SUPER: Towards the Use of Social Sensors for Security Assessments and Proactive Management of Emergencies

Social media statistics during recent disasters (e.g. the 20 million tweets relating to 'Sandy' storm and the sharing of related photos in Instagram at a rate of 10/sec) suggest that the understanding and management of real-world events by civil protection and law enforcement agencies could benefit from the effective blending of social media information into their resilience processes. In this paper, we argue that despite the widespread use of social media in various domains (e.g. marketing/branding/finance), there is still no easy, standardized and effective way to leverage different social media streams -- also referred to as social sensors -- in security/emergency management applications. We also describe the EU FP7 project SUPER (Social sensors for secUrity assessments and Proactive EmeRgencies management), started in 2014, which aims to tackle this technology gap

Mejorando los sistemas rurales de alertas tempranas a través de la integración de OpenBTS y JAIN SLEE

Actualmente existe una tendencia que combina las características de los servicios Web 2.0 y los servicios de telecomunicaciones, conocida como Telco 2.0. Estos servicios convergentes se han aplicado exitosamente en sistemas de alertas tempranas, proporcionando mayor agilidad y flexibilidad en la prestación de servicios. Sin embargo, existen varias limitantes que no permiten el despliegue de servicios convergentes en las zonas rurales de países en vía de desarrollo, como la falta de disponibilidad de una ngn (Next Generation Network), la ausencia de tecnología avanzada y la falta de recursos para inversión. Este artículo propone una arquitectura de integración entre jain slee y OpenBTS para sistemas rurales de alertas tempranas. Se evalúa el prototipo implementado con un caso de estudio específico al enviar advertencias Telco 2.0 a los cafeteros colombianos cuyas plantaciones puedan verse afectadas por la roya, una de las enfermedades más peligrosas para la producción de café.Nowadays exists a trend that combines the features of Web 2.0 services and telecommunications services known as Telco 2.0. These converged services have been successfully implemented in early warning systems providing improved agility and flexibility in service delivery. However the deployment of converged services in rural zones of developing countries presents several constraints which do not allow to provide this kind of services, as the unavailability of a Next Generation Network (ngn), absence of advanced technology and lack of investment resources. This paper proposes a jain slee and OpenBTS integration architecture for early warning systems in rural zones. The implemented prototype is evaluated with a specific case study involving the deployment of Telco 2.0 warnings in Colombian coffee plantations which may be affected by coffee rust, one of the most threatening diseases in coffee production

$1.00 per RT #BostonMarathon #PrayForBoston: analyzing fake content on Twitter

This study found that 29% of the most viral content on Twitter during the Boston bombing crisis were rumors and fake content.AbstractOnline social media has emerged as one of the prominent channels for dissemination of information during real world events. Malicious content is posted online during events, which can result in damage, chaos and monetary losses in the real world. We analyzed one such media i.e. Twitter, for content generated during the event of Boston Marathon Blasts, that occurred on April, 15th, 2013. A lot of fake content and malicious profiles originated on Twitter network during this event. The aim of this work is to perform in-depth characterization of what factors influenced in malicious content and profiles becoming viral. Our results showed that 29% of the most viral content on Twitter, during the Boston crisis were rumors and fake content; while 51% was generic opinions and comments; and rest was true information. We found that large number of users with high social reputation and verified accounts were responsible for spreading the fake content. Next, we used regression prediction model, to verify that, overall impact of all users who propagate the fake content at a given time, can be used to estimate the growth of that content in future. Many malicious accounts were created on Twitter during the Boston event, that were later suspended by Twitter. We identified over six thousand such user profiles, we observed that the creation of such profiles surged considerably right after the blasts occurred. We identified closed community structure and star formation in the interaction network of these suspended profiles amongst themselves