BIG CRISIS DATAAn 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 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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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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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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Dataset

CrisisLexT26

www.crisislex.org

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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

Computationally feasible

Supported by

data

Useful

Good projects in this space

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

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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