Verifying baselines for crisis event information classification on Twitter

Social media are rich information sources during and in the aftermath of crisis events such as earthquakes and terrorist attacks. Despite myriad challenges, with the right tools, significant insight can be gained which can assist emergency responders and related applications. However, most extant approaches are incomparable, using bespoke definitions, models, datasets and even evaluation metrics. Furthermore, it is rare that code, trained models, or exhaustive parametrisation details are made openly available. Thus, even confirmation of self-reported performance is problematic; authoritatively determining the state of the art (SOTA) is essentially impossible. Consequently, to begin addressing such endemic ambiguity, this paper seeks to make 3 contributions: 1) the replication and results confirmation of a leading (and generalisable) technique; 2) testing straightforward modifications of the technique likely to improve performance; and 3) the extension of the technique to a novel and complimentary type of crisis-relevant information to demonstrate it’s generalisability

Sense-Giving Strategies of Media Organisations in Social Media Disaster Communication: Findings from Hurricane Harvey

Media organisations are essential communication stakeholders in social media disaster communication during extreme events. They perform gatekeeper and amplification roles which are crucial for collective sense-making processes. In that capacity, media organisations distribute information through social media, use it as a source of information, and share such information across different channels. Yet, little is known about the role of media organisations on social media as supposed sense-givers to effectively support the creation of mutual sense. This study investigates the communication strategies of media organisations in extreme events. To that matter, a Twitter dataset consisting of 9,414,463 postings was collected during Hurricane Harvey in 2017. We employed social network analysis and content analysis methods to identify media communication approaches. Three different sense-giving strategies were identified: retweeting local in-house outlets; bound amplification of messages of individual associated journalists; and open message amplification

Social media and its impact on crisis communication: Case studies of Twitter use in emergency management in Australia and New Zealand

There is a growing awareness worldwide of the significance of social media to communication in times of both natural and human-created disasters and crises. While the media have long been used as a means of broadcasting messages to communities in times of crisis – bushfires, floods, earthquakes etc. – the significance of social media in enabling many-to-many communication through ubiquitous networked computing and mobile media devices is becoming increasingly important in the fields of disaster and emergency management. This paper undertakes an analysis of the uses made of social media during two recent natural disasters: the January 2011 floods in Brisbane and South-East Queensland in Australia, and the February 2011 earthquake in Christchurch, New Zealand. It is part of a wider project being undertaken by a research team based at the Queensland University of Technology in Brisbane, Australia, that is working with the Queensland Department of Community Safety (DCS) and the EIDOS Institute, and funded by the Australian Research Council (ARC) through its Linkages program. The project combines large-scale, quantitative social media tracking and analysis techniques with qualitative cultural analysis of communication efforts by citizens and officials, to enable both emergency management authorities and news media organisations to develop, implement, and evaluate new social media strategies for emergency communication

Information spreading during emergencies and anomalous events

The most critical time for information to spread is in the aftermath of a serious emergency, crisis, or disaster. Individuals affected by such situations can now turn to an array of communication channels, from mobile phone calls and text messages to social media posts, when alerting social ties. These channels drastically improve the speed of information in a time-sensitive event, and provide extant records of human dynamics during and afterward the event. Retrospective analysis of such anomalous events provides researchers with a class of "found experiments" that may be used to better understand social spreading. In this chapter, we study information spreading due to a number of emergency events, including the Boston Marathon Bombing and a plane crash at a western European airport. We also contrast the different information which may be gleaned by social media data compared with mobile phone data and we estimate the rate of anomalous events in a mobile phone dataset using a proposed anomaly detection method.Comment: 19 pages, 11 figure

Early detection of heterogeneous disaster events using social media

This article addresses the problem of detecting crisis‐related messages on social media, in order to improve the situational awareness of emergency services. Previous work focused on developing machine‐learning classifiers restricted to specific disasters, such as storms or wildfires. We investigate for the first time methods to detect such messages where the type of the crisis is not known in advance, that is, the data are highly heterogeneous. Data heterogeneity causes significant difficulties for learning algorithms to generalize and accurately label incoming data. Our main contributions are as follows. First, we evaluate the extent of this problem in the context of disaster management, finding that the performance of traditional learners drops by up to 40% when trained and tested on heterogeneous data vis‐á‐vis homogeneous data. Then, in order to overcome data heterogeneity, we propose a new ensemble learning method, and found this to perform on a par with the Gradient Boosting and AdaBoost ensemble learners. The methods are studied on a benchmark data set comprising 26 disaster events and four classification problems: detection of relevant messages, informative messages, eyewitness reports, and topical classification of messages. Finally, in a case study, we evaluate the proposed methods on a real‐world data set to assess its practical value

Social Media Data in an Augmented Reality System for Situation Awareness Support in Emergency Control Rooms

During crisis situations, emergency operators require fast information access to achieve situation awareness and make the best possible decisions. Augmented reality could be used to visualize the wealth of user-generated content available on social media and enable context-adaptive functions for emergency operators. Although emergency operators agree that social media analytics will be important for their future work, it poses a challenge to filter and visualize large amounts of social media data. We conducted a goal-directed task analysis to identify the situation awareness requirements of emergency operators. By collecting tweets during two storms in Germany we evaluated the usefulness of Twitter data for achieving situation awareness and conducted interviews with emergency operators to derive filter strategies for social media data. We synthesized the results by discussing how the unique interface of augmented reality can be used to integrate social media data into emergency control rooms for situation awareness support.publishedVersio

Expanding Awareness: Comparing Location, Keyword, and Network Filtering Methods to Collect Hyperlocal Social Media Data

Opportunities to collect real-time social media data during a crisis remain limited to location and keyword filtering despite the sparsity of geographic metadata and the tendency of keyword-based methods to capture information posted by remote rather than local users. Here we introduce a third, network filtering method that uses social network ties to infer the location of social media users in a geographic community and collect data from networks of these users during a crisis. In this paper we compare all three methods by analyzing the distribution of situational reports of infrastructure damage and service disruption across location, keyword, and network-filtered social media data during a weather emergency. We find that network filtering doubles the number of situational reports collected in real-time compared to location and keyword filtering alone, but that all three methods collect unique reports that can support situational awareness of incidents occurring across a community