keynote talk: big crisis data, an open invitation
TRANSCRIPT
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BIG CRISIS DATAAn Open Invitation
CARLOS CASTILLO@BigCrisisData
Manaus, Brasil, Outubro 2015
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Big Crisis Data — Carlos Castillo 2
This talk is about ...● Disasters and time-critical situations
– Natural, social, or technological hazards
– Mass convergence events● Social media
– Particularly microtext● Computing
– Applications of many fields including NLP, ML, IR
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Big Crisis Data — Carlos Castillo 3
http://www.youtube.com/watch?v=0UFsJhYBxzY
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Big Crisis Data — Carlos Castillo 4
An earthquake hits a Twitter user
http://xkcd.com/723/
● When an earthquake strikes, the first tweets are posted 20-30 seconds later
● Damaging seismic waves travel at 3-5 km/s, while network communications are light speed on fiber/copper + latency
● After ~100km seismic waves may be overtaken by tweets about them
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Big Crisis Data — Carlos Castillo 5
January 2010
How/when did it start for me?
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Big Crisis Data — Carlos Castillo 6
Humanitarian Computing
At least 775 publications:
● Crisis Analysis (55)
● Crisis Management (309)
● Situational Awareness (67)
● Social Media (231)
● Mobile Phones (74)
● Crowdsourcing (116)
● Software and Tools (97)
● Human-Computer Interaction (28)
● Natural Language Processing (33)
● Trust and Security (33)
● Geographical Analysis (53)
Source: http://humanitariancomp.referata.com/
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Big Crisis Data — Carlos Castillo 7
Humanitarian Computing Topics
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Fertile grounds for applied research✔ Problems of global significance
✔ Solved with labor-intensive methods
✔ Better solution provides a public good
✔ Large and noisy data sets available
✔ Engage volunteer communities
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Big Crisis Data — Carlos Castillo 11
Fertile grounds for applied research✔ Problems of global significance
✔ Solved with labor-intensive methods
✔ Better solution provides a public good
✔ Large and noisy data sets available
✔ Engage volunteer communities
Relevance to practitioners?
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Big Crisis Data — Carlos Castillo 12
Recent collaboratorsPatrick Meier
Sarah Vieweg– QCRI
Muhammad Imran– QCRI
Irina Temnikova– QCRI
Alexandra Olteanu– EPFL
Aditi Gupta– IIIT Delhi
“P.K.” Kumaraguru– IIIT Delhi
Fernando Diaz– Microsoft
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Big Crisis Data — Carlos Castillo 13
Outline
Volume
Vagueness
Visualization
Volunteering
Values
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Big Crisis Data — Carlos Castillo 14
Disaster Communications
and Scale
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Crises and disasters● Crises are unstable situations
– May or may not lead to a disaster● Disasters are social phenomena
– Disruptions of routines
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Big Crisis Data — Carlos Castillo 16
Temporal and Spatial Dimensions
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Big Crisis Data — Carlos Castillo 17
Examples
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REEL LIFE OR REAL LIFE?
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REEL LIFE OR REAL LIFE?
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https://www.youtube.com/watch?v=MylI8HmgMBk
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Big Crisis Data — Carlos Castillo 21
In Real Life ...● Some people panic, most people don't
● People gather information from familiar sources
● People quickly decide whether to flee, take cover, or take action
● People improvise complex rescue operations on the spot
Devon, UK, June 2014 London, UK, May 2015 San José Boquerón, Paraguay, Oct 2013
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Example Disaster-Related Messages“OMG! The fire seems out of control: It’s running down the hills!”
Bush fire near Marseilles, France, in 2009 [Longueville et al. 2009]
“Red River at East Grand Forks is 48.70 feet, +20.7 feet of flood stage, -5.65 feet of 1997 crest. #flood09”
Red River Valley floods in 2009 [Starbird et al. 2010]
“My moms backyard in Hatteras. That dock is usually about 3 feet above water [photo]”
Hurricane Sandy 2013 [Leavitt and Clark 2014]
“Sirens going off now!! Take cover...be safe!”Moore Tornado 2013 [Blanford et al. 2014].
“There is shooting at Utøya, my little sister is there and just called home!”
2011 attacks in Norway [Perng et al. 2013]
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Social media usage during disasters● Interpersonal (horizontal)
– Stay in touch with family and friends● Citizen sensing (bottom-up)
– Read/Write reports on ground situation● Official communications (top-down)
– E.g. advice, warnings, or evacuation orders
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Big Crisis Data — Carlos Castillo 24
Scale: Tweets per Second
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Requirements● Typical users
– Emergency response services
– Humanitarian relief agencies
– Journalists and the Public● Underspecified requirements that vary over time
● Usually a combination of:
1) Capture the “Big Picture”
2) Obtain “Actionable Insights”
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Big Crisis Data — Carlos Castillo 26
Understanding, Classifying and
Extracting
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Example
“Media must report about d alleged 20k RSS chaps off 2 #Nepal.here’s a pic coz d 1 @ShainaNC shared isn’t true.. ;)”
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Social media messages● Social media is more like a transcript of a conversation than like
text meant to stand on its own
– Awkward entry methods:● Fragmented language and incomplete sentences● Many typographic and grammatical errors
– Conversational:● Little or no context (hard to comprehend in isolation)● Code switching and borrowing● Internet slang
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Big Crisis Data — Carlos Castillo 29
Slang
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Big Crisis Data — Carlos Castillo 30
ClassificationCaution &
AdviceInformation
SourcesDamage &Casualties Donations
Gov
Eyewitness
Media
NGO
Outsider
...
