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Social Media Communications Across Crises

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Carlos Castillo (ChaTo)
Carlos Castillo (ChaTo)

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 ! Alexandra Olteanu Sarah Vieweg, Carlos Castillo
Leetaru et al. "Mapping the global Twitter heartbeat: The geography of Twitter." First Monday (2013)
from ReliefWeb.int—a digital service by UN OCHA, 25.02.2015
What are the similarities and differences in Twitter communications that take place during different crisis events, according to specific characteristics of such events?
What are the similarities and differences in Twitter communications that take place during different crisis events, according to specific characteristics of such events?
What are the similarities and differences in Twitter communications that take place during different crisis events, according to specific characteristics of such events?
Analysis Framework Crisis Dimensions Temporal Development G eographic Spread CrisisType natural human-induced
Analysis Framework Crisis Dimensions Temporal Development G eographic Spread CrisisType natural human-induced
Analysis Framework Crisis Dimensions Content Dimensions Eyewitness Government NGOs Media Business Outsiders Affected individuals Infrastructure Damage Donations Caution & Advice Sympathy Other Source Type
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
Analysis Framework Crisis Dimensions Content Dimensions Data Collection
Analysis Framework Crisis Dimensions Content Dimensions Data Collection Data Annotation
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
Analysis Framework Crisis Dimensions Content Dimensions Data Collection Data Annotation Data Analysis Step 1 Step 2 Step 3 Step 4 Step 5
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
Step 2: Content Dimensions
Step 2: Content Dimensions
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 ! ! ! ! ! ! ! ! !
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 ! ! ! ! ! !
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 ! ! ! ! ! ! ! ! !
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
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 ! ! ! ! ! ! ! ! !
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
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
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
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
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
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
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
Step 4: Data Annotation ☞ ~1000 tweets per crisis ☞ Informativeness ☞ Source of information ☞ Type of information ! ☞ Crowdsource workers from the affected countries
Step 5: Data Analysis ☞ Content types/sources vs. crisis dimensions ☞ Interplay between types and sources ☞ Crisis similarity ☞ Temporal aspects
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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%
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%
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%
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%
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%
Temporal Distribution: Types 12h 24h 36h 48h … several days peak
Temporal Distribution: Types 12h 24h 36h 48h … several days peak Caution & Advice
Temporal Distribution: Types 12h 24h 36h 48h … several days peak Caution & Advice Sympathy & Support
Temporal Distribution: Types 12h 24h 36h 48h … several days peak Caution & Advice Sympathy & Support Infrastructure & Utilities
Temporal Distribution: Types 12h 24h 36h 48h … several days peak Caution & Advice Sympathy & Support Affected Individuals Infrastructure & Utilities
Temporal Distribution: Types 12h 24h 36h 48h … several days peak Caution & Advice Sympathy & Support Affected Individuals Infrastructure & Utilities Other Specific Info.
Temporal Distribution: Types 12h 24h 36h 48h … several days peak Caution & Advice Sympathy & Support Affected Individuals Infrastructure & Utilities Other Specific Info. Donations & Volunteering
Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Progressive
Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Eyewitness Progressive
Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Eyewitness Government Progressive
Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Eyewitness Government Media Progressive
Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Eyewitness Government Outsiders Media Progressive
Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Eyewitness Government NGOs Outsiders Media Progressive
Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Eyewitness Government Business NGOs Outsiders Media Progressive
12h 24h 36h 48h … several days peak Instantaneous Temporal Distribution: Sources
12h 24h 36h 48h … several days peak Eyewitness Instantaneous Temporal Distribution: Sources
12h 24h 36h 48h … several days peak Eyewitness Outsiders Instantaneous Temporal Distribution: Sources
12h 24h 36h 48h … several days peak Eyewitness Outsiders Media Instantaneous Temporal Distribution: Sources
12h 24h 36h 48h … several days peak Eyewitness Government Outsiders Media Instantaneous Temporal Distribution: Sources
12h 24h 36h 48h … several days peak Eyewitness Government NGOs Outsiders Media Instantaneous Temporal Distribution: Sources
12h 24h 36h 48h … several days peak Eyewitness Government Business NGOs Outsiders Media Instantaneous Temporal Distribution: Sources
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
Back-up Slides
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)
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
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.
Annotations: Informativeness
Event List
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
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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Social Media Communications Across Crises

