Konstantinos Domdouzis

Combining Big Social Media Data and FCA for Crisis Response

The use of social media is now prevalent in all aspects of society. Each and every major news event is accompanied by a stream of real-time social media posts, creating a vast and ever changing data supply - a hallmark of big data. The aim of the ATHENA project is to turn this stream of information into a vital resource for the public and first responders during a crisis. ATHENA recognizes that citizens are currently under-utilized in crises and that they are often willing to engage in the response effort. Social media provides a robust platform for this interaction. Due to the volume and fast paced nature of the incoming data streams, the challenges for ATHENA system are how to integrate and process this data and then how to transform it into intelligence to be used by law enforcement agencies and the public alike. This paper introduces potential applications within the ATHENA project, in particular, those based on a technique known as formal concept analysis.

Creating corroborated crisis reports from social media data through formal concept analysis

During a crisis citizens reach for their smart phones to report, comment and explore information surrounding the crisis. These actions often involve social media and this data forms a large repository of real-time, crisis related information. Law enforcement agencies and other first responders see this information as having untapped potential. That is, it has the capacity extend their situational awareness beyond the scope of a usual command and control centre. Despite this potential, the sheer volume, the speed at which it arrives, and unstructured nature of social media means that making sense of this data is not a trivial task and one that is not yet satisfactorily solved; both in crisis management and beyond. Therefore we propose a multi-stage process to extract meaning from this data that will provide relevant and near real-time information to command and control to assist in decision support. This process begins with the capture of real-time social media data, the development of specific LEA and crisis focused taxonomies for categorisation and entity extraction, the application of formal concept analysis for aggregation and corroboration and the presentation of this data via map-based and other visualisations. We demonstrate that this novel use of formal concept analysis in combination with context-based entity extraction has the potential to inform law enforcement and/or humanitarian responders about on-going crisis events using social media data in the context of the 2015 Nepal earthquake.

A social media and crowdsourcing data mining system for crime prevention during and post-crisis situations

Purpose A number of crisis situations, such as natural disasters, have affected the planet over the past decade. The outcomes of such disasters are catastrophic for the infrastructures of modern societies. Furthermore, after large disasters, societies come face-to-face with important issues, such as the loss of human lives, people who are missing and the increment of the criminality rate. In many occasions, they seem unprepared to face such issues. This paper aims to present an automated social media and crowdsourcing data mining system for the synchronization of the police and law enforcement agencies for the prevention of criminal activities during and post a large crisis situation. Design/methodology/approach The paper realized qualitative research in the form of a review of the literature. This review focuses on the necessity of using social media and crowdsourcing data mining techniques in combination with advanced Web technologies for the purpose of providing solutions to problems related to criminal activities caused during and after a crisis. The paper presents the ATHENA crisis management system, which uses a number of data mining techniques to collect and analyze crisis-related data from social media for the purpose of crime prevention. Findings Conclusions are drawn on the significance of social media and crowdsourcing data mining techniques for the resolution of problems related to large crisis situations with emphasis to the ATHENA system. Originality/value The paper shows how the integrated use of social media and data mining algorithms can contribute in the resolution of problems that are developed during and after a large crisis.

Service-Oriented Design of a Command and Control Intelligence Dashboard for Crisis Management

The number and intensity of crisis incidents that have happened worldwide in the last decade, such as the Haiti earthquake and the Mumbai bombings, have revealed the need for an organized system to support search and rescue operations. This paper presents such a system and focuses on one of its central elements, the Crisis Management Command and Control Intelligence Dashboard (CCCID). The paper presents the Service-Oriented Architecture (SOA) approach that was followed for the design of the dashboard and explains its specialized functionalities. A number of conclusions are drawn in relation to the efficiency of the dashboard for crisis management.

