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Powers and Problems of Integrating Social Media Data with Public Health and Safety
- 1. 1! Powers and Problems of Integrating Social Media Data with Public Health and Safety Bloomberg D4GX 28 September 2015 Bloomberg HQ Cody Buntain and Jennifer Golbeck {cbuntain,golbeck}@cs.umd.edu Human-Computer Interaction Lab University of Maryland Gary LaFree garylafree@gmail.com START Center University of Maryland
- 2. What is the point of this talk?! 2!
- 3. The Point 3! Social media data can augment ! public survey-based research efforts!
- 4. The Point 4! Social media data can augment ! public survey-based research efforts!
- 5. The Point 5! Social media data can augment ! public survey-based research efforts!
- 6. The Point 6! Social media data can augment ! public survey-based research efforts!
- 7. The Point 7! Social media data can augment ! public survey-based research efforts!
- 8. The Problem • Surveys are pervasive and powerful 8!
- 9. The Problem • Surveys are slow 9!
- 10. The Problem • Surveys often don’t give progressive results 10!
- 11. The Problem • Surveys are expensive 11! $0! $3,500,000,000! $7,000,000,000! $10,500,000,000! $14,000,000,000! Total Population! Census Cost! Census Year!CensusCost!
- 12. A Solution • Augment surveys with accessible and cheap* social media data! • Social media data can supplement when surveys are too costly or slow! 12! 3,000,000! 3,500,000! 4,000,000! 4,500,000! 5,000,000! 5,500,000! 6,000,000! Day of Collection! TweetCount!
- 13. A Solution • Augment surveys with easily accessible social media data! • Social media data could substitute when surveys are too costly or slow! 13! 3,000,000! 3,500,000! 4,000,000! 4,500,000! 5,000,000! 5,500,000! 6,000,000! Day of Collection! TweetCount!
- 14. Demonstrations • Geocoded tweets and the US Census! • Drug references on social media! • Sentiment and law enforcement! 14!
- 15. Demonstrations • Geocoded tweets and the US Census! • Drug references on social media! • Sentiment and law enforcement! 15! Focus on Twitter because tweets are easy to acquire via public sample stream!
- 16. Twitter and the US Census • 1-3% of tweets have geolocation information! • GPS coordinates! • Bounding boxes! • Places! • Compare Twitter distributions per state with 2013 US Census data! 16!
- 17. • 1-3% of tweets have geolocation information! • GPS coordinates! • Bounding boxes! • Places! • Compare Twitter distributions per state with 2013 US Census data! 17! Twitter and the US Census
- 18. • Can Twitter track geographic trends in drug use?! • National Survey on Drug Use and Mental Health (NSDUH) has ground truth for:! • Marijuana! • Cocaine! • Compare orders of states for each drug! 18! Twitter and the NSDUH
- 19. • Can Twitter track geographic trends in drug use?! • National Survey on Drug Use and Mental Health (NSDUH) has ground truth for:! • Marijuana! • Cocaine! • Compare orders of states for each drug! 19! High ranked correlation! for marijuana! Twitter and the NSDUH
- 20. • Can Twitter track geographic trends in drug use?! • National Survey on Drug Use and Mental Health (NSDUH) has ground truth for:! • Marijuana! • Cocaine! • Compare orders of states for each drug! 20! High ranked correlation! for marijuana! Low correlation! for cocaine! Twitter and the NSDUH
- 21. • Can we measure public response to law enforcement?! • Outreach from law enforcement to community is important! 21! Twitter Sentiment and Police
- 22. • Compare sentiment towards police around two events:! • 2013 Boston Marathon Bombing! • 2014 Ferguson, MO Protests! 22! Twitter Sentiment and Police 2013 Boston Marathon Bombings! Sentiment!
- 23. • Compare sentiment towards police around two events:! • 2013 Boston Marathon Bombing! • 2014 Ferguson, MO Protests! 23! Twitter Sentiment and Police 2013 Boston Marathon Bombings! Sentiment! Significant ! drop!
- 24. • Compare sentiment towards police around two events:! • 2013 Boston Marathon Bombing! • 2014 Ferguson, MO Protests! 24! Twitter Sentiment and Police 2014 Ferguson, MO Protests! Sentiment!
- 25. • Location data in the public stream! • 1-3% of the 1% public sample might work for coarse insights! • Difficult to get community-level granularity! • Can sacrifice volume for specificity with GPS bounding boxes! 25! The Limitations 1%! Sample Stream!
- 26. • Limited availability of data from other sources! • Facebook! • Full access to data sources can be expensive! • Addressable by companies?! 26! The Limitations
- 27. • Twitter has known biases [1]! • Mostly male (though decreasing)! • Oversamples Caucasians! 27! The Limitations and the list previously described. We do so by compar the first word of their self-reported name to the gender We observe that there exists a match for 64.2% of the us Moreover, we find a strong bias towards male users: F 71.8% of the the users who we find a name match for ha male name. 0 0.2 0.4 0.6 0.8 1 2007-01 2007-07 2008-01 2008-07 2009-01 2009-07 FractionofJoiningUsers whoareMale Date Figure 3: Gender of joining users over time, binned groups of 10,000 joining users (note that the join rate
- 28. • Relevancy and credibility! • Huge amounts of data is nice, but we want the right data! • No guarantee things on social media are true 28! The Limitations ! ! !
- 29. 29! The Conclusion Social media data can augment ! public survey-based research efforts! The Point
- 30. 30! The Conclusion Social media data can augment ! public survey-based research efforts! The Point
- 31. 31! The Conclusion Social media data can augment ! public survey-based research efforts! The Point The Limits • User location information! • Limited data from other sources! • Sampling bias!
- 32. Questions?! 32!