only
Model
Explanatory Power |
Phrases + Controls
Model
Explanatory Power |
40.8 %
58.56 %
|
|
Error
17.03%
Error
2.24%
(NF) Predictors
not been able ( β = − 4.07 )
“I have not been able to finish the film
because none of my editors will see the
project through to the end.”
(NF) Predictors
later i ( β = − 3.04 )
hope to get ( β = − 2.39 )
“I can’t take size orders and possibly hope
to get them all made in time for
christmas.”
(NF) Predictors
even a dollar
( β = − 3.10 )
Wattenberg & Viégas, 2008
(F) Predictors
mention your ( β = 2.69 )
also receive ( β = 1.83 )
add $40 and you will also receive two vip
tickets to the premiere screening.
(F) Predictors
next step is ( β = 1.07 )
Recording is pretty much done, next step
is production.
(F) Predictors
cats ( β = 2.64 )
UNDERSTANDING CONTEXT
A closer look at predictive
phrases
Principles of Persuasion
Cialdini, R. B. 1993
Principles of Persuasion
1.
2.
3.
4.
5.
6.
Reciprocity
Scarcity
Authority
Social Proof
Social Identity
Liking
Cialdini, R. B. 1993
Principles of Persuasion
1.
2.
3.
4.
5.
6.
Reciprocity
Scarcity
Authority
Social Proof
Social Identity
Liking
Cialdini, R. B. 1993
RECIPROCITY
Brehm & Cole 1966, Goranson & Berkowitz, 1966, Ciladini 2001
RECIPROCITY
we’ll mention your (β = 2.69) name
in the sleeve of our full length
album
RECIPROCITY
I will thank you on my website,
send you
good karma and (β = 2.04) ..
SCARCITY
Ciladini 2001, Ciladini & Goldstein 2004
SCARCITY
also, you will be
given the chance (β = 2.69)
to purchase our small batch
pieces before the public domain
AUTHORITY
Ciladini 2001, Ciladini & Goldstein 2004
AUTHORITY
the project will be (β = 18.48)
produced by
dove award winning producer
SOCIAL PROOF
Ciladini 2001
SOCIAL PROOF
[name] has pledged (β = 5.42)
some money..… so, you can
see that i already have people
willing to support my art.
Language is a reliable signal of
success of crowd-funded projects
So are some controls….
CONTROLS
CONTROLS
Graphic Design
β = 1.35
Illustration
β = -2.55
Video Present
β = 0.60
Journalism
β = -1.12
Project Duration
β = -0.01
Facebook Connected β = 0.13
…
…
IMPLICATIONS
IMPLICATIONS
http://www.cc.gatech.edu/~tmitra3/data/KS.predicts
Also at: http://b.gatech.edu/1mf1C6E
The Language that Gets People to
Give:
Phrases that Predict Success on
Kickstarter
Tanushree Mitra & Eric Gilbert
@tanmit | @eegilbert
DATA: http://b.gatech.edu/1mf1C6E
Fancy Stats. Huh!
Google 1T Corpus
phrases
Scan presence of
KS phrases in
Google 1T
χ2 test between
phrase frequencies
+
Bonferroni Correction
Search for phrases with
significantly higher difference
+
Membership higher in Ggle
1T
54K Kickstarter phrases
Non-zero β weights
GENERAL PHRASES:
- 494 positive predictors
- 453 negative predictors
next step is
in the
upcoming
to announce
provide us
need one
….
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