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The Science of Social Data | PeerIndex
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The Science of Social Data | PeerIndex


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Slides presented by Ferenc Huszar, PeerIndex lead scientist at the Budapest Data Science Meetup on 27 May 2013.

Slides presented by Ferenc Huszar, PeerIndex lead scientist at the Budapest Data Science Meetup on 27 May 2013.

Published in: Technology, Business
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  • 1. The Science ofSocial DataFerenc Huszár
  • 2. The profile page
  • 3. The Influence graph
  • 4. PeerIndex data platforminfluence graphsegment scorespredictionsPeerIndex APIinfluencermarketinganalyticsdashboardpersonaliseddiscounts3rd party apps
  • 5. Technologies we use● Twitter Storm○ in-stream processing, ~100m tweets per day, webcrawling● Apache○ Hadoop, Hive, Pig● Amazon Web Services○ S3, EC2, Elastic MapReduce, DynamoDB, RedShift● python○ scikit-learn, numpy, scipy, ipython● vowpal wabbit
  • 6. Social influence andproduct adaptation● experiment: randomly hide recommendations● only way to prove social influence exists!● results:○ younger users are more susceptible to influence○ people influence peers of the same age the most○ women are less susceptible to influence than men○ women influence men more than other women○ its complicated = more susceptible to influence○ more influence on peers with whom they attendedthe same college○ more mutual friends = stronger influence○ co-appearance in photos did not have an effectSinan Aral
  • 7. James FowlerSocial influence andpolitical mobilisation
  • 8. Predicting real-life influencefrom social media
  • 9. Private traits predictablefrom social media T Graepel
  • 10. In summary● lots of data● active area of research● many attributes are predictable● social influence can be inferred● experiments are helpful
  • 11. Live experiment1. please take a slip of paper2. write down the name of○ someone you know○ who is here today○ do not write your own name3. put the slip in the blue bag
  • 12. Friendship paradox● "People have less friends than their friendsdo, on average"● Procedure for finding highly connected ppl:○ pick a random person, Alice○ Ask Alice to tell you the name of a random friend ofhers, Bob○ In expectation, Bob has more connections than anaverage person.● Twitter example: about 20% of active twitterusers follow Justin Bieber● Perk: James Fowlers Connected
  • 13. What is social influence?● a hard concept, like intelligence● "the ability of individuals to alter opinions,beliefs and behaviours of others in acommunity via their behaviours and actions"● What influence is not about:○ activity, volume○ reliability, trustworthiness○ technical expertise● PeerIndex attempts to quantify levels ofinfluence based on interaction data● much like IQ tries to quantify intelligence