...
Filteredtweets
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Classification Axes● By usefulness (application-dependent!)
– Not related, Related but useless, Useful● By factual, subjective, or emotional content
● By information provided
● By information source
– Government, NGOs, media, eyewitnesses, etc.● By humanitarian clusters
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Humanitarian Clusters
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Alexandra Olteanu, Sarah Vieweg and Carlos Castillo: What to Expect When the Unexpected Happens: Social Media Communications Across Crises.To appear in CSCW 2015.
Humanitarian Clusters (cont.)
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A large-scale study of crisis tweets● Collect tweets from 26 disasters
● Classify according to:
● Informative / Not informative● Information provided● Information source
● Several iterations required to write the “right” instructions
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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Information Provided in Crisis Tweets
N=26; Data available at http://crisislex.org/
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What do people tweet about?● Affected individuals
– 20% on average (min. 5%, max. 57%)
– most prevalent in human-induced, focalized & instantaneous events
● Sympathy and emotional support
– 20% on average (min. 3%, max. 52%)
– most prevalent in instantaneous events● Other useful information
– 32% on average (min. 7%, max. 59%)
– least prevalent in diffused events
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What do people tweet about? (cont.)● Infrastructure and utilities
– 7% on average (min. 0%, max. 22%)
– most prevalent in diffused events, in particular floods● Caution and advice
– 10% on average (min. 0%, max. 34%)
– least prevalent in instantaneous & human-induced events● Donations and volunteering
– 10% on average (min. 0%, max. 44%)
– most prevalent in natural hazards
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Distribution over information sources
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Distribution over time
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Big Crisis Data — Carlos Castillo 40
Dataset
CrisisLexT26
www.crisislex.org
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Big Crisis Data — Carlos Castillo 41
Information Extraction
...
Classifiedtweets @JimFreund: Apparently we have no choice.
There is a tornado watch in effect
tonight.
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Extraction● #hashtags, @user mentions, URLs, etc.
– Regular expressions
– Text library from Twitter● Temporal expressions
– Part-of-speech tagger + heuristics
– Natty library● Supervised learning
Muhammad Imran, Shady Elbassuoni, Carlos Castillo, Fernando Diaz and Patrick Meier: Practical Extraction of Disaster-Relevant Information from Social Media. Social Web and Disaster Management (SWDM) workshop. Rio de Janeiro, Brazil, 2013.
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Labels for extraction● Type-dependent instruction
● Ask evaluators to copy-paste a word/phrase from each tweet
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Learning: Conditional Random Fields
● Extends HMM to incorporate more possible dependencies
● Used extensively in NLP for part-of-speech tagging and information extraction
HMM Linear-chain CRF
hidden
observed
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Tool● CMU ARK Twitter NLP
– Tokenization
– Feature extraction
– CRF learning● Very easy to use
– simply change the training set (part-of-speech tags),
– then re-train
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Output examplesRT @weatherchannel: .@NYGovCuomo orders closing of NYC bridges. Only Staten Island bridges unaffected at this time. Bridges must close by 7pm. #Sandy #NYC
Wow what a mess #Sandy has made. Be sure to check on the elderly and homeless please! Thoughts and prayers to all affected
RT @twc_hurricane: Wind gusts over 60 mph are being reported at Central Park and JFK airport in #NYC this hour. #Sandy
RT @mitchellreports: Red Cross tells us grateful for Romney donation but prefer people send money or donate blood dont collect goods NOT best way to help #Sandy
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Extractor evaluation
Setting Rec Prec
Train 2/3 Joplin, Test 1/3 Joplin 78% 90%
Train 2/3 Sandy, Test 1/3 Sandy 41% 79%
Train Joplin, Test Sandy 11% 78%
Train Joplin + 10% Sandy, Test 90% Sandy
21% 81%
● Precision is: one word or more in common with what humans extracted
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Donations matching● Identify and match requests/offers for donations
– Money, clothing, food, shelter, volunteers, blood● Method
– Classify
– Determine key aspects
– Extract key aspects
– Per-aspect matching
Hemant Purohit, Amit Sheth, Carlos Castillo, Patrick Meier, Fernando Diaz: Emergency-Relief Coordination on Social Media: Automatically Matching Resource Requests and Offers. First Monday 19 (1), January 2014.
