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: January 14 2012 01:23:57.
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Alice s : 0.09 %
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New York : 0.08 %
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that Alice : 0.07 %
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training examples : 0.06 %
they re : 0.06 %
see that : 0.06 %
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you re : 0.06 %
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Dissecting Spread : 0.05 %
Stuff Harvard : 0.05 %
I ll : 0.05 %
doesn t : 0.05 %
each topic : 0.05 %
tweets likes : 0.05 %
users who : 0.05 %
York City : 0.05 %
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each document : 0.05 %
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Introduction Latent : 0.05 %
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Network Dissecting : 0.05 %
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cute animals : 0.05 %
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each feature : 0.05 %
Comments Uncategorized : 0.05 %
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Silicon Valley : 0.05 %
What s : 0.05 %
Winning Netflix : 0.05 %
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Introduction Restricted : 0.04 %
couple months : 0.04 %
followed by : 0.04 %
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Los Angeles : 0.04 %
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Social Media : 0.04 %
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Family Inspiration : 0.04 %
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my post : 0.04 %
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twitter com : 0.04 %
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didn t : 0.04 %
shares Facebook : 0.04 %
similarity metric : 0.04 %
Computer Science : 0.04 %
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Uncategorized Introduction : 0.04 %
Marc Bodnick : 0.04 %
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each word : 0.04 %
brought by : 0.04 %
Data brought : 0.04 %
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Social Advice : 0.04 %
Edwin Chen's : 0.04 %
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CRFs can : 0.04 %
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Modeling Sarah : 0.04 %
Topic Modeling : 0.04 %
likes Flowing : 0.04 %
Leave Comment : 0.04 %
here s : 0.04 %
Glitter Big : 0.04 %
each movie : 0.04 %
training example : 0.04 %
which contestants : 0.04 %
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Go through : 0.04 %
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were also : 0.04 %
p w : 0.04 %
followers each : 0.04 %
through each : 0.04 %
baseline predictors : 0.04 %
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you ll : 0.04 %
people who : 0.04 %
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my answer : 0.04 %
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Sarah Palin : 0.04 %
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Palo Alto : 0.04 %
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For example : 0.04 %
TV series : 0.04 %
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XKCD comics : 0.04 %
take look : 0.04 %
Restaurants San : 0.04 %
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https twitter : 0.04 %
graphical models : 0.04 %
first rating : 0.04 %
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Trig Family : 0.04 %
dirichlet allocation : 0.04 %
does each : 0.04 %
lda nlp : 0.04 %
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unit connected : 0.04 %
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tend be : 0.04 %
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Caltech students : 0.04 %
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science fiction : 0.04 %
matrix factorization : 0.04 %
Justin Bieber : 0.04 %
Stanford students : 0.04 %
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Potter Avatar LOTR : 0.11 %
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Gladiator Titanic Glitter : 0.08 %
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Network Dissecting Spread : 0.05 %
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Edwin Chen's Blog : 0.04 %
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conditional random fields : 0.04 %
Restaurants San Francisco : 0.04 %
factor that allows : 0.04 %
each feature function : 0.04 %
Topic Modeling Sarah : 0.04 %
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dirichlet allocation lda : 0.04 %
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Data brought by : 0.04 %
ratio i e : 0.04 %
Flowing Data brought : 0.04 %
https twitter com : 0.04 %
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as well as : 0.04 %
latent dirichlet allocation : 0.04 %
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SF fantasy unit : 0.04 %
allows Alice s : 0.04 %
that allows Alice : 0.04 %
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book s length : 0.03 %
introduction random forests : 0.03 %
New York Times : 0.03 %
FB vs Twitter : 0.03 %
it s because : 0.03 %
Latent Dirichlet Allocation : 0.03 %
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come food topic : 0.03 %
Presidential Campaign Elections : 0.03 %
SF fantasy fan : 0.03 %
which contestants believe : 0.03 %
Oscar winners fan : 0.03 %
e g maybe : 0.03 %
XKCD comics popular : 0.03 %
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followers each school : 0.03 %
especially popular Twitter : 0.03 %
comics especially popular : 0.03 %
would be nice : 0.03 %
our training examples : 0.03 %
hardcover paperback versions : 0.03 %
Here s graph : 0.03 %
Note that first : 0.03 %
that SF fantasy : 0.03 %
status https twitter : 0.03 %
Wildlife BP Corrosion : 0.03 %
sum runs over : 0.03 %
s rating depend : 0.03 %
conditional random field : 0.03 %
Biology Quantum Mechanics : 0.03 %
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so let s : 0.03 %
couple months later : 0.03 %
cute animals topic : 0.03 %
especially popular Facebook : 0.03 %