Social media and its role for LEAs: Review and applications

Social media has become a major aspect of online activity, and thus an essential part of cybercrime and cyber terrorism-related operations. As LEAs (law enforcement agencies) focus on cybercrime and cyber terrorism threats increases, so does the requirement to consider the potential application of social media as a vital aspect of any cyber defense strategy. In order to develop an understanding of cyber-related activity, an appreciation of social medias role in society is required to enable the development of strategies that tackle not only cybercrime and cyber terrorism, but also crimes facilitated through the use of social media. These include its potential exploitation in combatting a wide variety of criminal threats, such as those identified in the scenarios in the section “LEA Usage Scenarios for Social Media,” and ultimately in the development of competitive advantage over a wide variety of illicit criminal activity.

Analyzing Crowd-Sourced Information and Social Media for Crisis Management

The analysis of potentially large volumes of crowd-sourced and social media data is central to meeting the requirements of the ATHENA project. Here, we discuss the various stages of the pipeline process we have developed, including acquisition of the data, analysis, aggregation, filtering, and structuring. We highlight the challenges involved when working with unstructured, noisy data from sources such as Twitter, and describe the crisis taxonomies that have been developed to support the tasks and enable concept extraction. State-of-the-art techniques such as formal concept analysis and machine learning are used to create a range of capabilities including concept drill down, sentiment analysis, credibility assessment, and assignment of priority. We ground many of these techniques using results obtained from a set of tweets which emerged from the Colorado wildfires of 2012 in order to demonstrate the applicability of our work to real crisis scenarios.

Sustainable cloud computing

Sustainability is an important subject that is part of worldwide policy agendas for economic, social, and environmental transformation. Solutions, especially for environmental issues, must be found and necessary measures be taken to address them. Cloud computing is a new technology that has been developed in order to provide a new era of applications, which will allow more efficient use of computing power. In this chapter, a thorough introduction to cloud computing is realized with emphasis on its advantages for environmental sustainability. A list of challenges in relation to the use of the technology as green technology is presented, and the reasons for using cloud computing for sustainability are explained. Finally, a detailed list of the applications of cloud computing focusing on social, business, and environmental sustainability are listed, and a number of conclusions are provided.

Artificial intelligence-based service-oriented architectures (SOAs) for crisis management

The complexity of crisis-related situations requires the use of advanced technological infrastructures. In order to develop such infrastructures, specific architectures need to be applied such as the Service-oriented Architectures (SOAs). The purpose of this chapter is to indicate how SOAs can be used in modern Crisis Management systems, such as the ATHENA system. The chapter underlines the need for a detailed study of specific biological systems, such as the human brains hippocampus which follows the current, intense attempts of improvement of the current Artificial Intelligence-based systems and the development of a new area in Artificial Intelligence. A number of conclusions are drawn on how biologically-inspired systems can benefit the development of Service-oriented Architectures.

The ATHENA Mobile Application

For large crisis events, mobile devices and applications can contribute significantly in data exchange amongst citizens, first responders, police, and Law-Enforcement Agencies. Previous work in this field illustrates a mobile communication system is required that will combine the flexibility of using different communication technologies, constant availability, and the use of social media for communicating crisis-related information. The design of the ATHENA mobile application was based on the consideration of these factors and this design is presented in more detail in this chapter..

Identification of Critical Factors in Large Crisis Decision Making Processes Using Computational Tools: The Case of ATHENA

Crises are a constant element of modern day life. Earthquakes, floods, terrorist acts are major examples of crises that occur in different areas under different frequencies. Crisis situations are always dynamic and they are characterised by unpredictable consequences that societies cannot always handle. The dynamics of crisis situations are such that societies have to re-evaluate and re-design their policies. This paper aims to present a Social Media-based system that coordinates the responses of the authorities in a large crisis. The paper performs extensive review of literature in order to identify decision making approaches in crisis situations and the different factors that affect these approaches. It also presents the ATHENA Crisis Management system which is based on a platform that makes combined use of data mining algorithms for the purpose of analysing large amounts of data received through the Social Media during and after a large crisis. A number of conclusions are drawn on the identification of different types of factors that impact large crisis decision making.