Assisting Coordination during Crisis: A
Domain Ontology based Approach to Infer
Resource Needs from Tweets
Shreyansh Bhatt...
2
• Community responds to the scale of disaster
• (Informal) Social media can be leveraged to help coordination
Variety of...
3
Challenges
Filter
• High Volume and
Velocity:
• 20M+ Tweets during
1st week of #Sandy
• Identify resources
Semantics
• C...
4
Approach
5
Location Detection
• Two fold filtering to increase precision:
• 1.) Named Entity based, and 2.) DBpedia ontology based
...
6
Medical/power related tweets
• Medical emergency due to power cut at hospitals in Brooklyn was
identified by power cut i...
7
Medical/power related tweets (cont.)
• Medical emergency due to power cut at hospitals in Brooklyn was
identified by pow...
8
Tweets about resources with location types
• Ability to infer location from text increases location information over
twe...
9
Text location vs Metadata location
• Text-location detection precision : 88%
• 66% text-locations within affected region...
10
Summary
Visit our poster for More insights!
• Domain knowledge based approach to identify contextually interdependent
r...
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ACM Web-Science 2014: Assisting Crisis Coordination Using Social Media

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Domain knowledge-based approach to predict potential resource needs, such as Medical, based on relationships with other resource needs observed earlier, such as the Power outages.
Read further about details of all related work under the NSF SoCS Project: http://www.knoesis.org/research/semsoc/projects/socs

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ACM Web-Science 2014: Assisting Crisis Coordination Using Social Media

  1. 1. Assisting Coordination during Crisis: A Domain Ontology based Approach to Infer Resource Needs from Tweets Shreyansh Bhatt, Hemant Purohit, Andrew Hampton, Valerie Shalin, Amit Sheth, John Flach
  2. 2. 2 • Community responds to the scale of disaster • (Informal) Social media can be leveraged to help coordination Variety of responses on social media!
  3. 3. 3 Challenges Filter • High Volume and Velocity: • 20M+ Tweets during 1st week of #Sandy • Identify resources Semantics • Citizen do not always write about specific needs Locate Resources • Metadata location (device sensor & user profile) • ~ 21% tweets with metadata location Domain model : Crisis Ontology Technique to precisely identify text location Potential Solutions
  4. 4. 4 Approach
  5. 5. 5 Location Detection • Two fold filtering to increase precision: • 1.) Named Entity based, and 2.) DBpedia ontology based • Use of DBpedia allows annotation of city, state and famous places
  6. 6. 6 Medical/power related tweets • Medical emergency due to power cut at hospitals in Brooklyn was identified by power cut information (News: http://j.mp/2Hospitals) Time (2012) Message text (Power related) Text Location Identified Oct. 29, 21:30:02 Lots of wind and some rain but still running and no power outage in Clinton Hill, Brooklyn. #Sandy #Hurricane http://dbpedia.org/reso urce/Brooklyn Oct. 29, 23:56:57 Power cut to coney island and Brighton beach #HurricaneSandy #NYC http://dbpedia.org/reso urce/Coney_Island Oct. 30, 01:30:36 Power may be cut off soon in south bklyn. Coney, Gravesend Sheedshed Bay etc #Sandy #Frankenstorm http://dbpedia.org/reso urce/Coney_Island
  7. 7. 7 Medical/power related tweets (cont.) • Medical emergency due to power cut at hospitals in Brooklyn was identified by power cut information (News: http://j.mp/2Hospitals) Time (2012) Message text (Medical related) Text Location Identified Oct. 30, 03:15:08 @911BUFF: BREAKING CONEY ISLAND HOSPITAL ON FIRE. NYU HOSP. EVACUATED, BELLEVUE HOSPITAL ALSO LOSING BACKUP POWER #SANDY #NYC #frankenstorm http://dbpedia.org/resourc e/Bellevue_Hospital_Cent er Oct. 30, 20:20:42 SANDY: Bellevue Hospital is on backup power, trying to evacuate as much as possible, 2 young boys missing from SI since beginning of Hurricane http://dbpedia.org/resourc e/Bellevue_Hospital_Cent er
  8. 8. 8 Tweets about resources with location types • Ability to infer location from text increases location information over tweet metadata information by approximately 50% Total Text location Metadata location Text w/o Metadata Text and Metadata Metadata w/o text Power 103102 14969 24361 10974 3995 20366 Medical 16002 3243 6015 2057 1186 4829 Food/water 38952 5046 9152 3574 1472 7680 Power & Medical 948 231 377 134 97 280 Power & Food 2908 382 839 260 122 717 Power & food & medical 44 39 30 13 26 17
  9. 9. 9 Text location vs Metadata location • Text-location detection precision : 88% • 66% text-locations within affected region of disaster Location source Total tweets Total tweets with location in affected region Total tweets with location not in affected region Text 517 340 (66%) 177 (34%) Meta-data 313 238 (43%) 323 (57%)
  10. 10. 10 Summary Visit our poster for More insights! • Domain knowledge based approach to identify contextually interdependent resource needs reported via social media • DBpedia ontology driven approach for precise text-location detection • Next Steps • Exploit behaviors of seeking-supplying of resources in messages • Improve location information verification – Ack: NSF SoCS project, grant IIS-1111182, ‘Social Media Enhanced Organizational Sensemaking in Emergency Response’ • http://www.knoesis.org/research/semsoc/projects/socs • Questions? • Mail: {shreyansh,hemant}@knoesis.org, Tweet: @hemant_pt

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