So let s : 0.03 %
Wall Street Journal : 0.03 %
does each school : 0.03 %
e g Alice : 0.03 %
feature functions which : 0.02 %
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networks twitter xkcd : 0.02 %
similarity metric define : 0.02 %
probability so on : 0.02 %
Silicon Valley NYC : 0.02 %
someone else s : 0.02 %
Uncategorized Introduction Restricted : 0.02 %
follow least one : 0.02 %
processing r social : 0.02 %
i e users : 0.02 %
Valley Mergers Acquisitions : 0.02 %
Silicon Valley Mergers : 0.02 %
twitter xkcd Comments : 0.02 %
between Silicon Valley : 0.02 %
r social graphs : 0.02 %
articles popular Twitter : 0.02 %
differences between Silicon : 0.02 %
new scientist r : 0.02 %
Color Labs etc : 0.02 %
Restricted Boltzmann Machine : 0.02 %
all hidden units : 0.02 %
Path Color Labs : 0.02 %
p word w : 0.02 %
little while ago : 0.02 %
each school interested : 0.02 %
Here s sample : 0.02 %
Uncategorized Tweets vs : 0.02 %
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by p school : 0.02 %
shared Twitter vs : 0.02 %
May edition tweets : 0.02 %
followed by p : 0.02 %
Topics followed by : 0.02 %
vs Likes gets : 0.02 %
Likes gets shared : 0.02 %
gets shared Twitter : 0.02 %
word w topic : 0.02 %
San Francisco Bay : 0.02 %
scientist r social : 0.02 %
versions same book : 0.02 %
likes new scientist : 0.02 %
r social networks : 0.02 %
Sengupta s upvote : 0.02 %
measure doesn t : 0.02 %
social networks twitter : 0.02 %
Aditya Sengupta s : 0.02 %
come across most : 0.02 %
flowingdata likes new : 0.02 %
shows which contestants : 0.02 %
Now let s : 0.02 %
Francisco Bay Area : 0.02 %
guess as why : 0.02 %
article ever written : 0.02 %
let s look : 0.02 %
facebook flowingdata likes : 0.02 %
who follow least : 0.02 %
the articles popular : 0.02 %
nlp r sarah : 0.02 %
lda nlp r : 0.02 %
couple days later : 0.02 %
r sarah palin : 0.02 %
sarah palin topic : 0.02 %
large positive then : 0.02 %
palin topic models : 0.02 %
rating linearly depend : 0.02 %
s rating linearly : 0.02 %
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connected all visible : 0.02 %
strong associations latent : 0.02 %
connected any other : 0.02 %
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feature large positive : 0.02 %
associated feature large : 0.02 %
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since first rating : 0.02 %
turned Harry Potter : 0.02 %
set similar items : 0.02 %
set latent factors : 0.02 %
w p w : 0.02 %
can dot product : 0.02 %
number days since : 0.02 %
Hip Hop Music : 0.02 %
like Harry Potter : 0.02 %
topic models Comments : 0.02 %
weight associated feature : 0.02 %
unit connected any : 0.02 %
Facebook than Twitter : 0.02 %
hidden units again : 0.02 %
projects Spork Spoon : 0.02 %
Post New York : 0.02 %
Huffington Post New : 0.02 %
Boston Red Sox : 0.02 %
any post s : 0.02 %
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measure book s : 0.02 %
Harvard Stanford Berkeley : 0.02 %
contestants believe evolution : 0.02 %
though it would : 0.02 %
C programming language : 0.02 %
Operating Systems Compilers : 0.02 %
map shows which : 0.02 %
gephi information propagation : 0.02 %
information propagation processing : 0.02 %
connected each other : 0.02 %
w topic t : 0.02 %
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by May edition : 0.02 %
Trump Dalai Lama : 0.02 %
figure out which : 0.02 %
might have strong : 0.02 %
interested learning more : 0.02 %
B can see : 0.02 %
matrix factorization methods : 0.02 %
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sm
Total: 198
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echenu.me
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esshen.me
echen8.me
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echsen.me
echgen.me
eche4n.me
euchen.me
echens.me
ech4en.me
echenl.me
uchen.me
echenn.me
rchen.me
6echen.me
bechen.me
echsn.me
ecthen.me
nechen.me
echej.me
echer.me
eshen.me
echen0.me
echeb.me
cechen.me
ecjhen.me
ekchen.me
ech3n.me
echene.me
eachen.me
8echen.me
echeh.me
etshen.me
echen6.me
echena.me
echen1.me
echetn.me
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echend.me
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echen.me
echeny.me
7echen.me
9echen.me
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echeng.me
echent.me
echewn.me
echnen.me
ecchen.me
echenj.me
echan.me
kechen.me
ecxhen.me
echenv.me
hechen.me
edchen.me
echeon.me
qechen.me
yechen.me
cehen.me
echein.me
echen9.me
ecyen.me
ecghen.me
ecben.me
jechen.me
echaen.me
vechen.me
efhen.me
3chen.me
echern.me
ech4n.me
ecfhen.me
rechen.me
echen4.me
echenp.me
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zechen.me
exchen.me
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eche3n.me
echejn.me
echebn.me
echean.me
ecgen.me
ecnen.me
echrn.me
echjen.me
echenx.me
echren.me
2echen.me
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echem.me
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echain.me
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echenq.me
echien.me
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ekhen.me
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4echen.me
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echoen.me
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echenb.me
echben.me
ecdhen.me
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ecuhen.me
echenk.me
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aichen.me
ecbhen.me
echurn.me
eche.me
ecnhen.me
echehn.me
echuen.me
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xechen.me
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3echen.me
echden.me
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ecshen.me
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echwen.me
echemn.me
echenes.me
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exhen.me
echeyn.me
edhen.me
echen7.me
eken.me
ecjen.me
lechen.me
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ehcen.me
echenf.me
0echen.me
ech3en.me
echhen.me
echeni.me
echdn.me
echun.me
sechen.me
e4chen.me
evchen.me
echenz.me


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