Mark Cameron

Bio: Mark Cameron is an academic researcher from Commonwealth Scientific and Industrial Research Organisation. The author has contributed to research in topics: Social media & Situation awareness. The author has an hindex of 13, co-authored 35 publications receiving 2022 citations.

Using Social Media to Enhance Emergency Situation Awareness

Abstract: 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.

Understanding Human Mobility from Twitter

Abstract: Understanding human mobility is crucial for a broad range of applications from disease prediction to communication networks. Most efforts on studying human mobility have so far used private and low resolution data, such as call data records. Here, we propose Twitter as a proxy for human mobility, as it relies on publicly available data and provides high resolution positioning when users opt to geotag their tweets with their current location. We analyse a Twitter dataset with more than six million geotagged tweets posted in Australia, and we demonstrate that Twitter can be a reliable source for studying human mobility patterns. Our analysis shows that geotagged tweets can capture rich features of human mobility, such as the diversity of movement orbits among individuals and of movements within and between cities. We also find that short- and long-distance movers both spend most of their time in large metropolitan areas, in contrast with intermediate-distance movers’ movements, reflecting the impact of different modes of travel. Our study provides solid evidence that Twitter can indeed be a useful proxy for tracking and predicting human movement.

Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review

Abstract: Objective Research studies show that social media may be valuable tools in the disease surveillance toolkit used for improving public health professionals’ ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. A social media work group, consisting of surveillance practitioners, academic researchers, and other subject matter experts convened by the International Society for Disease Surveillance, conducted a systematic primary literature review using the PRISMA framework to identify research, published through February 2013, answering either of the following questions: Can social media be integrated into disease surveillance practice and outbreak management to support and improve public health? Can social media be used to effectively target populations, specifically vulnerable populations, to test an intervention and interact with a community to improve health outcomes? Examples of social media included are Facebook, MySpace, microblogs (e.g., Twitter), blogs, and discussion forums. For Question 1, 33 manuscripts were identified, starting in 2009 with topics on Influenza-like Illnesses (n = 15), Infectious Diseases (n = 6), Non-infectious Diseases (n = 4), Medication and Vaccines (n = 3), and Other (n = 5). For Question 2, 32 manuscripts were identified, the first in 2000 with topics on Health Risk Behaviors (n = 10), Infectious Diseases (n = 3), Non-infectious Diseases (n = 9), and Other (n = 10). Conclusions The literature on the use of social media to support public health practice has identified many gaps and biases in current knowledge. Despite the potential for success identified in exploratory studies, there are limited studies on interventions and little use of social media in practice. However, information gleaned from the articles demonstrates the effectiveness of social media in supporting and improving public health and in identifying target populations for intervention. A primary recommendation resulting from the review is to identify opportunities that enable public health professionals to integrate social media analytics into disease surveillance and outbreak management practice.

Emergency situation awareness from twitter for crisis management

Abstract: 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.

A sensitive Twitter earthquake detector

Abstract: 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.

The GARP modelling system: problems and solutions to automated spatial prediction

Abstract: This paper is concerned with the problems and solutions to reliable analysis of arbitrary datasets. Our approach is to describe components of a system called the GARP Modelling System (GMS) which we have developed for automating predictive spatial modelling of the distribution of species of plants and animals. The essence of the system is an underlying generic spatial modelling method which filters out potential sources of errors. The approach is generally applicable however, as the statistical problems arising in arbitrary spatial data analysis potentially apply to any domain. For ease of development, GMS is integrated with the facilities of existing database and visualization tools, and Internet browsers. The GMS is an example of a class of application which has been very successful for providing spatial data analysis in a simple to use way via the Internet.

Processing Social Media Messages in Mass Emergency: A Survey

Abstract: Social media platforms provide active communication channels during mass convergence and emergency events such as disasters caused by natural hazards. As a result, first responders, decision makers, and the public can use this information to gain insight into the situation as it unfolds. In particular, many social media messages communicated during emergencies convey timely, actionable information. Processing social media messages to obtain such information, however, involves solving multiple challenges including: parsing brief and informal messages, handling information overload, and prioritizing different types of information found in messages. These challenges can be mapped to classical information processing operations such as filtering, classifying, ranking, aggregating, extracting, and summarizing. We survey the state of the art regarding computational methods to process social media messages and highlight both their contributions and shortcomings. In addition, we examine their particularities, and methodically examine a series of key subproblems ranging from the detection of events to the creation of actionable and useful summaries. Research thus far has, to a large extent, produced methods to extract situational awareness information from social media. In this survey, we cover these various approaches, and highlight their benefits and shortcomings. We conclude with research challenges that go beyond situational awareness, and begin to look at supporting decision making and coordinating emergency-response actions.

Social media and vaccine hesitancy: new updates for the era of COVID-19 and globalized infectious diseases.

Abstract: Despite major advances in vaccination over the past century, resurgence of vaccine-preventable illnesses has led the World Health Organization to identify vaccine hesitancy as a major threat to global health. Vaccine hesitancy may be fueled by health information obtained from a variety of sources, including new media such as the Internet and social media platforms. As access to technology has improved, social media has attained global penetrance. In contrast to traditional media, social media allow individuals to rapidly create and share content globally without editorial oversight. Users may self-select content streams, contributing to ideological isolation. As such, there are considerable public health concerns raised by anti-vaccination messaging on such platforms and the consequent potential for downstream vaccine hesitancy, including the compromise of public confidence in future vaccine development for novel pathogens, such as SARS-CoV-2 for the prevention of COVID-19. In this review, we discuss the current position of social media platforms in propagating vaccine hesitancy and explore next steps in how social media may be used to improve health literacy and foster public trust in vaccination.