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What to Expect When the Unexpected Happens: Social Media Communications Across Crises

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Alexandra Olteanu, Sarah Vieweg and Carlos Castillo: "What to Expect When the Unexpected Happens: Social Media Communications Across Crises" In CSCW 2015, 14-18 March in Vancouver, Canada. ACM Press.

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What to Expect When the Unexpected Happens: Social Media Communications Across Crises

  1. 1. What to Expect When The Unexpected Happens: Social Media Communications Across Crises ! Alexandra Olteanu Sarah Vieweg, Carlos Castillo
  2. 2. Leetaru et al. "Mapping the global Twitter heartbeat: The geography of Twitter." First Monday (2013)
  3. 3. from ReliefWeb.int—a digital service by UN OCHA, 25.02.2015
  4. 4. What are the similarities and differences in Twitter communications that take place during different crisis events, according to specific characteristics of such events?
  5. 5. What are the similarities and differences in Twitter communications that take place during different crisis events, according to specific characteristics of such events?
  6. 6. What are the similarities and differences in Twitter communications that take place during different crisis events, according to specific characteristics of such events?
  7. 7. Analysis Framework Crisis Dimensions Temporal Development G eographic Spread CrisisType natural human-induced
  8. 8. Analysis Framework Crisis Dimensions Temporal Development G eographic Spread CrisisType natural human-induced
  9. 9. Analysis Framework Crisis Dimensions Content Dimensions Eyewitness Government NGOs Media Business Outsiders Affected individuals Infrastructure Damage Donations Caution & Advice Sympathy Other Source Type
  10. 10. Analysis Framework Crisis Dimensions Content Dimensions not informative informative Eyewitness Government NGOs Media Business Outsiders Affected individuals Infrastructure Damage Donations Caution & Advice Sympathy Other Source Type
  11. 11. Analysis Framework Crisis Dimensions Content Dimensions Data Collection
  12. 12. Analysis Framework Crisis Dimensions Content Dimensions Data Collection Data Annotation
  13. 13. Analysis Framework Crisis Dimensions Content Dimensions Data Collection Data Annotation Data Analysis Costa Rica earthquake’12 Manila floods’13 Singapore haze’13 Queensland floods’13 Typhoon Pablo’12 Australia bushfire’13 Italy earthquakes’12 Sardinia floods’13 Philipinnes floods’12 Alberta floods’13 Typhoon Yolanda’13 Colorado floods’13 Guatemala earthquake’12 Colorado wildfires’12 Bohol earthquake’13 NY train crash’13 Boston bombings’13 LA airport shootings’13 West Texas explosion’13 Russia meteor’13 Savar building collapse’13 Lac Megantic train crash’13 Venezuela refinery’12 Glasgow helicopter crash’13 Spain train crash’13 Brazil nightclub fire’13 0 10 20 30 40 50 60 70 80 90 100 Caution & Advice Affected Ind. Infrast. & Utilities Donat. & Volun. Sympathy Other Useful Info. Media Outsiders Eyewitness Government NGOs Business Other Useful Info. Sympathy Affected Ind. Donat. & Volun. Caution & Advice Infrast. & Utilities 0 5% 10% >15% Savar building collapse’13 LA airport shootings’13 NY train crash’13 Russia meteor’13 Colorado wildfires’12 Guatemala earthquake’12 Glasgow helicopter crash’13 West Texas explosion’13 Lac Megantic train crash’13 Venezuela refinery’12 Bohol earthquake’13 Boston bombings’13 Brazil nightclub fire’13 Spain train crash’13 Manila floods’13 Alberta floods’13 Philipinnes floods’12 Typhoon Yolanda’13 Costa Rica earthquake’12 Singapore haze’13 Italy earthquakes’12 Australia bushfire’13 Queensland floods’13 Colorado floods’13 Sardinia floods’13 Typhoon Pablo’12
  14. 14. Analysis Framework Crisis Dimensions Content Dimensions Data Collection Data Annotation Data Analysis Step 1 Step 2 Step 3 Step 4 Step 5
  15. 15. Step 1: Events Typology Natural Human-Induced Progressive Focalized Epidemics Demonstrations, Riots Diffused Floods, Storms, Wildfires Wars Instantaneous Focalized Meteorites, Landslides Train accidents, Building collapse Diffused Earthquakes Large-scale industrial accidents
  16. 16. Step 2: Content Dimensions
  17. 17. Step 2: Content Dimensions
  18. 18. Step 2: Content Dimensions ☞ Informativeness Informative: useful information, situational information, etc. Not informative: prayers, trolling, spam, humor, rumor, off-topic, etc. ☞ Source of information Primary sources Eyewitness: citizen reporters, local individuals, direct experience Secondary & tertiary sources Government: authorities, police & fire services, public institutions NGOs: non-profit org., non-governmental org., faith-based org. Business: comercial org., enterprises, for-profit corporation Media: news org., journalists, news media! Outsiders: remote crowd, non-locals, sympathizers ☞ Type of information ! ! ! ! ! ! ! ! !
