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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-realtime 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.

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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 ...

On 11 April 2018, the Fight Online Sex Trafficking Act (FOSTA) was signed into law in the United States. FOSTA introduced new provisions to amend the Communications Act of 1934 so that websites can be prosecuted if they engage ‘in the promotion or facilitation of prostitution’ or ‘facilitate traffickers in advertising the sale of unlawful sex acts with sex trafficking victims.’ While supporters of the law claim that its aim is to target human traffickers, its text makes no effort to differentiate between trafficking and consensual sex work and it functionally includes websites where workers advertise services or share information, including safety tips.[3] Following the law’s passage—and even before its full implementation—sex workers felt its impact as websites began to eliminate platforms previously used to advertise services. Backpage, an adult advertising website, was pre-emptively seized by the FBI. Other platforms began to censor or remove content related to sex work, including Google, Craigslist, and many online advertising networks. Sex workers in the United States have denounced the passage of FOSTA for reducing workers’ ability to screen clients and ensure safety practices. This paper provides an overview of the findings of a recent survey with sex workers in the United States, details the advent of similar initiatives in other countries, and explores how the legislation conflates trafficking with consensual sex work.

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