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Data Visualization at Twitter

Krist Wongsuphasawat, profile picture
Krist Wongsuphasawat

This document summarizes Krist Wongsuphasawat's work in data visualization at Twitter. It describes how he obtains tweet data, visualizes it using tools like R and D3 to show trends over time, locations, and text. Examples include visualizations of events like the World Cup and State of the Union. The process involves getting relevant data, visualizing it, evaluating the results, and iterating. The goal is to transform big Twitter data into smaller, insightful visualizations that tell stories.

Data & Analytics
Related topics:
Data Visualization Techniques
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visualization at Twitter data Krist Wongsuphasawat / @kristw
Krist Wongsuphasawat / @kristw
Bangkok, Thailand Krist Wongsuphasawat / @kristw
Computer Engineer Bangkok, Thailand Chulalongkorn University Krist Wongsuphasawat / @kristw
Computer Engineer Bangkok, Thailand Programming + Soccer Krist Wongsuphasawat / @kristw
Computer Engineer Bangkok, Thailand Programming + Soccer Krist Wongsuphasawat / @kristw
Computer Engineer Bangkok, Thailand Programming + Soccer Krist Wongsuphasawat / @kristw
Computer Engineer Bangkok, Thailand M.S. in Computer Science Univ. of Maryland Krist Wongsuphasawat / @kristw
Computer Engineer Bangkok, Thailand PhD in Computer Science Univ. of Maryland Information Visualization Krist Wongsuphasawat / @kristw
Computer Engineer Bangkok, Thailand PhD in Computer Science Univ. of Maryland Information Visualization Krist Wongsuphasawat / @kristw IBM Microsoft
Computer Engineer Bangkok, Thailand PhD in Computer Science Univ. of Maryland Information Visualization Krist Wongsuphasawat / @kristw IBM Microsoft Sr. Data Visualization Scientist Twitter
data visualization at Twitter
data visualization at Twitter
visualization data at Twitter
vis data at Twitter
data at Twitter “Tweets”
data at Twitter “Tweets” #events TV Shows New Year Earthquake Oscars Protest Super Bowl World Cup Election Breaking news …
data at Twitter “Tweets” #events TV Shows New Year Earthquake Oscars Protest Super Bowl World Cup Election Breaking news … #curiosity Sleep pattern Human behavior Language …
data at Twitter “Tweets” #events TV Shows New Year Earthquake Oscars Protest Super Bowl World Cup Election Breaking news … #curiosity Sleep pattern Human behavior Language … What could we learn from the Tweets?
vis data at Twitter “Tweets” Tell stories about an event, Pursue curiosity or inspiration Goal:
vis data at Twitter “Tweets” Tell stories about an event, Pursue curiosity or inspiration (with deadline) Goal:
Challenge accepted
vis data at Twitter “Tweets” Get data 1
easy?
Having all Tweets How people think I feel.
Having all Tweets How people think I feel. How I really feel.