  19. 19. Step 2: Content Dimensions ☞ Informativeness Informative: useful information, situational information, etc. Not informative: prayers, trolling, spam, humor, rumor, off-topic, etc. ☞ Source of information Primary sources Eyewitness: citizen reporters, local individuals, direct experience Secondary & tertiary sources Government: authorities, police & fire services, public institutions NGOs: non-profit org., non-governmental org., faith-based org. Business: comercial org., enterprises, for-profit corporation Media: news org., journalists, news media! ☞ Type of information ! ! ! ! ! !
  20. 20. Step 2: Content Dimensions ☞ Informativeness Informative: useful information, situational information, etc. Not informative: prayers, trolling, spam, humor, rumor, off-topic, etc. ☞ Source of information Primary sources Eyewitness: citizen reporters, local individuals, direct experience Secondary & tertiary sources Government: authorities, police & fire services, public institutions NGOs: non-profit org., non-governmental org., faith-based org. Business: comercial org., enterprises, for-profit corporation Media: news org., journalists, news media! Outsiders: remote crowd, non-locals, sympathizers ☞ Type of information ! ! ! ! ! ! ! ! !
  21. 21. Step 2: Content Dimensions ☞ Informativeness Informative: useful information, situational information, etc. Not informative: prayers, trolling, spam, humor, rumor, off-topic, etc. ☞ Source of information Primary sources Eyewitness: citizen reporters, local individuals, direct experience Secondary & tertiary sources Government: authorities, police & fire services, public institutions NGOs: non-profit org., non-governmental org., faith-based org. Business: comercial org., enterprises, for-profit corporation Media: news org., journalists, news media! Outsiders: remote crowd, non-locals, sympathizers ☞ Type of information
  22. 22. Step 2: Content Dimensions ☞ Informativeness Informative: useful information, situational information, etc. Not informative: prayers, trolling, spam, humor, rumor, off-topic, etc. ☞ Source of information Primary sources Eyewitness: citizen reporters, local individuals, direct experience Secondary & tertiary sources Government: authorities, police & fire services, public institutions NGOs: non-profit org., non-governmental org., faith-based org. Business: comercial org., enterprises, for-profit corporation Media: news org., journalists, news media! Outsiders: remote crowd, non-locals, sympathizers ☞ Type of information ! ! ! ! ! ! ! ! !
