Crisis Event Extraction Service (CREES) - Automatic Detection and Classification of Crisis-related Content on Social Media

Social media posts tend to provide valuable reports during crises. However, this information can be hidden in large amounts of unrelated documents. Providing tools that automatically identify relevant posts, event types (e.g., hurricane, floods, etc.) and information categories (e.g., reports on affected individuals, donations and volunteering, etc.) in social media posts is vital for their efficient handling and consumption. We introduce the Crisis Event Extraction Service (CREES), an open-source web API that automatically classifies posts during crisis situations. The API provides annotations for crisis-related documents, event types and information categories through an easily deployable and accessible web API that can be integrated into multiple platform and tools. The annotation service is backed by Convolutional Neural Networks (CNNs) and validated against traditional machine learning models. Results show that the CNN-based API results can be relied upon when dealing with specific crises with the benefits associated with the usage word embeddings

Turning data into action:Supporting humanitarian field workers with open data

In the aftermath of disasters, information is of the essence for humanitarian decision makers in the field. Their concrete information needs is highly context-influenced and often they find themselves unable to access the right information at the right time. We propose a novel ICT-based approach to address these information needs more accurately. First, we select a group of in-field decision makers and collect their concrete information needs in the disaster aftermath. We then review to what extent existing data and tools can already address these needs. We conclude that existing solutions fall short in meeting important information needs of the selected group. We describe the design of an information system prototype to address these gaps more accurately. We combine data of the International Aid Transparency Initiative and the Humanitarian Data Exchange to form the data-backend of our system. We describe our implementation approach and evaluation plan

Helping crisis responders find the informative needle in the tweet haystack

Crisis responders are increasingly using social media, data and other digital sources of information to build a situational understanding of a crisis situation in order to design an effective response. However with the increased availability of such data, the challenge of identifying relevant information from it also increases. This paper presents a successful automatic approach to handling this problem. Messages are filtered for informativeness based on a definition of the concept drawn from prior research and crisis response experts. Informative messages are tagged for actionable data -- for example, people in need, threats to rescue efforts, changes in environment, and so on. In all, eight categories of actionability are identified. The two components -- informativeness and actionability classification -- are packaged together as an openly-available tool called Emina (Emergent Informativeness and Actionability)

PERCEIVE: Precipitation Data Characterization by means on Frequent Spatio-Temporal Sequences

Nowadays large amounts of climatology data, including daily precipitation data, are collected by means of sensors located in different locations of the world. The data driven analysis of these large data sets by means of scalable machine learning and data mining techniques allows extracting interesting knowledge from data, inferring interesting patterns and correlations among sets of spatio-temporal events and characterizing them. In this paper, we describe the PERCEIVE framework. PERCEIVE is a data-driven framework based on frequent spatio-temporal sequences and aims at extracting frequent correlations among spatio-temporal precipitation events. It is implemented by using R and Apache Spark, for scalability reasons, and provides also a visualization module that can be used to intuitively show the extracted patterns. A preliminary set of experiments show the efficiency and the effectiveness of PERCEIVE

Information gain in sociotechnical systems

International audienceCommunication issues persist in sociotechnical systems with functioning communication equipment, prompting researchers and practitioners alike to bemoan the absence of information sharing. Computer scientists envision a broadly accessible virtual display, but lack the principles for selecting, formatting and organizing content to make it useful. We argue that what is needed is information rather than data, and that situating data in context is key to the provision of information. Documentation of information exchange issues in real crisis management is quite superficial, generally pointing to conclusions without any supporting data. Using documentation of the Deepwater Horizon Accident in 2010, we suggest three requirements for the design of computationally supported information exchange: 1) computational support to distribute distilled information, not low-level data, 2) a computationally accessible, current plan to provide context to guide the routing of information to interested parties and 3) a means to detect and elevate newly relevant, but formerly suppressed detail

Decision Support for the Optimal Coordination of Spontaneous Volunteers in Disaster Relief

When responding to natural disasters, professional relief units are often supported by many volunteers which are not affiliated to humanitarian organizations. The effective coordination of these volunteers is crucial to leverage their capabilities and to avoid conflicts with professional relief units. In this paper, we empirically identify key requirements that professional relief units pose on this coordination. Based on these requirements, we suggest a decision model. We computationally solve a real-world instance of the model and empirically validate the computed solution in interviews with practitioners. Our results show that the suggested model allows for solving volunteer coordination tasks of realistic size near-optimally within short time, with the determined solution being well accepted by practitioners. We also describe in this article how the suggested decision support model is integrated in the volunteer coordination system which we develop in joint cooperation with a disaster management authority and a software development company

DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank

During crisis events, people often use social media platforms such as Twitter to disseminate information about the situation, warnings, advice, and support. Emergency relief organizations leverage such information to acquire timely crisis circumstances and expedite rescue operations. While existing works utilize such information to build models for crisis event analysis, fully-supervised approaches require annotating vast amounts of data and are impractical due to limited response time. On the other hand, semi-supervised models can be biased, performing moderately well for certain classes while performing extremely poorly for others, resulting in substantially negative effects on disaster monitoring and rescue. In this paper, we first study two recent debiasing methods on semi-supervised crisis tweet classification. Then we propose a simple but effective debiasing method, DeCrisisMB, that utilizes a Memory Bank to store and perform equal sampling for generated pseudo-labels from each class at each training iteration. Extensive experiments are conducted to compare different debiasing methods' performance and generalization ability in both in-distribution and out-of-distribution settings. The results demonstrate the superior performance of our proposed method. Our code is available at https://github.com/HenryPengZou/DeCrisisMB.Comment: Accepted by EMNLP 2023 (Findings

Towards a Taxonomy for Neighborhood Volunteering Management Platforms

The management and organization of volunteering in the social sector have been strongly influenced by technological progress over the last two decades. New proposals for IT-based volunteering management platforms that draw on many elements of social media are appearing with increasing frequency. In this article, we analyzed the current state of the art and use a methodological approach to develop a taxonomy for classifying existing and emerging developments in the field. The taxonomy is intended to assist practitioners in selecting appropriate systems for their respective purposes as well as support researchers in identifying research gaps. The resulting research artifact has undergone an initial evaluation and can support maintaining a better overview in a growing subject area