Analyzing spatial data from twitter during a disaster

Social media can be an invaluable help in a mass emergency, but the information handling can be challenging. One major concern is identifying posts related to the area, or pinning them on a map. This exploratory study analyzes the spatial data coming with tweets during two natural disasters, an earthquake and a hurricane. Geo-tagged tweets confirm to be a small fraction of all tweets and disasters within a limited region appear to be a niche topic in the whole stream. The results can help researchers and practitioners in the design of tools to identify these messages

Why I Retweet? Exploring User’s Perspective on Decision-Making of Information Spreading during Disasters

The extensive use of social media during disasters raises an important issue concerning use of social media to spread information, including misinformation. This study explores the underlying behavioral context of disaster information sharing by Twitter users. We conducted a web survey with 999 respondents in Japan to determine what makes people retweet disaster information in disaster situations. As a result of factor analysis, four factors were identified from 36 questions, namely: 1) Willingness to provide relevant and updated information because the information is believable, 2) Want people to know the information they perceive as important, 3) Retweeter subjective feelings and interests, and 4) Want to get feedback and alert other people. The results suggest that two of the factors influenced different groups of people in the community differently; however, everybody can play their role to reduce the negative impact of social media used for future disaster. Based on the findings, we discuss practical and design implications of social media use during disasters

Human behaviour modelling in complex socio-technical systems : an agent based approach

For many years we have been striving to understand human behaviour and our interactions with our socio-technological environment. By advancing our knowledge in this area, we have helped the design of new or improved work processes and technologies. Historically, much of the work in analysing social interactions has been conducted within the social sciences. However, computer simulation has brought an extra tool in trying to understand and model human behaviours. Using an agent based approach this presentation describes my work in constructing computational models of human behaviour for informing design through simulation. With examples from projects in two main application areas of crisis and emergency management, and energy management I describe how my work addresses some main issues in agent based social simulation. The first concerns the process by which we develop these models. The second lies in the nature of socio-technical systems. Human societies are a perfect example of a complex system exhibiting characteristics of self-organisation, adaptability and showing emergent phenomena such as cooperation and robustness. I describe how complex systems theory may be applied to improve our understanding of socio-technical systems, and how our micro level interactions lead to emergent mutual awareness for problem-solving. From agent based simulation systems I show how context awareness may be modelled. Looking forward to the future, I discuss how the increasing prevalence of artificial agents in our society will cause us to re-examine the new types of interactions and cooperative behaviours that will emerge.Depuis de nombreuses années, nous nous sommes efforcés de comprendre le comportement humain et nos interactions avec l'environnement sociotechnique. Grâce à l'avancée de nos connaissances dans ce domaine, nous avons contribué à la conception de technologies et de processus de travail nouveaux ou améliorés. Historiquement, une part importante du travail d'analyse des interactions sociales fut entreprise au sein des sciences sociales. Cependant, la simulation informatique a apporté un nouvel outil pour tenter de comprendre et de modéliser les comportements humains. En utilisant une approche à base d'agents, cette présentation décrit mon travail sur la construction de modèles informatiques du comportement humain pour guider la conception par la simulation. A l'aide d'exemples issus de projets des deux domaines d'application que sont la gestion des crises et de l'urgence et la gestion de l'énergie, je décris comment mon travail aborde certains problèmes centraux à la simulation sociale à base d'agents. Le premier concerne le processus par lequel nous développons ces modèles. Le second problème provient de la nature des systèmes sociotechniques. Les sociétés humaines constituent un exemple parfait de système complexe possédant des caractéristiques d'auto-organisation et d'adaptabilité, et affichant des phénomènes émergents tels que la coopération et la robustesse. Je décris comment la théorie des systèmes complexes peut être appliquée pour améliorer notre compréhension des systèmes sociotechniques, et comment nos interactions au niveau microscopique mènent à l'émergence d'une conscience mutuelle pour la résolution de problèmes. A partir de systèmes de simulation à base d'agents, je montre comment la conscience du contexte peut être modélisée. En terme de perspectives, j'expliquerai comment la hausse de la prévalence des agents artificiels dans notre société nous forcera à considérer de nouveaux types d'interactions et de comportements coopératifs

Classification algorithms for Big Data with applications in the urban security domain