Challenges • Too much data • Want only relevant Tweets • hashtag: #BRA • keywords: “goal” • Need to aggregate & reduce size • Long processing time (hours)
Hadoop Cluster Vertica Pig / Scalding (slow) SQL Data Storage Tool Workflow
Hadoop Cluster Vertica Pig / Scalding (slow) SQL Data Storage Tool Workflow
Workflow Hadoop Cluster Vertica Pig / Scalding (slow) SQL Data Storage Tool Your laptop Smaller dataset
Hadoop Cluster Vertica Pig / Scalding (slow) SQL Data Storage Tool Tool node.js / python / excel (fast) Final dataset Your laptop Workflow Smaller dataset
vis data at Twitter “Tweets” Get data 1 2 Visualize
Visualize • Peek into data • Check data & test ideas • Decide how to visualize • Guided by data type • Choose tools • Start building
Visualize • Peek into data • Check data & test ideas • Decide how to visualize • Guided by data type • Choose tools • Start building R d3 Tableau Yeoman
(+ media) photos, videos data What? TEXT Where? When? GEO TIME
Visualize Data What? TEXT Where? When? GEO TIME
Visualize Data What? TEXT Where? When? GEO TIME
Time Tweets/second
Time Tweets/second
Time Tweets/second + Annotation http://www.flickr.com/photos/twitteroffice/5681263084/
Visualize Data What? TEXT Where? When? GEO TIME
Geo Heatmap Low density High density
Geo San Francisco flickr.com/photos/twitteroffice/8798020541 Low density High density
Geo San Francisco Rebuild the world based on tweet volumes twitter.github.io/interactive/andes/
Visualize Data What? TEXT Where? When? GEO TIME
Text www.wordle.net Some experiments during World Cup
Text www.wordle.net Word cloud of Tweets right after the 1st goal
Text Word cloud of Tweets right after the 1st goal It was an “own” goal. www.wordle.net
Text WordTree [Wattenberg & Viégas 2008] www.jasondavies.com/wordtree www.jasondavies.com/wordtree
Visualize Data What? TEXT Where? When? GEO TIME
Time + Geo Japan Earthquake 2011 blog.twitter.com/2011/global-pulse youtu.be/SybWjN9pKQk
Data Visualization at Twitter
Data Visualization at Twitter
Time + Geo Tweet pattern [Rios & Lin 2012] Night Late night Daytime Night Late night Daytime
Time + Geo Tweet pattern [Rios & Lin 2012] Night Late night Daytime Night Late night Daytime
Time + Geo Tweet pattern [Rios & Lin 2012] Night Late night Daytime Night Late night Daytime
Time + Geo Tweet pattern [Rios & Lin 2012] Night Late night Daytime Night Late night Daytime
Visualize Data What? TEXT Where? When? GEO TIME
Geo + Text Real-time Tweet map
Geo + Text Real-time Tweet map
Geo + Text Real-time Tweet map most frequent term
Geo + Text Real-time Tweet map Gmail was down Jan 24, 2014
Geo + Text Real-time Tweet map Nelson Mandela passed away Dec 5, 2013
Visualize Data What? TEXT Where? When? GEO TIME
Time + Text UEFA Champions League Biggest tournament for European soccer clubs Many Tweets during the matches
UEFA Champions League Team 1 Team 2 Time + Text Dortmund Bayern Munich
UEFA Champions League Team 1 Team 2 Time + Text Dortmund Bayern Munich
UEFA Champions League Team 1 Team 2 Time + Text Dortmund Bayern Munich
UEFA Champions League Team 1 Team 2 Dortmund Bayern Munich Count Tweets mentioning the teams every minute Time + Text
Time + Text UEFA Champions League
Time + Text UEFA Champions League + “goal” count + context
+ “offside” Time + Text UEFA Champions League
+ players Time + Text UEFA Champions League
Competition Tree vs vs A B C D vs A C C
Competition Tree vs vs A B C D vs + A C C
Competition Tree vs vs A B C D vs + = A C C
Visualize Data What? TEXT Where? When? GEO TIME
Time + Text + Geo State of the Union twitter.github.io/interactive/sotu2014
Time + Text + Geo State of the Union 1) timeline + topic from Tweets 4) Density map of Tweets about selected topic 3) Volume of Tweets by topics during selected part of the SOTU 2) context (speech) twitter.github.io/interactive/sotu2014
Time + Text World Cup 2014
Time + Text + Geo World Cup 2014
Visualize Data What? TEXT Where? When? GEO TIME
Visualize Data What? TEXT + Where? When? GEO TIME Non-Twitter data CONTEXT
Time + Text New Year 2014
Time + Text New Year 2014
Time + Text + Geo (c) New Year 2014 twitter.github.io/interactive/newyear2014/
vis data at Twitter “Tweets” Get data 1 2 Visualize
vis data at Twitter “Tweets” Get data 1 2 Visualize Evaluate 3
vis data at Twitter “Tweets” Get data 1 2 Visualize Evaluate 3 Iterate!