  23. 23. Step 2: Content Dimensions ☞ Informativeness Informative: useful information, situational information, etc. Not informative: prayers, trolling, spam, humor, rumor, off-topic, etc. ☞ Source of information Primary sources Eyewitness: citizen reporters, local individuals, direct experience Secondary & tertiary sources Government: authorities, police & fire services, public institutions NGOs: non-profit org., non-governmental org., faith-based org. Business: comercial org., enterprises, for-profit corporation Media: news org., journalists, news media! Outsiders: remote crowd, non-locals, sympathizers ☞ Type of information ! ! ! ! ! ! ! ! ! Affected individuals! casualties; people missing, found, trapped, seen; reports about self Infrastructure & utilities! road closures, collapsed structure, water sanitation, services Donations & volunteering requesting help, proposing relief, relief coordination, shelter needed Caution & advice! predicting or forecasting, instructions to handle certain situations Sympathy & emotional support! thanks, gratitude, prayers, condolences, emotion-related Other useful information! meta-discussions, flood level, wind, visibility, weather
  24. 24. Step 3: Data Collection ☞ Twitter base-sample* ☞ 2012 & 2013 ☞ ~1% random sample of Twitter public stream ☞ ~130+ million tweets per month ☞ Keyword-based searches ☞ proper names of affected location ☞ manila floods, boston bombings, #newyork derailment ☞ proper names of meteorological phenomena ☞ sandy hurricane, typhoon yolanda ☞ promoted hashtags ☞ #SafeNow, #RescuePH, #ReliefPH ☞ 26 crisis events ☞ 14 countries and 8 languages ☞ 12 different hazard types ☞ earthquake, wildfire, floods, bombings, shootings, etc. ! ☞ 15 instantaneous crises ☞ 15 diffused crises * https://archive.org/details/twitterstream
  25. 25. Step 3: Data Collection ☞ Twitter base-sample* ☞ 2012 & 2013 ☞ ~1% random sample of Twitter public stream ☞ ~130+ million tweets per month ☞ Keyword-based searches ☞ proper names of affected location ☞ manila floods, boston bombings, #newyork derailment ☞ proper names of meteorological phenomena ☞ sandy hurricane, typhoon yolanda ☞ promoted hashtags ☞ #SafeNow, #RescuePH, #ReliefPH ☞ 26 crisis events ☞ 14 countries and 8 languages ☞ 12 different hazard types ☞ earthquake, wildfire, floods, bombings, shootings, etc. ! ☞ 15 instantaneous crises ☞ 15 diffused crises * https://archive.org/details/twitterstream
  26. 26. Step 3: Data Collection ☞ Twitter base-sample* ☞ 2012 & 2013 ☞ ~1% random sample of Twitter public stream ☞ ~130+ million tweets per month ☞ Keyword-based searches ☞ proper names of affected location ☞ manila floods, boston bombings, #newyork derailment ☞ proper names of meteorological phenomena ☞ sandy hurricane, typhoon yolanda ☞ promoted hashtags ☞ #SafeNow, #RescuePH, #ReliefPH ☞ 26 crisis events ☞ 14 countries and 8 languages ☞ 12 different hazard types ☞ earthquake, wildfire, floods, bombings, shootings, etc. ! ☞ 15 instantaneous crises ☞ 15 diffused crises * https://archive.org/details/twitterstream
  27. 27. Step 3: Data Collection ☞ Twitter base-sample* ☞ 2012 & 2013 ☞ ~1% random sample of Twitter public stream ☞ ~130+ million tweets per month ☞ Keyword-based searches ☞ proper names of affected location ☞ manila floods, boston bombings, #newyork derailment ☞ proper names of meteorological phenomena ☞ sandy hurricane, typhoon yolanda ☞ promoted hashtags ☞ #SafeNow, #RescuePH, #ReliefPH ☞ 26 crisis events ☞ 14 countries and 8 languages ☞ 12 different hazard types ☞ earthquake, wildfire, floods, bombings, shootings, etc. ! ☞ 15 instantaneous crises ☞ 15 diffused crises * https://archive.org/details/twitterstream
