Towards comprehensive community based disaster management

Social Computing and Crisis in the New Information Age

Marc van den Homberg, 8 th of October 2013

marc.vandenhomberg@tno.nl

Topics HFM -RWS 241 “Social Media and Information Technology for Disaster and Crisis Response”

Current practices in information sharing

Novel technologies for information sharing and disaster information

coordination

Social trust, social cyber-threats, and related socio-culture aspects

Development of an exercise in information sharing in a disaster

scenario.

Current practices in information sharing

Source: UN OCHA

Humanitarianism in the network age, 2013,

Nethope report 2010

New practices…

Traditional practices

of information

sharing

New practices, social media

Challenges of information sharing betweenaffected and supporting community

Multitude of data sources

Coupling of traditional with social media data

Aggregation and validation of data

Information not well adapted to local context

(illiteracy, language, documented expertise vs storytelling)

Technology gap

Using too advanced technology which does not ‘reach’ communities

Often one-way communication, with a very limited feedback loop

Collaboration gaps

Reluctance between actors to share information

Professionals versus volunteer communities

Novel technologies for information sharing and disaster information coordination

Community based comprehensive recovery

www.cobacore.eu

Collaboration gaps right after a disaster

respondingprofessionals

respondingcommunity

affected community

resilient community

From external response to community driven crisis response

individual and collective needs

individual and collective capabilities

Current response models mostly between affected community and responding professionals

COBACORE includes the interaction with responding community

10

COBACORE; EU FP7 project No. 313308

Expert finder

Pluralism monitor

Media miningPress freedom

ZimbabweGlobal

Expert panel for validation (media experts, a.o. IPDC UNESCO)

Data acquisition

Annotation Analysis Interpretation

TOPICS ACTORS

AT

TE

NT

ION

� Topics (e.g., agriculture, politics,

economy)

� Election news or not

� Foreign news versus national and

local news

� Specific events

� Regions

Individuals

� names

� affiliations (with groups in society)

� gender of individuals

� pro or against government

� function (MP, minister, ..)

� political party

� government versus opposition

Groups

� religious

� ethnic

� NGO

� political party

SE

NT

IME

NT Tone (positive/negative)

Use of hate speech

Annotation

Incremental supervised learning architecture

Text analysis

engine

Topic

detection/

classification

Sentiment

analysis

Named entity

recognition

Online news

incremental learning

supervision

DB/KB

Demo: text mining on news data in action!

Twitcident

Filtering social media (tweets) to obtain real-time intelligence for support of operational emergency services. Also for prevention and analysis.

Contact details

Marc van den Homberg T +31 88 8667135

Senior business consultant ICT4D M +31 6 51069884

Oude Waalsdorperweg 63,

2597 AK Den Haag E marc.vandenhomberg@tno.nl

The Netherlands S marcvandenhomberg