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Donations matching
Average precision = 0.21 (0.16 if only text similarity is used)
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Crisis maps from social
media
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Patrick Meier, Social Innovation Director @ QCRI – http://irevolution.net/
“What can speed humanitarian
response to tsunami-ravaged coasts?
Expose human rights atrocities?
Launch helicopters to rescue
earthquake victims? Outwit corrupt
regimes?
A map.”
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Crisis mapping goes mainstream (2011)
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Automatic Mapping (floods)● Top: hydrological data
● Bottom: tweet density
● Broad match with affected areas
● Many biases towards places with higher density of smartphones
De Albuquerque, João Porto, Herfort, Benjamin, Brenning, Alexander, and Zipf, Alexander. 2015. A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management. International Journal of Geographical Information Science, 29(4), 667–689.
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Automatic Mapping (Dengue)
Gomide, Janaina and Veloso, Adriano and Meira, Wagner and Almeida, Virgilio and Benevenuto, Fabricio and Ferraz, Fernanda and Teixeira, Mauro (2011) Dengue surveillance based on a computational model of spatio-temporal locality of Twitter. pp. 1-8. In: Proceedings of the ACM WebSci'11, June 14-17 2011, Koblenz, Germany.
● Top: official reports
● Bottom: tweets
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Current Approach
Hybrid real-time systems
MicroMappers
Manual processing: crowdsourcing
Automatic processing: machine learning
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http://newsbeatsocial.com/watch/0_s6xxcr3p
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https://www.youtube.com/watch?v=uKgE3yWJ0_I
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Volunteering and Values
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Volunteering is a constant● Integral part of how communities react to disasters
● Organizational types:
– Existing – Extending – Expanding – Emerging● Emergent organizations a mixed blessing for existing ones
● New scenario: digital volunteering
– E.g. volunteer annotations, including crisis mapping
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Why do people volunteer?
Altruism is key, but it's
one of many reasons
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Privacy and Ethics● Protect the privacy of individuals
– ICRC Data Protection Guidelines
– UN Guidelines on Cyber Security● Protect victims and responders during armed attacks
● Protect volunteers from distal exposure
● Protect citizen reporters from danger and retaliation
● Give back and share results and data
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“I'm dying, they are tweeting”
Digital Voyeurism
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CONCLUSIONS
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Computationally feasible
Supported by
data
Useful
Good projects in this space
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Computationally feasible
Supported by
data
Useful
Good projects in this space
Temptation! Danger!
Poorly planned projects :-(
AI-complete problems
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Interdisciplinary Research● As many things, it has Good, Bad, and Ugly aspects● Good
– You learn a lot, and it's the only way of supporting claims of practical utility in applied research
● Bad– Formal response organizations can be very difficult to engage with;
relationships should be established between operations● Ugly
– Working software and 24/7 support for a critical need now vs advanced proof-of-concept later
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Possibility of large impact by using computer science to support
humanitarian work
=Applied computing at its best
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References● Carlos Castillo: “Big Crisis Data.” Cambridge University Press, 2016 (forthcoming).● Muhammad Imran, Carlos Castillo, Fernando Diaz, Sarah Vieweg: "Processing Social Media Messages in Mass
Emergency: A Survey" In ACM Computing Surveys, Volume 47, Issue 4, June 2015.● 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. ● Muhammad Imran, Ioanna Lykourentzou, Yannick Naudet and Carlos Castillo: Engineering Crowdsourced Stream
Processing Systems. Technical report, 2015.● Hemant Purohit, Amit Sheth, Carlos Castillo, Patrick Meier, Fernando Diaz: Emergency-Relief Coordination on
Social Media: Automatically Matching Resource Requests and Offers. First Monday 19 (1), January 2014. ● Sarah Vieweg, Carlos Castillo and Muhammad Imran: "Integrating Social Media Communications into the Rapid
Assessment of Sudden Onset Disasters." SocInfo 2014.● Alexandra Olteanu, Carlos Castillo, Fernando Diaz and Sarah Vieweg: CrisisLex: A Lexicon for Collecting and
Filtering Microblogged Communications in Crises. In ICWSM. Ann Arbor, MI, USA. June 2014. ● Carlos Castillo, Marcelo Mendoza, Barbara Poblete: Predicting Information Credibility in Time-Sensitive Social
Media (+Supplementary Material). In Internet Research, Vol. 23, Issue 5, Special issue on The Predictive Power of Social Media, pp. 560-588. October 2013.
● Muhammad Imran, Shady Elbassuoni, Carlos Castillo, Fernando Diaz and Patrick Meier: Practical Extraction of Disaster-Relevant Information from Social Media. Social Web and Disaster Management (SWDM) workshop. Rio de Janeiro, Brazil, 2013.
● Muhammad Imran, Shady Elbassuoni, Carlos Castillo, Fernando Diaz and Patrick Meier: Extracting Information Nuggets from Disaster-Related Messages in Social Media. In ISCRAM. Baden-Baden, Germany, 2013. Best paper award.
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Thank you!Follow @BigCrisisData