  28. 28. Step 3: Data Collection ☞ Twitter base-sample* ☞ 2012 & 2013 ☞ ~1% random sample of Twitter public stream ☞ ~130+ million tweets per month ☞ Keyword-based searches ☞ proper names of affected location ☞ manila floods, boston bombings, #newyork derailment ☞ proper names of meteorological phenomena ☞ sandy hurricane, typhoon yolanda ☞ promoted hashtags ☞ #SafeNow, #RescuePH, #ReliefPH ☞ 26 crisis events ☞ 14 countries and 8 languages ☞ 12 different hazard types ☞ earthquake, wildfire, floods, bombings, shootings, etc. ! ☞ 15 instantaneous crises ☞ 15 diffused crises * https://archive.org/details/twitterstream
  29. 29. Step 3: Data Collection ☞ Twitter base-sample* ☞ 2012 & 2013 ☞ ~1% random sample of Twitter public stream ☞ ~130+ million tweets per month ☞ Keyword-based searches ☞ proper names of affected location ☞ manila floods, boston bombings, #newyork derailment ☞ proper names of meteorological phenomena ☞ sandy hurricane, typhoon yolanda ☞ promoted hashtags ☞ #SafeNow, #RescuePH, #ReliefPH ☞ 26 crisis events ☞ 14 countries and 8 languages ☞ 12 different hazard types ☞ earthquake, wildfire, floods, bombings, shootings, etc. ! ☞ 15 instantaneous crises ☞ 15 diffused crises * https://archive.org/details/twitterstream
  30. 30. Step 4: Data Annotation ☞ ~1000 tweets per crisis ☞ Informativeness ☞ Source of information ☞ Type of information ! ☞ Crowdsource workers from the affected countries
  31. 31. Step 5: Data Analysis ☞ Content types/sources vs. crisis dimensions ☞ Interplay between types and sources ☞ Crisis similarity ☞ Temporal aspects
  32. 32. What: Types of Information Infrastructure and utilities: 7% on average (min. 0%, max. 22%)! most prevalent in diffused crises, in particular during floods! Caution and advice: 10% on average (min. 0%, max. 34%)! least prevalent in instantaneous & human-induced disasters! Donations and volunteering: 10% on average (min. 0%, max. 44%)! most prevalent in natural disasters Costa Rica earthquake’12 Manila floods’13 Singapore haze’13 Queensland floods’13 Typhoon Pablo’12 Australia bushfire’13 Italy earthquakes’12 Sardinia floods’13 Philipinnes floods’12 Alberta floods’13 Typhoon Yolanda’13 Colorado floods’13 Guatemala earthquake’12 Colorado wildfires’12 Bohol earthquake’13 NY train crash’13 Boston bombings’13 LA airport shootings’13 West Texas explosion’13 Russia meteor’13 Savar building collapse’13 Lac Megantic train crash’13 Venezuela refinery’12 Glasgow helicopter crash’13 Spain train crash’13 Brazil nightclub fire’13 0 10 20 30 40 50 60 70 80 90 100 Caution & Advice Affected Ind. Infrast. & Utilities Donat. & Volun. Sympathy Other Useful Info.
  33. 33. Affected individuals: 20% on average (min. 5%, max. 57%)! most prevalent in human-induced, focalized & instantaneous crises! Sympathy and emotional support: 20% on average (min. 3%, max. 52%)! most prevalent in instantaneous crises! Other useful information: 32% on average (min. 7%, max. 59%)! least prevalent in diffused crises Costa Rica earthquake’12 Manila floods’13 Singapore haze’13 Queensland floods’13 Typhoon Pablo’12 Australia bushfire’13 Italy earthquakes’12 Sardinia floods’13 Philipinnes floods’12 Alberta floods’13 Typhoon Yolanda’13 Colorado floods’13 Guatemala earthquake’12 Colorado wildfires’12 Bohol earthquake’13 NY train crash’13 Boston bombings’13 LA airport shootings’13 West Texas explosion’13 Russia meteor’13 Savar building collapse’13 Lac Megantic train crash’13 Venezuela refinery’12 Glasgow helicopter crash’13 Spain train crash’13 Brazil nightclub fire’13 0 10 20 30 40 50 60 70 80 90 100 Caution & Advice Affected Ind. Infrast. & Utilities Donat. & Volun. Sympathy Other Useful Info. What: Types of Information
