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talk of the city http://tinyurl.com/cctxbzo tracking emotions in the city http://tinyurl.com/7uvjasy
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Londoners and Social Media:Track Community “Happiness” + Target Ads
@danielequercia
<who am i>
daniele quercia
offline & online
<goal>
social media language personality
social media
social media
<why>
social media
social media Pop press pundits (Archbishop England&Walses)“Social-networking sites “dehumanize” community life”
social media
social media 1Q&A
social media 2Q&A
social media 3Q&A
social media CS Researchers:“Twitter is NOT a social network but a news media”
social media Pop press pundits (Archbishop England&Wales):“Social-networking sites “dehumanize” community life”
CS Researchers:“Twitter is NOT a social network but a news media”
social media Pop press pundits (Archbishop England&Wales)“Social-networking sites “dehumanize” community life”
CS Researchers:“Twitter is NOT a social network but a news media”
“I beg to diff
er” ;-)
social media language personality
social media
community deprivation well-being use of words
?
community deprivation well-being use of words
community deprivation well-being use of words
3 match sentiment with (census) deprivation
2 classify sentiment of profiles
1 collect profiles & geo-reference them
Goal
community deprivation well-being use of words
250K profiles in London (31.5M tweets)
3 seeds: newspaper accounts
1 collect profiles & geo-reference them
1,323 in London neighborhoods 573 in 51 neighborhoods
Word Count vs. Maximum Entropy
2 classify sentiment of profiles
Word Count
social media language personality
social media language personality
social media language personality
Max Entropy
Training? Upon 300K tweets with smiley and frowny faces
Word Count vs. Max Entropy
Word Count vs. Max Entropy
Index of Multiple Deprivation
3 match sentiment with (census) deprivation
r=.350 word count r=.365 MaxEnt
predicting socioeconomic well-being with twitter
[CSCW’12] Tracking Gross Community Happiness from Tweets
Going beyond sentiment … Look at the subject matter of tweets!
Extract topics from tweets. Easiest way?
Matching Keywords
Extract topics from tweets. Easiest way?
Matching Keywords
Dictionary of keywords? A machine learning model? Training?
Use machine learning model (no training required)
Latent Dirichlet Allocation (LDA)
read profiles & define topics
create virtual bins (latent topics)assign words to a bin (@ random)for each bin: select pair of words if co-occur more than chance: keep them in the bin else: put them into another bin (@ random)
read profiles & define topics
create virtual bins (latent topics)assign words to a bin (@ random)for each bin: select pair of words if co-occur more than chance: keep them in the bin else: put them into another bin (@ random)
Facebook Twitter
read profiles & define topics
create virtual bins (latent topics)assign words to a bin (@ random)for each bin: select pair of words if co-occur more than chance: keep them in the bin else: put them into another bin (@ random)
Facebook Twitter
social
econometrics
read profiles & define topics
create virtual bins (latent topics)assign words to a bin (@ random)for each bin: select pair of words if co-occur more than chance: keep them in the bin else: put them into another bin (@ random)
Facebook Twitter
social
econometrics
Latent Dirichlet Allocation (LDA)
Latent Dirichlet Allocation (LDA)
social media environment sports health wedding parties
Spanish/Portuguesecelebrity gossips
Support Vector Regression IMD <- SVR(topics) accuracy: 8.14 in [13.12,46.88]
Some areas have very few profiles! residents +
Some areas have very few profiles! residents + visitors
Analyze geo-referenced tweets(not only residents but also visitors)
Linear Regression R2=.49 (49% of IMD variability explained)
So what?
Theoretical Implications
Practical Implications
Ads and the City:Considering Geographic Distance Goes a Long Way
Problem Statement: Given a venue (new bar/restaurant), suggests guests
Problem Statement: Given a venue (new bar/restaurant), suggests guests
Problem Statement: Given a venue (new bar/restaurant), suggests guests
Web ≠ people move!
Web ≠ people move!
On people mobility (from the literature):
1) likes might matter 2) distance matters 3) “power users” are special
On people mobility (from the literature):
1) likes might matter 2) distance matters 3) “power users” are special
On people mobility (from the literature):
1) likes might matter 2) distance matters 3) “power users” are special
The extent one is a power user ;)
HIGH α travel farther
HIGH α travel farther
1) Naïve Bayesian2) Bayesian3) Linear Regression (learn weights)
(2)
(2)
(2)
(2)
(2)
(2)
(2)
Future (well, current & you could help)
1 complex buildings
“Who talks to whom”
Network
2 tools for topical & sentiment analysis
3
3
3 urbanopticon.org
2 Tools for topical & sentiment analysis
1 Complex Buildings
@danielequercia