Evaluation • Self • Peer feedback • Non team members / Potential audience
vis data at Twitter Get data 1 2 Visualize Evaluate 3
vis data at Twitter Get data 1 2 Visualize Evaluate 3 big data => small data
vis data at Twitter Get data 1 2 Visualize Evaluate 3 big data => small data What? Where? When?
big data => small data self, peer, external vis data at Twitter Get data 1 2 Visualize Evaluate 3 What? Where? When?
big data => small data self, peer, external vis data at Twitter “Tweets” Get data 1 2 Visualize Evaluate 3 What? Where? When?
big data => small data self, peer, external vis data at Twitter “Tweets” Get data 1 2 Visualize Evaluate 3 What? Where? When? • users • followers graph • logs • etc. ! • derived data: language, sentiment
big data => small data self, peer, external vis data at Twitter “Tweets” Get data 1 2 Visualize Evaluate 3 What? Where? When? • users Who? … • followers graph • logs • etc. ! • derived data: language, sentiment
big data => small data self, peer, external vis data at Twitter “Tweets” Get data 1 2 Visualize Evaluate 3 What? Where? When? • users Who? … • followers graph • logs • etc. ! • derived data: language, sentiment (with deadline)
big data => small data self, peer, external vis data at Twitter “Tweets” Get data 1 2 Visualize Evaluate 3 What? Where? When? • users Who? … • followers graph • logs • etc. (with deadline) ! • derived data: language, sentiment @kristw / https://interactive.twitter.com
big data => small data self, peer, external vis data at Twitter “Tweets” Get data 1 2 Visualize Evaluate 3 What? Where? When? • users Who? … • followers graph • logs • etc. (with deadline) ! • derived data: language, sentiment @kristw / https://interactive.twitter.com + visualizations by @philogb, @miguelrios & @trebor
Questions?
big data => small data self, peer, external vis data at Twitter “Tweets” Get data 1 2 Visualize Evaluate 3 What? Where? When? • users Who? … • followers graph • logs • etc. (with deadline) @kristw / https://interactive.twitter.com + visualizations by @philogb, @miguelrios & @trebor
Thank you

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Data Visualization at Twitter

  • 1. visualization at Twitter data Krist Wongsuphasawat / @kristw
  • 3. Bangkok, Thailand Krist Wongsuphasawat / @kristw
  • 4. Computer Engineer Bangkok, Thailand Chulalongkorn University Krist Wongsuphasawat / @kristw
  • 5. Computer Engineer Bangkok, Thailand Programming + Soccer Krist Wongsuphasawat / @kristw
  • 6. Computer Engineer Bangkok, Thailand Programming + Soccer Krist Wongsuphasawat / @kristw
  • 7. Computer Engineer Bangkok, Thailand Programming + Soccer Krist Wongsuphasawat / @kristw
  • 8. Computer Engineer Bangkok, Thailand M.S. in Computer Science Univ. of Maryland Krist Wongsuphasawat / @kristw
  • 9. Computer Engineer Bangkok, Thailand PhD in Computer Science Univ. of Maryland Information Visualization Krist Wongsuphasawat / @kristw
  • 10. Computer Engineer Bangkok, Thailand PhD in Computer Science Univ. of Maryland Information Visualization Krist Wongsuphasawat / @kristw IBM Microsoft
  • 11. Computer Engineer Bangkok, Thailand PhD in Computer Science Univ. of Maryland Information Visualization Krist Wongsuphasawat / @kristw IBM Microsoft Sr. Data Visualization Scientist Twitter
  • 15. vis data at Twitter
  • 16. data at Twitter “Tweets”
  • 17. data at Twitter “Tweets” #events TV Shows New Year Earthquake Oscars Protest Super Bowl World Cup Election Breaking news …
  • 18. data at Twitter “Tweets” #events TV Shows New Year Earthquake Oscars Protest Super Bowl World Cup Election Breaking news … #curiosity Sleep pattern Human behavior Language …
  • 19. data at Twitter “Tweets” #events TV Shows New Year Earthquake Oscars Protest Super Bowl World Cup Election Breaking news … #curiosity Sleep pattern Human behavior Language … What could we learn from the Tweets?
