First Steps towards Implementing a Sahana Eden Social Media Dashboard

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Derrick Newman
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1 First Steps towards Implementing a Sahana Eden Social Media Dashboard Irina Temnikova¹, Dogan Biyikli², Francis Boon³ Bulgarian Academy of Sciences¹, Independent Consultant², Sahana Software Foundation³ 1

2 Structure of the Presentation 1. Work and team presentation 2. Idea 3. Motivations 4. Implementation 5. Results 6. Future work 7. References 2

3 1. Work and team presentation 1. Developed during the first h4d2 hackathon 2. September Include language technologies in a real application (trying to help) 4. Work done in two days 5. Team members: - crisis management systems developer - emergency management practitioner - computational linguist 3

Include language technologies in a real application (trying to help) 4.

4 2. Idea 1. Beginning stage 2. SMS, , Google Talk, Twitter integration into Sahana Eden 3. Messages filtering by topics using language technologies 4. Messages prioritization 5. To be available for disaster managers via Sahana Eden 4

5 2. Sahana Eden 1. Open-source software platform 2. Developed by the Sahana Foundation 3. Aim: provide information management solutions for disaster management 4. Highly configurable and easy to integrate with other systems even during a crisis 5. Used for/by: Haiti Earthquake, Wildfires in Chile, Asian Disaster Preparedness Centre (ADPC) 5

Aim: provide information management solutions for disaster management 4.

6 3. Motivations 1. Need of intelligence on emergencies for decision-taking on response 2. Need of additional information from unconventional sources 3. Current social media tracking initiatives mostly manual 4. Developed tools not publicly available 6

7 3. Related Work Related tools: - Twitcident (Abel, F. et al., 2012) - project EPIC, Univ. of Colorado (Verma S. et al., 2011) - Univ. of Sheffield (Varga, A. et al., 2012) - StandBy Task Force? Other? 7

8 4. Implementation Sahana Eden: 1. Implemented in Python 2. Uses Web2Py 3. Built around a Rapid Application Development (RAD) framework 8

9 4. Implementation 1. User requirements 2. Message filtering 3. Message prioritisation 9

10 4. User Requirements 1. Developed on the basis of practical experience and reference works (Bruinsma, 2010; National Incident Management System, 2008) 2. Workflows showing decision taking steps + necessary information 3. Flexible to reflect different types of, levels of complexity of incidents and needed resources 10

2010; National Incident Management System, 2008) 2.

11 4. User Requirements Three types of accidents (human-made/natural/both): 11

12 4. Message Filtering (Twitter) Hashtags often not used literally ( my exam was a #disaster ) Twitter-specific features (hashtags) +linguistic features (keywords) 12

13 4. Message Filtering (Twitter) Coverage problem Extracting keywords' synonyms Use: Python Natural Language ToolKit (NLTK) (Bird, S. et al., 2009) WordNet (Fellbaum, 1998) Earthquake earthquake, quake, temblor and seism 13

14 4. Message Filtering (Twitter) 14

15 4. Message Prioritisation (Twitter) type (SMS or tweet ) sender (trusted or not trusted ) location (mentioned or not mentioned ) mode (text+image or text ) 15

16 5. Results 16

17 6. Future Work WordNet: Part-of-speech ambiguity (fire: to fire/fervor/ardor) More tweets filtering: number of re-tweets, number of followers and their growth over time (Starbird, K. et al., 2012) Messages categorized by types of events Proper evaluation 17

number of followers and their growth over time (Starbird, K.

18 7. References Abel, F. et al. (2012) Twitcident: Fighting Fire with Information from Social Web Stream. In Proceedings of International Conference on Hypertext and Social Media, Milwaukee, USA. Bird, S.et al. (2009) Natural Language Processing with Python - Analyzing Text with the Natural Language Toolkit. O'Reilly Media. Bruinsma, G. W. J. (2010). Adaptive workflow simulation of emergency response. University of Twente. Fellbaum, C. (1998) WordNet: an Electronic Lexical Database, MIT Press. National Incident Management System. (2008) U.S. Homeland Security, December Starbird, K. et al. (2012) Learning from the Crowd: Collaborative Filtering Techniques for Identifying On-the-Ground Twitterers during Mass Disruptions. ISCRAM Varga, A. et al. (2012). Exploring the Similarity between Social Knowledge Sources and Twitter for Cross-domain Topic Classification. In proceedings of Knowledge Extraction and Consolidation from Social Media (KECSM2012). Verma, S. et al. (2011) Natural Language Processing to the Rescue: Extracting 'Situational Awareness' Tweets During Mass Emergency. Fifth International AAAI Conference on Weblogs and Social Media. 18

Adaptive workflow simulation of emergency response. University of Twente. Fellbaum, C. (1998) WordNet: an Electronic Lexical Database, MIT Press. National Incident Management Sy

19 Contacts Irina Temnikova Dogan Biyikli Francis Boon 19

20 Thank you Any suggestions? 20

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