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Future Social Media Research
The document outlines a vision for reinventing social media research, emphasizing the need to move beyond traditional monitoring methods by integrating qualitative insights with quantitative data. It proposes ten steps to enhance social media monitoring, including expanding sampling methods, understanding context, and utilizing machine learning and hybrid approaches. The author argues for a shift in focus from mere media monitoring to a more comprehensive analysis of social data, which includes understanding audience behaviors and integrating external datasets.
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Future Social Media Research
- 1. The Future ofSocial Media Research! Or how to re-invent social media monitoring in 10 steps Francesco D’Orazio @abc3d | PulsarPlatform.com
- 2. legacy! Since it emerged15 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 anapproach 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 ! Andwith 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 monitoringis 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 platformsare broken!
- 8. Add to thisthe 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.
- 9. #tumblr: ! 85m post/ day! 84% images !
- 10. social media research isnot web analytics!
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- 12. It’s qualitative data ona quantitative scale!
- 13. Drop the ‘media’. ! It’s‘social media data’!
- 14. It needs social sciencenot ‘media monitoring’!
- 15. How can socialresearch fix smm in 10 steps!
- 16.
- 18. 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
- 21. 4 maps by visibility
- 24. 4 maps by visibility
- 26. Mentions of brandx! @Brand follower activity! 0.1%!
- 27. Decoding your onlineaudience Discovering clusters of fans using Social Network Analysis
- 28. " Detecting social communitiesin " Question Time’s Twitter audience
- 29.
- 30. 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
- 31.
- 32. © Alexandre Fartoaka Vhils 2010 Most Social Media Monitoring platforms focus on just Content
- 33. © Alexandre Fartoaka Vhils 2010 But we also wanted to understand Context and Behaviour
- 34. Dimensions of SocialData Content Demographics Social Graph Interest Graph Behaviours
- 35. Nose-to-tail indexing or BiggerBig Data Behaviours Social Graph Demographics Interest Graph Content
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- 38. AnalyticsIntelligence How many Whattime of the week Tweets/Hour? is best for what? How many What’s the brand negatives? equity?
- 39.
- 40. 4! down break thesocial silo!
- 42. Predicting the Oscars Integrating socialdata with multiple external datasets to increase predictability
- 43. Images Location EXP #1 Reality Mining CallLog SMS Log Bluetooth Accelerometer Gyroscope Orientation Activity Running Apps Battery Screen Browser Searches THE PICTURES AS TEMPORAL AND SPATIAL MARKERS OF THE JOURNEY
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- 57. SOCIAL PANELS >real-time segments Dynamic Segments Real-time Audience Insights Pulsar | Social Data Intelligence @pulsar_social | PulsarPla]orm.com
- 58. A Nation Divided? ASocial 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
- 59. A Nation Divided? How300,000 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%
- 60.
- 63. Social Data Intelligence @pulsar_social | PulsarPla]orm.com