  34. 34. Who: Sources of Information Singapore haze’13 Philipinnes floods’12 Alberta floods’13 Manila floods’13 Queensland floods’13 Typhoon Pablo’12 Italy earthquakes’12 Australia bushfire’13 Colorado floods’13 Colorado wildfires’12 Bohol earthquake’13 Costa Rica earthquake’12 LA airport shootings’13 Venezuela refinery’12 West Texas explosion’13 Sardinia floods’13 Spain train crash’13 Guatemala earthquake’12 Brazil nightclub fire’13 Boston bombings’13 Glasgow helicopter crash’13 Russia meteor’13 Lac Megantic train crash’13 Typhoon Yolanda’13 Savar building collapse’13 NY train crash’13 0 10 20 30 40 50 60 70 80 90 100 Eyewitness Government NGOs Business Media Outsiders Business: 2% on average (min. 0%, max. 9%)! most prevalent in diffused crises! NGOs: 4% on average (min. 0%, max. 17%)! most prevalent in natural disasters, in particular during typhoons & floods! Government: 5% on average (min. 1%, max. 13%)! most prevalent in natural, progressive & diffused crises
  35. 35. Eyewitness accounts: 9% on average (min. 0%, max. 54%)! most prevalent in progressive & diffused crises! Outsiders: 38% on average (min. 3%, max. 65%) ! least in the Singapore Haze crisis! Traditional & Internet media: 42% on average (min. 18%, max. 77%) ! most prevalent in instantaneous crises, which make the “breaking news” Who: Sources of Information Singapore haze’13 Philipinnes floods’12 Alberta floods’13 Manila floods’13 Queensland floods’13 Typhoon Pablo’12 Italy earthquakes’12 Australia bushfire’13 Colorado floods’13 Colorado wildfires’12 Bohol earthquake’13 Costa Rica earthquake’12 LA airport shootings’13 Venezuela refinery’12 West Texas explosion’13 Sardinia floods’13 Spain train crash’13 Guatemala earthquake’12 Brazil nightclub fire’13 Boston bombings’13 Glasgow helicopter crash’13 Russia meteor’13 Lac Megantic train crash’13 Typhoon Yolanda’13 Savar building collapse’13 NY train crash’13 0 10 20 30 40 50 60 70 80 90 100 Eyewitness Government NGOs Business Media Outsiders
  36. 36. Events Similarity: Information Types Savar building collapse’13 LA airport shootings’13 NY train crash’13 Russia meteor’13 Colorado wildfires’12 Guatemala earthquake’12 Glasgow helicopter crash’13 West Texas explosion’13 Lac Megantic train crash’13 Venezuela refinery’12 Bohol earthquake’13 Boston bombings’13 Brazil nightclub fire’13 Spain train crash’13 Manila floods’13 Alberta floods’13 Philipinnes floods’12 Typhoon Yolanda’13 Costa Rica earthquake’12 Singapore haze’13 Italy earthquakes’12 Australia bushfire’13 Queensland floods’13 Colorado floods’13 Sardinia floods’13 Typhoon Pablo’12 lower similarity
  37. 37. Events Similarity: Information Types Savar building collapse’13 LA airport shootings’13 NY train crash’13 Russia meteor’13 Colorado wildfires’12 Guatemala earthquake’12 Glasgow helicopter crash’13 West Texas explosion’13 Lac Megantic train crash’13 Venezuela refinery’12 Bohol earthquake’13 Boston bombings’13 Brazil nightclub fire’13 Spain train crash’13 Manila floods’13 Alberta floods’13 Philipinnes floods’12 Typhoon Yolanda’13 Costa Rica earthquake’12 Singapore haze’13 Italy earthquakes’12 Australia bushfire’13 Queensland floods’13 Colorado floods’13 Sardinia floods’13 Typhoon Pablo’12 lower similarity