  • 20. vis data at Twitter “Tweets” Tell stories about an event, Pursue curiosity or inspiration Goal:
  • 21. vis data at Twitter “Tweets” Tell stories about an event, Pursue curiosity or inspiration (with deadline) Goal:
  • 23. vis data at Twitter “Tweets” Get data 1
  • 24. easy?
  • 25. Having all Tweets How people think I feel.
  • 26. Having all Tweets How people think I feel. How I really feel.
  • 27. Challenges • Too much data • Want only relevant Tweets • hashtag: #BRA • keywords: “goal” • Need to aggregate & reduce size • Long processing time (hours)
  • 28. Hadoop Cluster Vertica Pig / Scalding (slow) SQL Data Storage Tool Workflow
  • 29. Hadoop Cluster Vertica Pig / Scalding (slow) SQL Data Storage Tool Workflow
  • 30. Workflow Hadoop Cluster Vertica Pig / Scalding (slow) SQL Data Storage Tool Your laptop Smaller dataset
  • 31. Hadoop Cluster Vertica Pig / Scalding (slow) SQL Data Storage Tool Tool node.js / python / excel (fast) Final dataset Your laptop Workflow Smaller dataset
  • 32. vis data at Twitter “Tweets” Get data 1 2 Visualize
  • 33. Visualize • Peek into data • Check data & test ideas • Decide how to visualize • Guided by data type • Choose tools • Start building
  • 34. Visualize • Peek into data • Check data & test ideas • Decide how to visualize • Guided by data type • Choose tools • Start building R d3 Tableau Yeoman
  • 35. (+ media) photos, videos data What? TEXT Where? When? GEO TIME
  • 36. Visualize Data What? TEXT Where? When? GEO TIME
  • 37. Visualize Data What? TEXT Where? When? GEO TIME
  • 40. Time Tweets/second + Annotation http://www.flickr.com/photos/twitteroffice/5681263084/
  • 41. Visualize Data What? TEXT Where? When? GEO TIME
  • 42. Geo Heatmap Low density High density
  • 43. Geo San Francisco flickr.com/photos/twitteroffice/8798020541 Low density High density
  • 44. Geo San Francisco Rebuild the world based on tweet volumes twitter.github.io/interactive/andes/
  • 45. Visualize Data What? TEXT Where? When? GEO TIME
  • 46. Text www.wordle.net Some experiments during World Cup
  • 47. Text www.wordle.net Word cloud of Tweets right after the 1st goal
  • 48. Text Word cloud of Tweets right after the 1st goal It was an “own” goal. www.wordle.net
  • 49. Text WordTree [Wattenberg & Viégas 2008] www.jasondavies.com/wordtree www.jasondavies.com/wordtree
  • 50. Visualize Data What? TEXT Where? When? GEO TIME
  • 51. Time + Geo Japan Earthquake 2011 blog.twitter.com/2011/global-pulse youtu.be/SybWjN9pKQk
  • 54. Time + Geo Tweet pattern [Rios & Lin 2012] Night Late night Daytime Night Late night Daytime
  • 55. Time + Geo Tweet pattern [Rios & Lin 2012] Night Late night Daytime Night Late night Daytime
  • 56. Time + Geo Tweet pattern [Rios & Lin 2012] Night Late night Daytime Night Late night Daytime
  • 57. Time + Geo Tweet pattern [Rios & Lin 2012] Night Late night Daytime Night Late night Daytime
  • 58. Visualize Data What? TEXT Where? When? GEO TIME
  • 59. Geo + Text Real-time Tweet map
  • 60. Geo + Text Real-time Tweet map
  • 61. Geo + Text Real-time Tweet map most frequent term
  • 62. Geo + Text Real-time Tweet map Gmail was down Jan 24, 2014
  • 63. Geo + Text Real-time Tweet map Nelson Mandela passed away Dec 5, 2013
  • 64. Visualize Data What? TEXT Where? When? GEO TIME
  • 65. Time + Text UEFA Champions League Biggest tournament for European soccer clubs Many Tweets during the matches
  • 66. UEFA Champions League Team 1 Team 2 Time + Text Dortmund Bayern Munich
  • 67. UEFA Champions League Team 1 Team 2 Time + Text Dortmund Bayern Munich
  • 68. UEFA Champions League Team 1 Team 2 Time + Text Dortmund Bayern Munich
