The Social World of Twitter: Topics, Geography, and Emotions
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The Social World of Twitter:Topics, Geography, and Emotions
@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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3 relate metrics to 3 aspects
2 compute (ego)network metrics
1 collect profiles
Goal: Characterize Twitter ``community’’
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250K profiles in London (31.5M tweets)
3 seeds: newspaper accounts
1 collect profiles
228K profiles
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2 compute (ego)network metrics
228K egonetworks4 versions: original, reciprocal(24%), 1-way msg(4%), 2-way(<1%)
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a Topics b Geography c Emotions
3 relate net metrics to 3 aspects
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a topics
AlchemyAPI, OpenCalais, TextWise
hp 1: higher diversity – higher brokerage
Get topics & Compute diversity
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a topics
AlchemyAPI, OpenCalais, TextWise
hp 1: higher diversity – higher brokerage
Get topics & Compute diversity
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a topics
hp 1: higher diversity – higher brokerage
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a topics
hp 1: higher diversity – higher brokerage
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a topics
hp 1: higher diversity – higher brokerage
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b geography
hp 2: closely-knit - less geo dispersed
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b geography
hp 2: closely-knit - less geo dispersed
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b geography
hp 2: closely-knit - less geo dispersed
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b geography
hp 2: closely-knit - less geo dispersed
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c emotions
hp 3: closely-knit – emotion sharing
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c emotions
hp 3: closely-knit – emotion sharing
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c emotions
hp 3: closely-knit – emotion sharing
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c emotions
hp 3’: homophily
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1. Brokers tend to cover diverse topics
2. Users have a “typical” geo span
3. “Happy” (“sad”) users do cluster together
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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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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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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