The document, presented by Carlos Castillo, discusses the utilization of big crisis data during disasters and time-critical situations, emphasizing the role of social media in crisis communication. It explores various aspects of humanitarian computing, including data extraction and classification methods, and the importance of timely information dissemination in emergencies. The presentation highlights ongoing research collaborations and the challenges and ethical considerations involving digital volunteering and crisis response.

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

2. BigCrisis 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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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.

78. BigCrisis Data — Carlos Castillo 78
Thank you!
Follow @BigCrisisData