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