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Data and Journalism

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Data and Journalism

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The world of Journalists is changing. Their business model seems to vanish. That is not really true. Their world is only shifting. This lecture focus on the change brought to Journalists by Big Data.

Big Data is "hype-term" but “being data-driven” creates new possibilities for Journalists. The talk goes through the 5 V's of Big Data and why we should focus on small Data.

Several use-cases for Journalists are discussed from Influencers over Reach Metrics to Trend Prediction and Content Validation. A few tools supporting the Journalistic work are introduced.

The world of Journalists is changing. Their business model seems to vanish. That is not really true. Their world is only shifting. This lecture focus on the change brought to Journalists by Big Data.

Big Data is "hype-term" but “being data-driven” creates new possibilities for Journalists. The talk goes through the 5 V's of Big Data and why we should focus on small Data.

Several use-cases for Journalists are discussed from Influencers over Reach Metrics to Trend Prediction and Content Validation. A few tools supporting the Journalistic work are introduced.

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Data and Journalism

  1. 1. Data & Journalism
  2. 2. Lutz Finger Blog  :  LutzFinger.com   linkedin.com/in/lutzfinger     www.facebook.com/lutz.finger   @LutzFinger  
  3. 3. The philosophy of the day is data-ism. - DAVID BROOKS
  4. 4. Why this Hype? Google Searches for “big data” 30+ Petabytes of usergenerated Data 4TB = 11 million PDF of images in 24 HOURS 75 Million events Per day 1) MORE Data - “big data” 2) Better Technology Source:  WikiBon  &  Google  Trends  –  Apr.  2013  
  5. 5. Definition – 5 V’s of “big Data” Volume   Variety   Data at Scale (TB, PB,… ) Data in many Forms (Structured, Unstructured, ..) Velocity   Speed (Streaming, Real Time, Near Time, ..) Veracity   Uncertainty (imprecise, not always up-to-date ..) Value  
  6. 6. BIG Data is Hype… go to SMALL data
  7. 7. small as in… Seizing  market  opportuniPes   Holding  on  to  customers   CompePng  more  effecPvely   BoosPng  financial  performance   0%   10%   20%   30%   40%   50%   60%   70%   Survey  from  C-­‐Level  by  The  Economist  Intelligence  Unit  -­‐  2013  
  8. 8. Be Aware: Social Media Data vs. Usage
  9. 9. Predicting Osama Bin Laden’s Location 200  km   Source:  TwiZer  &  University  of  Tennessee  
  10. 10. Find Influencer
  11. 11. INFLUENCER = EXPERT •  Opinion leaders (Katz 1955) •  Influentials (Merton 1968) •  Law of the Few (Gladwell 2000)
  12. 12. A few person decide what we do…
  13. 13. There are no universal influencers. It’s a myth.
  14. 14. The Reality: Influence is Homophily Influence is often overestimated. •  •  •  •  4 years 1001 Students on Facebook traditional Self-reported Data How did taste Spread Source:  Kevin  Lewisa,  Marco  Gonzaleza  and  Jason   Kaufman  (2012):  PNAS  Vol  109,  no  1   It needs: •  Reach •  Readiness •  Topic Dependence
  15. 15. REACH is the KNOWN Game •  Aja Dior M.? •  AP News? Aja Dior M. omgg, my aunt tiffany who work for whitney houston just found whitney houston dead in the tub. such ashamed & sad :( 45 min
  16. 16. How to measure Reach?
  17. 17. How to measure Reach Only Twitter What will that Mean for the rest?
  18. 18. Engagement as Factor No Name No Name
  19. 19. Many tools offer Engagement Metrics Taken  from  Social  Bakers  
  20. 20. Predict
  21. 21. Be there before the story breaks Study  by  Fisheye  AnalyPcs  
  22. 22. Facebook Parties Study  by  Fisheye  AnalyPcs  
  23. 23. Prediction is difficult – especially about the future. Nils Bohr
  24. 24. Political Prediction Kirsten  Long  &  Rachel  Van  Dongen,  PoliPco,  Dec  12     1. Santorum 2. Romney (-8 votes) 3. Paul (-3.000 votes)
  25. 25. Deep Water Horizon & Social Outreach What is wrong here? Study  by  Fisheye  AnalyPcs  
  26. 26. Content Validation
  27. 27. Many have an interest to reach YOU …
  28. 28. Sometimes with Bots
  29. 29. Bots are easy to create… @you malware.com @you-as-well malware.com D fresh-contact malware.com
  30. 30. Networks try to act… 2011 20% detection 2012 2013 7% SPAM
  31. 31. But they are hard to identify Social Friend @JamesMTitus Knowledge Lajello Silent Influencer @Al_AGW
  32. 32. Example: Online Reviews Arjun  Mukherjee  et.al.    
  33. 33. Tools
  34. 34. Timing is Everything Search  Frequency   Max.  every  Monday   Min.  every  Saturday   “Job”   Search  Frequency   Max.  every  Saturday   Max.  every  Sunday   What  are  the  SEARCH  terms?   Source:  Google  Trends  
  35. 35. Mood of the nation Study  by  Fisheye  AnalyPcs  
  36. 36. Tools for Content discovery Source:  Tame.it  
  37. 37. News of the World 10th June 2011: 2.6 million readers Ready to Switch? Indicated desire to switch 1% Former NOTW subscribers 4% had to look for a new SUNDAY paper To Which Sunday Paper? Star on Sunday General / Misc 9% 5% Sunday Sport Standard 2% The Observer 2% Daily star on Sunday Star Sunday 2% 15% Sunday Time 7% 95% no info on switching Sunday Herald 2% Daily Mail 22% Sun On Sunday 8% Sunday Mirror 26% Timeframe:  Jul  2011   Study  by  Fisheye  AnalyPcs  
  38. 38. Analyze by Audience Source:  PeerIndex  
  39. 39. Automate Monitoring
  40. 40. Difficulty of Keyword Setup

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