  • 69. UEFA Champions League Team 1 Team 2 Dortmund Bayern Munich Count Tweets mentioning the teams every minute Time + Text
  • 70. Time + Text UEFA Champions League
  • 71. Time + Text UEFA Champions League + “goal” count + context
  • 72. + “offside” Time + Text UEFA Champions League
  • 73. + players Time + Text UEFA Champions League
  • 74. Competition Tree vs vs A B C D vs A C C
  • 75. Competition Tree vs vs A B C D vs + A C C
  • 76. Competition Tree vs vs A B C D vs + = A C C
  • 77. Visualize Data What? TEXT Where? When? GEO TIME
  • 78. Time + Text + Geo State of the Union twitter.github.io/interactive/sotu2014
  • 79. Time + Text + Geo State of the Union 1) timeline + topic from Tweets 4) Density map of Tweets about selected topic 3) Volume of Tweets by topics during selected part of the SOTU 2) context (speech) twitter.github.io/interactive/sotu2014
  • 80. Time + Text World Cup 2014
  • 81. Time + Text + Geo World Cup 2014
  • 82. Visualize Data What? TEXT Where? When? GEO TIME
  • 83. Visualize Data What? TEXT + Where? When? GEO TIME Non-Twitter data CONTEXT
  • 84. Time + Text New Year 2014
  • 85. Time + Text New Year 2014
  • 86. Time + Text + Geo (c) New Year 2014 twitter.github.io/interactive/newyear2014/
  • 87. vis data at Twitter “Tweets” Get data 1 2 Visualize
  • 88. vis data at Twitter “Tweets” Get data 1 2 Visualize Evaluate 3
  • 89. vis data at Twitter “Tweets” Get data 1 2 Visualize Evaluate 3 Iterate!
  • 90. Evaluation • Self • Peer feedback • Non team members / Potential audience
  • 91. vis data at Twitter Get data 1 2 Visualize Evaluate 3
  • 92. vis data at Twitter Get data 1 2 Visualize Evaluate 3 big data => small data
  • 93. vis data at Twitter Get data 1 2 Visualize Evaluate 3 big data => small data What? Where? When?
  • 94. big data => small data self, peer, external vis data at Twitter Get data 1 2 Visualize Evaluate 3 What? Where? When?
  • 95. big data => small data self, peer, external vis data at Twitter “Tweets” Get data 1 2 Visualize Evaluate 3 What? Where? When?
  • 96. big data => small data self, peer, external vis data at Twitter “Tweets” Get data 1 2 Visualize Evaluate 3 What? Where? When? • users • followers graph • logs • etc. ! • derived data: language, sentiment
  • 97. big data => small data self, peer, external vis data at Twitter “Tweets” Get data 1 2 Visualize Evaluate 3 What? Where? When? • users Who? … • followers graph • logs • etc. ! • derived data: language, sentiment
  • 98. big data => small data self, peer, external vis data at Twitter “Tweets” Get data 1 2 Visualize Evaluate 3 What? Where? When? • users Who? … • followers graph • logs • etc. ! • derived data: language, sentiment (with deadline)
  • 99. big data => small data self, peer, external vis data at Twitter “Tweets” Get data 1 2 Visualize Evaluate 3 What? Where? When? • users Who? … • followers graph • logs • etc. (with deadline) ! • derived data: language, sentiment @kristw / https://interactive.twitter.com
  • 100. big data => small data self, peer, external vis data at Twitter “Tweets” Get data 1 2 Visualize Evaluate 3 What? Where? When? • users Who? … • followers graph • logs • etc. (with deadline) ! • derived data: language, sentiment @kristw / https://interactive.twitter.com + visualizations by @philogb, @miguelrios & @trebor
  • 102. big data => small data self, peer, external vis data at Twitter “Tweets” Get data 1 2 Visualize Evaluate 3 What? Where? When? • users Who? … • followers graph • logs • etc. (with deadline) @kristw / https://interactive.twitter.com + visualizations by @philogb, @miguelrios & @trebor