  • 1. What to Expect When The Unexpected Happens: Social Media Communications Across Crises ! Alexandra Olteanu Sarah Vieweg, Carlos Castillo
  • 2. Leetaru et al. "Mapping the global Twitter heartbeat: The geography of Twitter." First Monday (2013)
  • 3. from ReliefWeb.int—a digital service by UN OCHA, 25.02.2015
  • 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. What are the similarities and differences in Twitter communications that take place during different crisis events, according to specific characteristics of such events?
  • 6. What are the similarities and differences in Twitter communications that take place during different crisis events, according to specific characteristics of such events?
  • 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. 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
  • 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
  • 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. Step 2: Content Dimensions
  • 17. Step 2: Content Dimensions
  • 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Step 4: Data Annotation ☞ ~1000 tweets per crisis ☞ Informativeness ☞ Source of information ☞ Type of information ! ☞ Crowdsource workers from the affected countries
  • 31. Step 5: Data Analysis ☞ Content types/sources vs. crisis dimensions ☞ Interplay between types and sources ☞ Crisis similarity ☞ Temporal aspects
  • 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Temporal Distribution: Types 12h 24h 36h 48h … several days peak
  • 52. Temporal Distribution: Types 12h 24h 36h 48h … several days peak Caution & Advice
  • 53. Temporal Distribution: Types 12h 24h 36h 48h … several days peak Caution & Advice Sympathy & Support
  • 54. Temporal Distribution: Types 12h 24h 36h 48h … several days peak Caution & Advice Sympathy & Support Infrastructure & Utilities
  • 55. Temporal Distribution: Types 12h 24h 36h 48h … several days peak Caution & Advice Sympathy & Support Affected Individuals Infrastructure & Utilities
  • 56. Temporal Distribution: Types 12h 24h 36h 48h … several days peak Caution & Advice Sympathy & Support Affected Individuals Infrastructure & Utilities Other Specific Info.
  • 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. Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Progressive
  • 59. Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Eyewitness Progressive
  • 60. Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Eyewitness Government Progressive
  • 61. Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Eyewitness Government Media Progressive
  • 62. Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Eyewitness Government Outsiders Media Progressive
  • 63. Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Eyewitness Government NGOs Outsiders Media Progressive
  • 64. Temporal Distribution: Sources 12h 24h 36h 48h … several days peak Eyewitness Government Business NGOs Outsiders Media Progressive
  • 65. 12h 24h 36h 48h … several days peak Instantaneous Temporal Distribution: Sources
  • 66. 12h 24h 36h 48h … several days peak Eyewitness Instantaneous Temporal Distribution: Sources
  • 67. 12h 24h 36h 48h … several days peak Eyewitness Outsiders Instantaneous Temporal Distribution: Sources
  • 68. 12h 24h 36h 48h … several days peak Eyewitness Outsiders Media Instantaneous Temporal Distribution: Sources
  • 69. 12h 24h 36h 48h … several days peak Eyewitness Government Outsiders Media Instantaneous Temporal Distribution: Sources
  • 70. 12h 24h 36h 48h … several days peak Eyewitness Government NGOs Outsiders Media Instantaneous Temporal Distribution: Sources
  • 71. 12h 24h 36h 48h … several days peak Eyewitness Government Business NGOs Outsiders Media Instantaneous Temporal Distribution: Sources
  • 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
  • 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. 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. 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.
  • 78.
  • 80. 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
  • 81. 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