Keynote talk: Big Crisis Data, an Open Invitation

BIG CRISIS DATAAn Open Invitation

CARLOS CASTILLO@BigCrisisData

Manaus, Brasil, Outubro 2015

https://twitter.com/bigcrisisdata

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


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

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


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

http://xkcd.com/723/


January 2010

How/when did it start for me?


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/

http://humanitariancomp.referata.com/


Humanitarian Computing Topics


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


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?


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


Outline

Volume

Vagueness

Visualization

Volunteering

Values


Disaster Communications

and Scale


Crises and disasters● Crises are unstable situations

– May or may not lead to a disaster● Disasters are social phenomena

– Disruptions of routines


Temporal and Spatial Dimensions


Examples


REEL LIFE OR REAL LIFE?


https://www.youtube.com/watch?v=MylI8HmgMBk

https://www.youtube.com/watch?v=MylI8HmgMBk


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

http://www.dailystar.co.uk/news/latest-news/382238/Mum-jumps-river-save-baby-pram-falls-river

http://www.theguardian.com/uk-news/2015/may/29/unicyclist-in-hospital-after-collision-with-bus-in-london

http://www.abc.com.py/nacionales/nino-heroe-salva-a-su-hermano-tras-incendio-626579.html


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]


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


Scale: Tweets per Second


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”


Understanding, Classifying and

Extracting


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.. ;)”


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


Slang


ClassificationCaution &

AdviceInformation

SourcesDamage &Casualties Donations

Gov

Eyewitness

Media

NGO

Outsider

...

...

Filteredtweets


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


Humanitarian Clusters


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.)

http://chato.cl/papers/olteanu_vieweg_castillo_2015_social_media_crises_transversal_study.pdf


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.


Information Provided in Crisis Tweets

N=26; Data available at http://crisislex.org/

http://crisislex.org/


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


– most prevalent in instantaneous events● Other useful information


– least prevalent in diffused events


What do people tweet about? (cont.)● Infrastructure and utilities


– most prevalent in diffused events, in particular floods● Caution and advice


– least prevalent in instantaneous & human-induced events● Donations and volunteering


– most prevalent in natural hazards


Distribution over information sources


Distribution over time


Dataset

CrisisLexT26

www.crisislex.org

http://www.crisislex.org/


Information Extraction

...

Classifiedtweets @JimFreund: Apparently we have no choice.

There is a tornado watch in effect

tonight.


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.


Labels for extraction● Type-dependent instruction

● Ask evaluators to copy-paste a word/phrase from each tweet


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


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


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


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


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.


Donations matching

Average precision = 0.21 (0.16 if only text similarity is used)


Crisis maps from social

media


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.”

http://irevolution.net/


Crisis mapping goes mainstream (2011)


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.


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


Current Approach

Hybrid real-time systems

MicroMappers

Manual processing: crowdsourcing

Automatic processing: machine learning


http://newsbeatsocial.com/watch/0_s6xxcr3p

http://newsbeatsocial.com/watch/0_s6xxcr3p


https://www.youtube.com/watch?v=uKgE3yWJ0_I

https://www.youtube.com/watch?v=uKgE3yWJ0_I


Volunteering and Values


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


Why do people volunteer?

Altruism is key, but it's

one of many reasons


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

https://www.icrc.org/eng/assets/files/other/icrc-002-0999.pdf

https://docs.unocha.org/sites/dms/Documents/Humanitarianism%20in%20the%20Cyberwarfare%20Age%20-%20OCHA%20Policy%20Paper%2011.pdf


“I'm dying, they are tweeting”

Digital Voyeurism

http://linkis.com/sWw8m


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


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


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.

http://chato.cl/research


Thank you!Follow @BigCrisisData

https://twitter.com/bigcrisisdata

Keynote talk: Big Crisis Data, an Open Invitation

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