A classification algorithm is a versatile tool, that can serve as a predictor for the future or as an analytical tool to understand the past. Several obstacles prevent classification from scaling to a large Volume, Velocity, Variety or Value. The aim of this thesis is to scale distributed classification algorithms beyond current limits, assess the state-of-practice of Big Data machine learning frameworks and validate the effectiveness of a data science process in improving urban safety. We found in massive datasets with a number of large-domain categorical features a difficult challenge for existing classification algorithms. We propose associative classification as a possible answer, and develop several novel techniques to distribute the training of an associative classifier among parallel workers and improve the final quality of the model. The experiments, run on a real large-scale dataset with more than 4 billion records, confirmed the quality of the approach. To assess the state-of-practice of Big Data machine learning frameworks and streamline the process of integration and fine-tuning of the building blocks, we developed a generic, self-tuning tool to extract knowledge from network traffic measurements. The result is a system that offers human-readable models of the data with minimal user intervention, validated by experiments on large collections of real-world passive network measurements. A good portion of this dissertation is dedicated to the study of a data science process to improve urban safety. First, we shed some light on the feasibility of a system to monitor social messages from a city for emergency relief. We then propose a methodology to mine temporal patterns in social issues, like crimes. Finally, we propose a system to integrate the findings of Data Science on the citizenry’s perception of safety and communicate its results to decision makers in a timely manner. We applied and tested the system in a real Smart City scenario, set in Turin, Italy

Aligning Social Media, Mobile, Analytics, and Cloud Computing Technologies and Disaster Response

After nearly 2 decades of advances in information and communications technologies (ICT) including social media, mobile, analytics, and cloud computing, disaster response agencies in the United States have not been able to improve alignment between ICT-based information and disaster response actions. This grounded theory study explored emergency response ICT managers\u27 understanding of how social media, mobile, analytics, and cloud computing technologies (SMAC) are related to and can inform disaster response strategies. Sociotechnical theory served as the conceptual framework to ground the study. Data were collected from document reviews and semistructured interviews with 9 ICT managers from emergency management agencies in the state of Hawaii who had experience in responding to major disasters. The data were analyzed using open, axial coding, and selective coding. Three elements of a theory emerged from the findings: (a) the ICT managers were hesitant about SMAC technologies replacing first responder\u27s radios to interoperate between emergency response agencies during major disasters, (b) the ICT managers were receptive to converging conventional ICT with SMAC technologies, and (c) the ICT managers were receptive to joining legacy information sharing strategies with new information sharing strategies based on SMAC technologies. The emergent theory offers a framework for aligning SMAC technologies and disaster response strategies. The implications for positive social change include reduced interoperability failures between disaster agencies during major catastrophes, which may lower the risk of casualties and deaths to emergency responders and disaster victims, thus benefiting them and their communities

Data fusion for human intelligence and crisis management : handling information from untrusted sources

Situation awareness is a key requirement in managing civil contingencies, since major incidents, accidents and natural disasters are by their very nature highly unpredictable and confusing situations. It is important that those responsible for dealing with them have the best available information. The mash-up approach brings together information from multiple public and specialist sources to form a synoptic view, but the controller is still faced with multiple, partial and possibly conflicting reports from untrusted sources. The aim of this research is to investigate how the varying provenance of the data can be tracked and exploited to prioritise the information presented to a busy incident controller, and to synthesise a model or models of the situation that the evidence pertains to. The approach in this research is to develop a system involving novel approach and techniques to allow incident controllers and similar decision makers to augment official information input streams with information contributed by the wider public (either explicitly submitted to them or harvested from social networks such as Facebook and Twitter), and to be able to handle inconsistencies and uncertainty arising from the unreliability of such sources in a flexible way. The system takes in situational data in a structured format, such as the Tactical Situation Object (TSO) proposed by OASIS, a project funded by the European Framework Programme 6 (FP6) and performs an automated logical consistency checking in order to isolate inconsistent and absurd messages, identify the inconsistency between messages and cluster the consistent messages together. Each cluster of consistent messages that gives a possible view of a situation that the evidence pertains to is referred to as a `World View'. The logical consistency checking is performed using Alloy and Alloy Analyzer (sic). Finally, the system presents a set of possible world views, each internally consistent, which are ranked based upon an initial information provenance and quality metric (configured by the user) which is used to score the individual data items. The provenance and quality metric includes those factors that influence trust in information such as identity and location of informant, reputation, corroboration, freshness of information, etc. The result is a set of world views prioritised according to the provenance, trust and information quality metric. This thesis also presents some experimental results as proof of the concept. The experimentation has been carried out with a very small set of data to make the automation (automatic experimentation) feasible. However, a theoretical proof is offered to demonstrate the viability of the concept. Future work includes testing the system in real-life cases, in order to understand the utility of the system

Social media and SMS in the haiti earthquake

International audienceWe describe some first results of an empirical study describing how social media and SMS were used in coordinating humanitarian relief after the Haiti Earthquake in January 2010. Current information systems for crisis management are increasingly incorporating information obtained from citizens transmitted via social media and SMS. This information proves particularly useful at the aggregate level. However it has led to some problems: information overload and processing difficulties, variable speed of information delivery, managing volunteer communities, and the high risk of receiving inaccurate or incorrect information