Talk in Manaus, Brazil. Higher-resolution and source available upon request. Reference: http://dx.doi.org/10.1145/2820426.2822359

1. BIG CRISIS DATA
An Open Invitation
CARLOS CASTILLO
@BigCrisisData
Manaus, Brasil, Outubro 2015

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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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http://www.youtube.com/watch?v=0UFsJhYBxzY

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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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January 2010
How/when did it start for me?

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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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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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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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Recent collaborators
Patrick 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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Outline
Volume
Vagueness
Visualization
Volunteering
Values

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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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Temporal and Spatial Dimensions

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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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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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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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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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Slang

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Classification
Caution &
Advice
Information
Sources
Damage &
Casualties Donations
Gov
Eyewitness
Media
NGO
Outsider
...
...
Filtered
tweets

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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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Dataset
CrisisLexT26
www.crisislex.org

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Information Extraction
...
Classified
tweets @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 examples
RT @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

73. Computationally
feasible
Supported by
data
Useful
Good projects in this space

74. 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

76. 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