  38. 38. Events Similarity: Information Types Savar building collapse’13 LA airport shootings’13 NY train crash’13 Russia meteor’13 Colorado wildfires’12 Guatemala earthquake’12 Glasgow helicopter crash’13 West Texas explosion’13 Lac Megantic train crash’13 Venezuela refinery’12 Bohol earthquake’13 Boston bombings’13 Brazil nightclub fire’13 Spain train crash’13 Manila floods’13 Alberta floods’13 Philipinnes floods’12 Typhoon Yolanda’13 Costa Rica earthquake’12 Singapore haze’13 Italy earthquakes’12 Australia bushfire’13 Queensland floods’13 Colorado floods’13 Sardinia floods’13 Typhoon Pablo’12 Dominated by natural, diffused and progressive lower similarity
  39. 39. Events Similarity: Information Types Savar building collapse’13 LA airport shootings’13 NY train crash’13 Russia meteor’13 Colorado wildfires’12 Guatemala earthquake’12 Glasgow helicopter crash’13 West Texas explosion’13 Lac Megantic train crash’13 Venezuela refinery’12 Bohol earthquake’13 Boston bombings’13 Brazil nightclub fire’13 Spain train crash’13 Manila floods’13 Alberta floods’13 Philipinnes floods’12 Typhoon Yolanda’13 Costa Rica earthquake’12 Singapore haze’13 Italy earthquakes’12 Australia bushfire’13 Queensland floods’13 Colorado floods’13 Sardinia floods’13 Typhoon Pablo’12 Dominated by natural, diffused and progressive Dominated by human-induced, focalized and instantaneous lower similarity
  40. 40. Singapore haze’13 Philipinnes floods’12 Alberta floods’13 Manila floods’13 Italy earthquakes’12 Australia bushfire’13 Colorado wildfires’12 Colorado floods’13 Queensland floods’13 Typhoon Pablo’12 Typhoon Yolanda’13 Brazil nightclub fire’13 Russia meteor’13 Boston bombings’13 Glasgow helicopter crash’13 Bohol earthquake’13 Venezuela refinery’12 Sardinia floods’13 West Texas explosion’13 NY train crash’13 Costa Rica earthquake’12 LA airport shootings’13 Savar building collapse’13 Lac Megantic train crash’13 Guatemala earthquake’12 Spain train crash’13 lower similarity Events Similarity: Information Sources
  41. 41. Singapore haze’13 Philipinnes floods’12 Alberta floods’13 Manila floods’13 Italy earthquakes’12 Australia bushfire’13 Colorado wildfires’12 Colorado floods’13 Queensland floods’13 Typhoon Pablo’12 Typhoon Yolanda’13 Brazil nightclub fire’13 Russia meteor’13 Boston bombings’13 Glasgow helicopter crash’13 Bohol earthquake’13 Venezuela refinery’12 Sardinia floods’13 West Texas explosion’13 NY train crash’13 Costa Rica earthquake’12 LA airport shootings’13 Savar building collapse’13 Lac Megantic train crash’13 Guatemala earthquake’12 Spain train crash’13 lower similarity Events Similarity: Information Sources
  42. 42. Singapore haze’13 Philipinnes floods’12 Alberta floods’13 Manila floods’13 Italy earthquakes’12 Australia bushfire’13 Colorado wildfires’12 Colorado floods’13 Queensland floods’13 Typhoon Pablo’12 Typhoon Yolanda’13 Brazil nightclub fire’13 Russia meteor’13 Boston bombings’13 Glasgow helicopter crash’13 Bohol earthquake’13 Venezuela refinery’12 Sardinia floods’13 West Texas explosion’13 NY train crash’13 Costa Rica earthquake’12 LA airport shootings’13 Savar building collapse’13 Lac Megantic train crash’13 Guatemala earthquake’12 Spain train crash’13 lower similarity Dominated by instantaneous, focalized and human-induced Events Similarity: Information Sources
