the social world of twitter: topics, geography, and emotions
DESCRIPTION
The social world of twitter http://tinyurl.com/7xdv524TRANSCRIPT
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
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
3 relate metrics to 3 aspects
2 compute (ego)network metrics
1 collect profiles
Goal: Characterize Twitter ``community’’
250K profiles in London (31.5M tweets)
3 seeds: newspaper accounts
1 collect profiles
228K profiles
2 compute (ego)network metrics
228K egonetworks4 versions: original, reciprocal(24%), 1-way msg(4%), 2-way(<1%)
a Topics b Geography c Emotions
3 relate net metrics to 3 aspects
a topics
AlchemyAPI, OpenCalais, TextWise
hp 1: higher diversity – higher brokerage
Get topics & Compute diversity
a topics
AlchemyAPI, OpenCalais, TextWise
hp 1: higher diversity – higher brokerage
Get topics & Compute diversity
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 geography
hp 2: closely-knit - less geo dispersed
b geography
hp 2: closely-knit - less geo dispersed
b geography
hp 2: closely-knit - less geo dispersed
b geography
hp 2: closely-knit - less geo dispersed
c emotions
hp 3: closely-knit – emotion sharing
c emotions
hp 3: closely-knit – emotion sharing
c emotions
hp 3: closely-knit – emotion sharing
c emotions
hp 3’: homophily
1. Brokers tend to cover diverse topics
2. Users have a “typical” geo span
3. “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 media environment sports health wedding parties
Spanish/Portuguesecelebrity gossips
Support Vector Regression IMD <- SVR(topics) accuracy: 8.14 in [13.12,46.88]
3
3
3 urbanopticon.org
2 Tools for topical & sentiment analysis
1 Complex Buildings
@danielequercia