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