ACM Web-Science 2014: Assisting Crisis Coordination Using Social Media
-
Upload
knoesis-center-wright-state-university -
Category
Technology
-
view
267 -
download
1
description
Transcript of ACM Web-Science 2014: Assisting Crisis Coordination Using Social Media
![Page 1: ACM Web-Science 2014: Assisting Crisis Coordination Using Social Media](https://reader038.fdocuments.net/reader038/viewer/2022110306/554b993db4c905764c8b490a/html5/thumbnails/1.jpg)
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
![Page 2: ACM Web-Science 2014: Assisting Crisis Coordination Using Social Media](https://reader038.fdocuments.net/reader038/viewer/2022110306/554b993db4c905764c8b490a/html5/thumbnails/2.jpg)
2
• Community responds to the scale of disaster
• (Informal) Social media can be leveraged to help coordination
Variety of responses on social media!
![Page 3: ACM Web-Science 2014: Assisting Crisis Coordination Using Social Media](https://reader038.fdocuments.net/reader038/viewer/2022110306/554b993db4c905764c8b490a/html5/thumbnails/3.jpg)
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
![Page 4: ACM Web-Science 2014: Assisting Crisis Coordination Using Social Media](https://reader038.fdocuments.net/reader038/viewer/2022110306/554b993db4c905764c8b490a/html5/thumbnails/4.jpg)
4
Approach
![Page 5: ACM Web-Science 2014: Assisting Crisis Coordination Using Social Media](https://reader038.fdocuments.net/reader038/viewer/2022110306/554b993db4c905764c8b490a/html5/thumbnails/5.jpg)
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
![Page 6: ACM Web-Science 2014: Assisting Crisis Coordination Using Social Media](https://reader038.fdocuments.net/reader038/viewer/2022110306/554b993db4c905764c8b490a/html5/thumbnails/6.jpg)
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/resource/Brooklyn
Oct. 29, 23:56:57
Power cut to coney island and Brighton beach #HurricaneSandy #NYC
http://dbpedia.org/resource/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/resource/Coney_Island
![Page 7: ACM Web-Science 2014: Assisting Crisis Coordination Using Social Media](https://reader038.fdocuments.net/reader038/viewer/2022110306/554b993db4c905764c8b490a/html5/thumbnails/7.jpg)
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/resource/Bellevue_Hospital_Center
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/resource/Bellevue_Hospital_Center
![Page 8: ACM Web-Science 2014: Assisting Crisis Coordination Using Social Media](https://reader038.fdocuments.net/reader038/viewer/2022110306/554b993db4c905764c8b490a/html5/thumbnails/8.jpg)
8
Tweets about resources with location types
• Ability to infer location from text increases location information over tweet metadata information by approximately 50%
TotalText
locationMetadata location
Text w/o Metadata
Text and Metadata
Metadata w/o text
Power 103102 14969 24361 10974 3995 20366Medical 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
![Page 9: ACM Web-Science 2014: Assisting Crisis Coordination Using Social Media](https://reader038.fdocuments.net/reader038/viewer/2022110306/554b993db4c905764c8b490a/html5/thumbnails/9.jpg)
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%)
![Page 10: ACM Web-Science 2014: Assisting Crisis Coordination Using Social Media](https://reader038.fdocuments.net/reader038/viewer/2022110306/554b993db4c905764c8b490a/html5/thumbnails/10.jpg)
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