  43. 43. Singapore haze’13 Philipinnes floods’12 Alberta floods’13 Manila floods’13 Italy earthquakes’12 Australia bushfire’13 Colorado wildfires’12 Colorado floods’13 Queensland floods’13 Typhoon Pablo’12 Typhoon Yolanda’13 Brazil nightclub fire’13 Russia meteor’13 Boston bombings’13 Glasgow helicopter crash’13 Bohol earthquake’13 Venezuela refinery’12 Sardinia floods’13 West Texas explosion’13 NY train crash’13 Costa Rica earthquake’12 LA airport shootings’13 Savar building collapse’13 Lac Megantic train crash’13 Guatemala earthquake’12 Spain train crash’13 lower similarity Dominated by instantaneous, focalized and human-induced Events Similarity: Information Sources
  44. 44. Singapore haze’13 Philipinnes floods’12 Alberta floods’13 Manila floods’13 Italy earthquakes’12 Australia bushfire’13 Colorado wildfires’12 Colorado floods’13 Queensland floods’13 Typhoon Pablo’12 Typhoon Yolanda’13 Brazil nightclub fire’13 Russia meteor’13 Boston bombings’13 Glasgow helicopter crash’13 Bohol earthquake’13 Venezuela refinery’12 Sardinia floods’13 West Texas explosion’13 NY train crash’13 Costa Rica earthquake’12 LA airport shootings’13 Savar building collapse’13 Lac Megantic train crash’13 Guatemala earthquake’12 Spain train crash’13 lower similarity Dominated by instantaneous, focalized and human-induced Events Similarity: Information Sources
  45. 45. Singapore haze’13 Philipinnes floods’12 Alberta floods’13 Manila floods’13 Italy earthquakes’12 Australia bushfire’13 Colorado wildfires’12 Colorado floods’13 Queensland floods’13 Typhoon Pablo’12 Typhoon Yolanda’13 Brazil nightclub fire’13 Russia meteor’13 Boston bombings’13 Glasgow helicopter crash’13 Bohol earthquake’13 Venezuela refinery’12 Sardinia floods’13 West Texas explosion’13 NY train crash’13 Costa Rica earthquake’12 LA airport shootings’13 Savar building collapse’13 Lac Megantic train crash’13 Guatemala earthquake’12 Spain train crash’13 lower similarity Dominated by instantaneous, focalized and human-induced Dominated by natural, diffused and progressive Events Similarity: Information Sources
  46. 46. Who Says What? Media Outsiders Eyewitness Government NGOs Business Other Useful Info. Sympathy Affected Ind. Donat. & Volun. Caution & Advice Infrast. & Utilities 0 5% 10% >15%
  47. 47. Who Says What? Media Outsiders Eyewitness Government NGOs Business Other Useful Info. Sympathy Affected Ind. Donat. & Volun. Caution & Advice Infrast. & Utilities 0 5% 10% >15%
  48. 48. Who Says What? Media Outsiders Eyewitness Government NGOs Business Other Useful Info. Sympathy Affected Ind. Donat. & Volun. Caution & Advice Infrast. & Utilities 0 5% 10% >15%
  49. 49. Who Says What? Media Outsiders Eyewitness Government NGOs Business Other Useful Info. Sympathy Affected Ind. Donat. & Volun. Caution & Advice Infrast. & Utilities 0 5% 10% >15%
  50. 50. Who Says What? Media Outsiders Eyewitness Government NGOs Business Other Useful Info. Sympathy Affected Ind. Donat. & Volun. Caution & Advice Infrast. & Utilities 0 5% 10% >15%
  51. 51. Temporal Distribution: Types 12h 24h 36h 48h … several days peak
  52. 52. Temporal Distribution: Types 12h 24h 36h 48h … several days peak Caution & Advice
  53. 53. Temporal Distribution: Types 12h 24h 36h 48h … several days peak Caution & Advice Sympathy & Support
  54. 54. Temporal Distribution: Types 12h 24h 36h 48h … several days peak Caution & Advice Sympathy & Support Infrastructure & Utilities
  55. 55. Temporal Distribution: Types 12h 24h 36h 48h … several days peak Caution & Advice Sympathy & Support Affected Individuals Infrastructure & Utilities
  56. 56. Temporal Distribution: Types 12h 24h 36h 48h … several days peak Caution & Advice Sympathy & Support Affected Individuals Infrastructure & Utilities Other Specific Info.
