The Social World of Twitter: Topics, Geography, and Emotions
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The Social World of Twitter: Topics, Geography, and Emotions

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The social world of twitter

The social world of twitter
http://tinyurl.com/7xdv524

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The Social World of Twitter: Topics, Geography, and Emotions The Social World of Twitter: Topics, Geography, and Emotions Presentation Transcript

  • The Social World of Twitter:Topics, Geography, and Emotions@danielequercia
  • <who am i>
  • daniele quercia
  • offline & online
  • <goal>
  • social media language personality social media
  • social media <why>
  • social media
  • Pop press pundits (Archbishop England&Walses) social media“Social-networking sites “dehumanize” community life”
  • social media
  • social media 1 Q&A
  • social media 2 Q&A
  • social media 3 Q&A
  • CS Researchers:“Twitter is NOT media social a social network but a news media”
  • Pop press pundits (Archbishop England&Wales): social media“Social-networking sites “dehumanize” community life”CS Researchers:“Twitter is NOT a social network but a news media”
  • Pop press pundits (Archbishop England&Wales) social media“Social-networking sites “dehumanize” community life”CS Researchers:“Twitter is NOT a social network but a news media” er” ;-) g to diff “I be
  • social media language personality social media
  • Goal: Characterize Twitter ``community’’ 1 collect profiles 2 compute (ego)network metrics 3 relate metrics to 3 aspects
  • 1 collect profiles 3 seeds: newspaper accounts 250K profiles in London (31.5M tweets) 228K profiles
  • 2 compute (ego)network metrics 228K egonetworks 4 versions: original, reciprocal(24%), 1-way msg(4%), 2-way(<1%)
  • 3 relate net metrics to 3 aspects a Topics b Geography c Emotions
  • a topics hp 1: higher diversity – higher brokerage Get topics & Compute diversity AlchemyAPI, OpenCalais, TextWise
  • a topics hp 1: higher diversity – higher brokerage Get topics & Compute diversity AlchemyAPI, OpenCalais, TextWise
  • a topics hp 1: higher diversity – higher brokerage
  • a topics hp 1: higher diversity – higher brokerage
  • a topics hp 1: higher diversity – higher brokerage
  • b geographyhp 2: closely-knit - less geo dispersed
  • b geographyhp 2: closely-knit - less geo dispersed
  • b geographyhp 2: closely-knit - less geo dispersed
  • b geographyhp 2: closely-knit - less geo dispersed
  • c emotionshp 3: closely-knit – emotion sharing
  • c emotionshp 3: closely-knit – emotion sharing
  • c emotionshp 3: closely-knit – emotion sharing
  • c emotionshp 3’: homophily
  • 1. Brokers tend to cover diverse topics2. Users have a “typical” geo span3. “Happy” (“sad”) users do cluster together
  • Future (well, current & you could help)
  • 1 complex buildings
  • “Who talks to whom”
  • Network
  • 2 tools for topical & sentiment analysis
  • social mediaenvironmentsportshealth wedding parties Spanish/Portuguese celebrity gossips
  • Support Vector Regression IMD <- SVR(topics) accuracy: 8.14 in [13.12,46.88]
  • 3
  • 3
  • 1 Complex Buildings2 Tools for topical & sentiment analysis3 urbanopticon.org
  • @danielequercia