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In an article recently published in Research World Magazine and on his Tumblr blog Abc3d, our Chief Innovation Officer, Francesco D’Orazio outlines the challenges facing the social media monitoring …

In an article recently published in Research World Magazine and on his Tumblr blog Abc3d, our Chief Innovation Officer, Francesco D’Orazio outlines the challenges facing the social media monitoring industry – and 10 ways to tackle them.

http://abc3d.tumblr.com/post/62887759854/social-data-intelligence

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  • 1. The Future of Social Media Research! Or how to re-invent social media monitoring in 10 steps Francesco D’Orazio @abc3d | PulsarPlatform.com
  • 2. legacy! Since it emerged 15 years ago, the industry has been largely responsible for driving some of the most interesting evolutions in the research space, such as the democratization of text mining and computational approaches to mining qualitative information.
  • 3. qual/quant! blurring! This is an approach that for the first time enables both a granular and a birds-eye view of the data, making it possible to produce qualitative observations on a mass scale. A new perspective that is blurring the lines between qualitative and quantitative thinking.
  • 4. a new epistemology ! And with computation also comes the ability to mine larger (and messier) datasets, which is in turn steadily shifting the focus of what we call knowledge, from understanding causation to identifying correlations.
  • 5. Social media monitoring is a growing industry but one that is stuck in its old ways. And in need a of a urgent re-think. Or As McLuhan would have put it, we look at social data through the rear view mirror
  • 6. the 480+ smm platforms are broken!
  • 7. Add to this the more systemic revolutions the industry is facing, such as the visualization of social media, which is going to pose huge challenges to an industry that’s been entirely built on text mining.
  • 8. #tumblr: ! 85m post / day! 84% images !
  • 9. social media research is not web analytics!
  • 10. It’s not quantitative data!
  • 11. It’s qualitative data on a quantitative scale!
  • 12. Drop the ‘media’. ! It’s ‘social media data’!
  • 13. It needs social science not ‘media monitoring’!
  • 14. How can 
 social research 
 fix smm 
 in 10 steps!
  • 15. 1!sampling beyond keywords!
  • 16. Great  Britain   Indonesia   USA   Malaysia   09  am   Nigeria   Ireland   India   Spain   France   South  Africa   Other   10  am   11  pm   10  pm   9  pm   8  pm   12  pm   11  am   1  pm   2  pm   7  pm   07  May  2013,  10  pm:   the  rumour  spreads   on  Twi?er   6  pm   5  pm   4  pm   3  pm   How fast does news travel? Conversations about Alex Ferguson retirement by the hour by country
  • 17. 4  maps  by  visibility  
  • 18. 4  maps  by  visibility  
  • 19. Mentions of brand x! @Brand follower activity! 0.1%!
  • 20. Decoding your online audience Discovering clusters of fans using Social Network Analysis
  • 21. " Detecting social communities in " Question Time’s Twitter audience
  • 22. NHAparty! WillBlackWriter! RippedOffBriton! OwenJones84   SalmaYaqoob! johnprescott   richardcalhoun! ChrisBryantMP! PennyRed! steveclarkuk! mehdirhasan! davidschneider! fleetstreetfox! paulwaugh! oflynnexpress! RicHolden! bbc5live! BBCNews! afneil! bbcquestiontime  
  • 23. Orange Cluster 41%   59%   - 42 y.o. White British - Manufacturing professionals, Nurses, Teachers - From London (48%), Manchester (6%), Liverpool (5%) - Jewish, Christian - Into politics, tech news, environment, cooking, tennis; - Following @BBCBreaking, @Ed_Miliband, @Queen_UK. Fuchsia Cluster Green Cluster 37%   63%   - 28 y.o., White British - Students, Musicians and actors - From London (41%), Manchester (10%), Liverpool (4%) - Christian and Jewish - Into partying , reading, comedy, football; - Following @stephenfry, @RealDMitchell and @daraobriain - 32 y.o., White British - Manufacturing professionals, Nurses, Teachers - From London (48%), Manchester (6%), Cardiff (3%) - Jewish, Christian - Into Business news, reading, history, rugby, tennis and golf; - Following @BBCBreaking @Number10gov @Lord_Sugar. 44%   56%   Blue Cluster Pale Blue Cluster 31%   69%   - 20 y.o, White British, Black - Manufacturing professionals, Nurses, Teachers - From London (48%), Manchester (8%), Belfast (4%) - Muslim, Christian - Into Gaming , comedy/humor, sports; - Following @jimmycarr, @andy_murray, @rioferdy5 20%   80%   - 35 y.o., White Birtish - Senior Managers , Journalists, Writers and Lawyers - From London (50%), Manchester (3%), Leeds (2%) - Christian and Jewish - Into Politics, dining and wining, tennis, football; - Following @David_Cameron, @stephenfry, @Number10gov
  • 24. 2! from 
 content to context!
  • 25. © Alexandre Farto aka Vhils 2010 Most Social Media Monitoring platforms focus on just Content
  • 26. © Alexandre Farto aka Vhils 2010 But we also wanted to understand Context and Behaviour
  • 27. Dimensions of Social Data Content Demographics Social Graph Interest Graph Behaviours
  • 28. Nose-to-tail indexing or Bigger Big Data Behaviours   Social  Graph   Demographics   Interest  Graph   Content  
  • 29. Data anthropology
  • 30. 3! from 
 analytics to intelligence
 frameworks!
  • 31. AnalyticsIntelligence How many What time of the week Tweets/Hour? is best for what? How many What’s the brand negatives? equity?
  • 32. Measuring Visibility
  • 33. 4! down 
 break the social silo!
  • 34. Predicting the Oscars Integrating social data with multiple external datasets to increase predictability
  • 35. Images Location EXP #1 Reality Mining Call Log SMS Log Bluetooth Accelerometer Gyroscope Orientation Activity Running Apps Battery Screen Browser Searches THE  PICTURES   AS  TEMPORAL   AND  SPATIAL   MARKERS  OF   THE  JOURNEY  
  • 36. 5! scalable 
 human-analysis!
  • 37. 6!machinelearning!
  • 38. 7! dataUX !
  • 39. A data canvas
  • 40. Visual Mining
  • 41. Data Transparency
  • 42. 8!decisionmaking!
  • 43. 9! hybrid 
 methods!
  • 44. SOCIAL PANELS > real-time segments Dynamic Segments Real-time Audience Insights Pulsar  |  Social  Data  Intelligence        @pulsar_social  |  PulsarPla]orm.com  
  • 45. A Nation Divided? A Social Panels case study 50k readers 50k readers 23,000 Tweets 50k readers 22,000 Tweets 50k readers 50k readers 63,000 Tweets 47,000 Tweets 50k readers 32,000 Tweets 12,000 Tweets
  • 46. A Nation Divided? How 300,000 readers of six top UK newspapers are feeling about the death of Margaret Thatcher Positive towards Thatcher Negative towards Thatcher Neutral: hidden The Daily Mail 23,000 Tweets 10% 8% 23% The Guardian 26% 16% 22,000 Tweets 19% The Daily Mirror 63,000 Tweets The Independent 47,000 Tweets The Telegraph 8% 32,000 Tweets 12% 15% 18% 23% 19%
  • 47. 10! 
 
 making research programmable

  • 48. Social  Data  Intelligence        @pulsar_social  |  PulsarPla]orm.com