  57. 57. Temporal Distribution: Types 12h 24h 36h 48h … several days peak Caution & Advice Sympathy & Support Affected Individuals Infrastructure & Utilities Other Specific Info. Donations & Volunteering
  58. 58. Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Progressive
  59. 59. Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Eyewitness Progressive
  60. 60. Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Eyewitness Government Progressive
  61. 61. Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Eyewitness Government Media Progressive
  62. 62. Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Eyewitness Government Outsiders Media Progressive
  63. 63. Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Eyewitness Government NGOs Outsiders Media Progressive
  64. 64. Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Eyewitness Government Business NGOs Outsiders Media Progressive
  65. 65. 12h 24h 36h 48h … several days peak Instantaneous Temporal Distribution: Sources
  66. 66. 12h 24h 36h 48h … several days peak Eyewitness Instantaneous Temporal Distribution: Sources
  67. 67. 12h 24h 36h 48h … several days peak Eyewitness Outsiders Instantaneous Temporal Distribution: Sources
  68. 68. 12h 24h 36h 48h … several days peak Eyewitness Outsiders Media Instantaneous Temporal Distribution: Sources
  69. 69. 12h 24h 36h 48h … several days peak Eyewitness Government Outsiders Media Instantaneous Temporal Distribution: Sources
  70. 70. 12h 24h 36h 48h … several days peak Eyewitness Government NGOs Outsiders Media Instantaneous Temporal Distribution: Sources
  71. 71. 12h 24h 36h 48h … several days peak Eyewitness Government Business NGOs Outsiders Media Instantaneous Temporal Distribution: Sources
  72. 72. Take-Away Twitter is a medium through which the nuance of events is amplified; yet, when looking at the same data at a higher- level we see commonalities and patterns. ! ! ☞ Download all our collections from crisislex.org Thanks! Karl Aberer @ChatoX @velofemme Patrick Meier Questions? @o_saja
  73. 73. Back-up Slides
  74. 74. Temporal Distribution ProgressiveInstantaneous • Start: when the event occurs • High volumes of tweets right after onset • Start: when the hazard is detected • High volumes around the peak of the event (e.g., affects a densely populated area, high economic damage)
  75. 75. Goal: Retrieve comprehensive collections of event- related messages • Keyword-based sampling: 
 #sandy, #bostonbombings, #qldflood • Location-based sampling: 
 tweets geo-tagged in disaster area Problem: Create event collections without too many off- topic tweets Recall(% of on-crisis tweets retrieved) Precision (on-crisis%ofretrievedtweets) DesiredKW-based Geo-based Lexicon-based ICWSM 2014, Olteanu et al. Efficient Data Collection
  76. 76. CrisisLex API limits • rigid query language • limited volumes Laconic language Challenges damage affected people people displaced donate blood text redcross stay safe crisis deepens evacuated toll raises …… ICWSM 2014, Olteanu et al.
  77. 77. Annotations: Informativeness
  78. 78. Event List
  79. 79. Association-Rules ☞ Diffused events have more than average caution & advice messages ☞ valid for 24/26 events ! ☞ Human-induced & accidental events have less than average eyewitness accounts ☞ valid for 21/26 events
  80. 80. Informativeness & Content Redundancy ☞ Informativeness ☞ Crisis-related: 89% on average (min. 64%, max. 100%) ☞ Informative (from crisis-related): 69% on average (min. 44%, max. 92%) ! ☞ Redundancy ☞ NGOs & Government ☞ top 3 messages account for 20%-22% of messages ! ☞ Caution & Advice and Infrastructure & Utilities ☞ top 3 messages account for 12%-14% of messages

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