Bella Robinson

Using Social Media to Enhance Emergency Situation Awareness

The described system uses natural language processing and data mining techniques to extract situation awareness information from Twitter messages generated during various disasters and crises.

Emergency situation awareness from twitter for crisis management

This paper describes ongoing work with the Australian Government to detect, assess, summarise, and report messages of interest for crisis coordination published by Twitter. The developed platform and client tools, collectively termed the Emergency Situation Awareness - Automated Web Text Mining (ESA-AWTM) system, demonstrate how relevant Twitter messages can be identified and utilised to inform the situation awareness of an emergency incident as it unfolds. A description of the ESA-AWTM platform is presented detailing how it may be used for real life emergency management scenarios. These scenarios are focused on general use cases to provide: evidence of pre-incident activity; near-real-time notification of an incident occurring; first-hand reports of incident impacts; and gauging the community response to an emergency warning. Our tools have recently been deployed in a trial for use by crisis coordinators.

Modeling water resource systems using a service-oriented computing paradigm

Service-oriented computing is a software engineering paradigm that views complex software systems as an interconnected collection of distributed computational components. Each component has a defined web service interface that allows it to be loosely-coupled with client applications. The service-oriented paradigm presents an attractive way of modeling multidisciplinary water resource systems because it allows a diverse community of scientists and engineers to work independently on components of a larger modeling system. While a service-oriented paradigm has been successfully applied for integrating water resource data, this paper considers service-oriented computing as an approach for integrating water resource models. We present an interface design for exposing water resource models as web services and demonstrate how it can be used to simulate a rainfall/runoff event within a watershed system. We discuss the advantages and disadvantages of using service-oriented computing for modeling water resource systems, and conclude with future work needed to advance the application of service-oriented computing for modeling water resource systems.

A sensitive Twitter earthquake detector

This paper describes early work at developing an earthquake detector for Australia and New Zealand using Twitter. The system is based on the Emergency Situation Awareness (ESA) platform which provides all-hazard information captured, filtered and analysed from Twitter. The detector sends email notifications of evidence of earthquakes from Tweets to the Joint Australian Tsunami Warning Centre. The earthquake detector uses the ESA platform to monitor Tweets and checks for specific earthquake related alerts. The Tweets that contribute to an alert are then examined to determine their locations: when the Tweets are identified as being geographically close and the retweet percentage is low an email notification is generated. The earthquake detector has been in operation since December 2012 with 31 notifications generated where 17 corresponded with real, although minor, earthquake events. The remaining 14 were a result of discussions about earthquakes but not prompted by an event. A simple modification to our algorithm results in 20 notifications identifying the same 17 real events and reducing the false positives to 3. Our detector is sensitive in that it can generate alerts from only a few Tweets when they are determined to be geographically close.

ESA: emergency situation awareness via microbloggers

During a disastrous event, such as an earthquake or river flooding, information on what happened, who was affected and how, where help is needed, and how to aid people who were affected, is crucial. While communication is important in such times of crisis, damage to infrastructure such as telephone lines makes it difficult for authorities and victims to communicate. Microblogging has played a critical role as an important communication platform during crises when other media has failed. We demonstrate our ESA (Emergency Situation Awareness) system that mines microblogs in real-time to extract and visualise useful information about incidents and their impact on the community in order to equip the right authorities and the general public with situational awareness.

Emergency Situation Awareness: Twitter Case Studies

The Emergency Situation Awareness (ESA) system provides all-hazard situation awareness information for emergency managers using content gathered from the public Twitter API. It collects, filters and analyses Tweets from specific regions of interest in near-real-time, enabling effective alerting for unexpected incidents and monitoring of emergency events with results accessible via an interactive website.

Comparing web feeds and tweets for emergency management

This paper describes ongoing work with the Australian Government to assemble information from a collection of web feeds describing emergency incidents of interest for emergency managers. The developed system, the Emergency Response Intelligence Capability (ERIC) tool, has been used to gather information about emergency events during the Australian summer of 2012/13. The web feeds are an authoritative source of structured information summarising incidents that includes links to emergency services web sites containing further details about the events underway. The intelligence obtained using ERIC for a specific fire event has been compared with information that was available in Twitter using the Emergency Situation Awareness (ESA) platform. This information would have been useful as a new source of intelligence: it was reported faster than via the web feed, contained more specific event information, included details of impact to the community, was updated more frequently, included information from the public and remains available as a source of information long after the web feed contents have been removed.

Image Classification to Support Emergency Situation Awareness

Recent advances in image classification methods, along with the availability of associated tools, has seen their use become widespread in many domains. This paper presents a novel application of current image classification approaches in the area of emergency situation awareness. We discuss image classification based on low level features as well as methods built on top of pre-trained classifiers. The performance of the classifiers are assessed in terms of accuracy along with consideration to computational aspects given the size of the image database. Specifically, we investigate image classification in the context of a bush fire emergency in the Australian state of NSW where images associated with Tweets during the emergency were used to train and test classification approaches. Emergency service operators are interested in having images relevant to such fires reported as extra information to help manage evolving emergencies. We show that these methodologies can classify images into fire and not fire related classes with an accuracy of 86%.

TweetRipple: Understanding Your Twitter Audience and the Impact of Your Tweets

Social media platforms have become a main stream communication medium, and many organisations, including government agencies, now routinely employ both Twitter and Facebook to disseminate information and engage with their customer base. A few questions then arise, e.g., what is the effectiveness of ones engagement on social media? Who is being reached? Is it the intended audience? What is the impact? Or, as some ask, what is the Return on Investment (ROI)? In our work, we are attempting to answer these questions. We suggest that the ROI is multifaceted, and present a framework (CAMIO) to study it. We also describe a system, called TweetRipple, built on CAMIO to support for people to start answering these questions.

The Effects of Data Collection Methods in Twitter.

There have been recent efforts to use social media to estimate demographic characteristics, such as age, gender or income, but there has been little work on investigating the effect of data acquisition methods on producing these estimates. In this paper, we compare four different Twitter data acquisition methods and explore their effects on the prediction of one particular demographic characteristic: occupation (or profession). We present a comparative analysis of the four data acquisition methods in the context of estimating occupation statistics for Australia. Our results show that the social network-based data collection method seems to perform the best. However, we note that each different data collection approach has its own benefits and limitations.