Emergency Situation Awareness: Twitter Case Studies

Power, Robert; Robinson, Bella; Colton, John; McCarthy, Cameron

doi:10.1007/978-3-319-11818-5_19

Cited by 48 publications

(30 citation statements)

References 4 publications

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“…Most supervised machine learning approaches used in this domain rely on linguistic and other statistical attributes of the post such as part of speech (POS), user mentions, length of the post, and number of hashtags. Supervised machine learning approaches range from traditional classification methods such as Support Vector Machines (SVM), Naive Bayes, Conditional Random Fields [20,17,8] to recent trends of deep learning [3]. In [3,4], word embeddings are applied and semantics are added in the form of extracted entities and their types, but adaptability of the model to unseen types of crisis data is not evaluated.…”

Section: Related Workmentioning

confidence: 99%

Classifying Crises-Information Relevancy with Semantics

2018

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Abstract. Social media platforms have become key portals for sharing and consuming information during crisis situations. However, humanitarian organisations and effected communities often struggle to sieve through the large volumes of data that are typically shared on such platforms during crises to determine which posts are truly relevant to the crisis, and which are not. Previous work on automatically classifying crisis information was mostly focused on using statistical features. However, such approaches tend to be inappropriate when processing data on a type of crisis that the model was not trained on, such as processing information about a train crash, whereas the classifier was trained on floods, earthquakes, and typhoons. In such cases, the model will need to be retrained, which is costly and time-consuming. In this paper, we explore the impact of semantics in classifying Twitter posts across same, and different, types of crises. We experiment with 26 crisis events, using a hybrid system that combines statistical features with various semantic features extracted from external knowledge bases. We show that adding semantic features has no noticeable benefit over statistical features when classifying same-type crises, whereas it enhances the classifier performance by up to 7.2% when classifying information about a new type of crisis.

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Section: Related Workmentioning

confidence: 99%

Classifying Crises-Information Relevancy with Semantics

2018

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“…A significant requirement was the ability to find relevant "high value" images such as those with smoke plumes. This collaboration led to the customization of our ESA tool (Power et al, 2014) that provides all-hazard situation awareness information for emergency managers using content gathered from Twitter. This preliminary work was based on our earlier attempts at identifying images of interest, which relied on filtering the tweet message text using keywords and machine learning text classifiers (Power et al, 2013).…”

Section: Background Emergency Situation Awarenessmentioning

confidence: 99%

“…Varga et al (2013) propose methods for matching problem reports to aid messages while Tweet4Act (Chowdhury et al, 2013) filters for irrelevant tweets. Power et al (2014) have developed a system for processing large volumes of Twitter data using language models to identify Tweets of interest to emergency managers. An aspect of social media in relation to disaster management, which has so far received little attention, is images.…”

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confidence: 99%

Image Classification to Support Emergency Situation Awareness

et al. 2016

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Recent advances in image classification methods, along with the availability of associated tools, have 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 pretrained classifiers. The performance of the classifiers is 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%.Keywords: classification, image processing, emergency response, machine learning, situation awareness InTRoDUcTIonIn times of crisis, it is increasingly common for the public to use social media to broadcast their needs, propagate news, and stay abreast of evolving situations (Landwehr and Carley, 2014). Situation awareness during disaster management and emergency response is an evolving area for research. In this context, situation awareness relates to picking up sensory cues from the environment, interpreting said cues, and forecasting what may occur (Endsley, 1995). The ubiquity of social media platforms presents an opportunity to harness developing information to improve situation awareness for management and response teams.With advances in natural language processing (NLP) technologies, attention has been given to research and development for extracting relevant information from streaming data such as Twitter. For example, Sen (2015) investigates finding tweets that do not reflect user sentiment using NLP. Varga et al. (2013) propose methods for matching problem reports to aid messages while Tweet4Act (Chowdhury et al., 2013) filters for irrelevant tweets. Power et al. (2014) have developed a system for processing large volumes of Twitter data using language models to identify Tweets of interest to emergency managers. An aspect of social media in relation to disaster management, which has so far received little attention, is images. Images have the potential to provide new insights on top of the text-derived intelligence in tweets, giving a rich and contextual information stream in crisis situations. For example, images of fires provide an immediate cue to crisis coordinators about an event allowing them to react appropriately. Images provide a less ambiguous insight into a situation compared to subjective textual descriptions. An image can show the size of the fire and also provide clues to environmental conditions such as weather conditions and the potential fuel load ...

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“…Some progress has been made in ERIC by linking to the CSIRO Emergency Situational Awareness (ESA) platform [3] which continuously retrieves and analyses new Twitter posts originating from Australia and New Zealand. It is able to detect high frequency words and alerts the user to these using a tag cloud.…”

Section: Social Media Integrationmentioning

confidence: 99%

The Emergency Response Intelligence Capability Tool

Power

Robinson

Wise

et al. 2015

IFIP Advances in Information and Communication Technology

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Part 2: Information Systems, Information Modelling and SemanticsInternational audienceThe Emergency Response Intelligence Capability (ERIC) tool, http://eric.csiro.au, automatically gathers data about emergency events from authoritative web sources, harmonises the information content and presents it on an interactive map. All data is recorded in a database which allows the changing status of emergency events to be identified and provides an archive for historical review.ERIC was developed for the Australian Government Department of Human Services Emergency Management team who is responsible for intelligence gathering and situation reporting during emergency events. Event information is combined with demographic data to profile the affected community. Identifying relevant community attributes, such as languages spoken or socioeconomic information, allows the department to tailor its response appropriately to better support the impacted community.An overview of ERIC is presented, including its use by the department and the difficulties overcome in establishing and maintaining a nationally consistent harmonised model of emergency event information. Preliminary results of republishing the emergency event information using the Australian Profile of the Common Alerting Protocol, an XML standard to facilitate the construction and exchange of emergency alert and warning messages, are also presented

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Emergency Situation Awareness: Twitter Case Studies

Cited by 48 publications

References 4 publications

Classifying Crises-Information Relevancy with Semantics

Classifying Crises-Information Relevancy with Semantics

Image Classification to Support Emergency Situation Awareness

The Emergency Response Intelligence Capability Tool

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