tracking "gross community happiness" from tweets
DESCRIPTION
Policy makers are calling for new socio-economic measures that reflect subjective well-being, to complement traditional measures of material welfare as the Gross Domestic Product (GDP). Self- reporting has been found to be reasonably accurate in measuring one’s well-being and conveniently tallies with sentiment expressed on social media (e.g., those satisfied with life use more positive than negative words in their Facebook status updates). Social media content can thus be used to track well-being of individuals. A question left unexplored is whether such content can be used to track wellbeing of entire physical communities as well. To this end, we consider Twitter users based in a variety of London census communities, and study the relationship between sentiment expressed in tweets and community socio-economic wellbeing. We find that the two are highly correlated: the higher the normalized sentiment score of a community’s tweets, the higher the community’s socio- economic well-being. This suggests that monitoring tweets is an effective way of tracking community well-being too.TRANSCRIPT
Tracking “Gross Community Happiness” from Tweets
@danielequercia
offline & online
community deprivation well-being use of words
?
community deprivation well-being use of words
community deprivation well-being use of words
social media
social media
top-using city
London
3 match sentiment with (census) deprivation
2 classify sentiment of profiles
1 collect profiles & geo-reference them
Goal
community deprivation use of words
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
Word Count vs. Maximum Entropy
2 classify sentiment of profiles
Index of Multiple Deprivation
3 match sentiment with (census) deprivation
r=.350 word count r=.365 MaxEnt
predicting socioeconomic well-being with twitter
So what?
Theoretical Implications
Practical Implications
Limitations
Future (well, current & you could help)
1 beyond sentiment …
Look at the subject matter of tweets!
social media environment sports health wedding parties
Spanish/Portuguesecelebrity gossips
Linear Regression R2=.49 (49% of IMD variability explained)
2 complex buildings
3 tools for topical & sentiment analysis
4
4 urbanopticon (image of the city)
3 scalable tools
2 complex spaces
1 topical analysis
deadline: March 2
@danielequercia