Advanced social intelligence

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Social data mining beyond keywords, Francesco D’Orazio @abc3d @ Big Data London, O’Reilly Strata Conference Special, October 1st 2012 and to Big Data World Congress London, November 7th 2012

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Advanced social intelligence

  1. 1. Social Data !Beyond Keywords Francesco D’Orazio @abc3d www.facegroup.comBig Data Congress, London, November 7 2012
  2. 2. We have beenasking thewrong question
  3. 3. “We talk of the relationshipsconsumers have with our brands as ifthey were primary but the data pointsto things being otherwise.Consumers’ most valuablerelationships are not with brands butwith other consumers” Mark Earls
  4. 4. Social data allow us to learn aboutthe relationships and interactionsbetween consumers and how brandscan fit into that equation  Cosmic 140 © Information Architects, Inc. 2010
  5. 5. Social data allows us to see how interactions play out at individual level (microscope)
  6. 6. Social data allow us to see howinteractions play out at network level(the macroscope)
  7. 7. The codebehind atweet
  8. 8. Currently we look at social media data ! like a butcher looks at a carcassCutting bykeywords Aggregating by channels Weighting by influence
  9. 9. © Alexandre Farto aka Vhils 2010 Most Social Media Monitoring platforms focus on just Content
  10. 10. © Alexandre Farto aka Vhils 2010 But we also wanted to understand Context and Behaviour
  11. 11. © Thomas Doyle 2010 So we designed new research frameworks and social data mining tools
  12. 12. Social Media vs Social Data Social media is the set of applications and platforms allowing Social data is the people to participate in collective information online social activities produced by millions of people as they actively participate in online social activities. 13
  13. 13. Social Media vs Social Data 14
  14. 14. Social Media vs Social Data 15
  15. 15. Dimensions of Social Data Content Demographics Behaviours Social Graph Interest Graph 16
  16. 16. What makes social data ‘big’?Large volumesReal-time collectionReal-time analysisInteractivityFull-Text Weavrs Emotion Map
  17. 17. 1!TOPIC/BRAND tracking By keywords Share of Voice
  18. 18. 2!AUDIENCE mapping > brand graph By keywords By audience Share of Voice Share of Mind
  19. 19. 3!CLUSTER tracking > real-timesegmentationDynamicSegments Real-time Audience Insights
  20. 20. 4! CONTENT diffusion > where, how and who is sharing the content
  21. 21. 5!INFLUENCE mapping > hubs &connectors by volumes, visibilityand engagementInfluence by Volume Influence by Visibility Influence by Engagement
  22. 22. 6!SOCIAL CRM > efficientworkflow with automaticlabeling and predictive modeling
  23. 23. 7!Augmentation > layering socialdata on a Panel or a Community
  24. 24. 8! SOCIAL SIMULATIONS20 participants 20 alpha-bots designed by them 20 beta-bots tested and fine- tuned by us 20 beta-bots cloned 20 times each (400 beta-clones active over a 2 weeks period)
  25. 25. 9!REALITY MINING
  26. 26. Social science +Computational Social data Social Science + Computation  
  27. 27. CONTINUOUSITERATION: play with awider range ofhypothesis to solve aproblem.Pulsar Social Data Mining platform 31
  28. 28. CONTINUOUS ITERATION: starting with 0 hypothesis> finding out what I don’t know I don’t knowIs Big Data producing a new epistemology? 32
  29. 29. Thank you francesco d’orazio, @abc3d! www.facegroup.com

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