A Linguistically-driven Approach to Cross-Event Damage Assessment of Natural Disasters from Social Media Messages

This work focuses on the analysis of Italian social media messages for disaster management and aims at the detection of messages carrying critical information for the damage assessment task. A main novelty of this study consists in the focus on out-domain and cross-event damage detection, and on the investigation of the most relevant tweet-derived features for these tasks. We devised different experiments by resorting to a wide set of linguistic features qualifying the lexical and grammatical structure of a text as well as ad-hoc features specifically implemented for this task. We investigated the most effective features that allow to achieve the best results. A further result of this study is the construction of the first manually annotated Italian corpus of social media messages for damage assessment

Crisis Mapping during Natural Disasters via Text Analysis of Social Media Messages

Recent disasters demonstrated the central role of social media during emergencies thus motivating the exploitation of such data for crisis mapping. We propose a crisis mapping system that addresses limitations of current state-of-the-art approaches by analyzing the textual content of disaster reports from a twofold perspective. A damage detection component employs a SVM classifier to detect mentions of damage among emergency reports. A novel geoparsing technique is proposed and used to perform message geolocation. We report on a case study to show how the information extracted through damage detection and message geolocation can be combined to produce accurate crisis maps. Our crisis maps clearly detect both highly and lightly damaged areas, thus opening up the possibility to prioritize rescue efforts where they are most needed

Towards better social crisis data with HERMES: Hybrid sensing for EmeRgency ManagEment System

People involved in mass emergencies increasingly publish information-rich contents in online social networks (OSNs), thus acting as a distributed and resilient network of human sensors. In this work, we present HERMES, a system designed to enrich the information spontaneously disclosed by OSN users in the aftermath of disasters. HERMES leverages a mixed data collection strategy, called hybrid crowdsensing, and state-of-the-art AI techniques. Evaluated in real-world emergencies, HERMES proved to increase: (i) the amount of the available damage information; (ii) the density (up to 7x) and the variety (up to 18x) of the retrieved geographic information; (iii) the geographic coverage (up to 30%) and granularity

Pulling Information from Social Media in the Aftermath of Unpredictable Disasters

Social media have become a primary communication channel among people and are continuously overwhelmed by huge volumes of User Generated Content. This is especially true in the aftermath of unpredictable disasters, when users report facts, descriptions and photos of the unfolding event. This material contains actionable information that can greatly help rescuers to achieve a better response to crises, but its volume and variety render manual processing unfeasible. This paper reports the experience we gained from developing and using a web-enabled system for the online detection and monitoring of unpredictable events such as earthquakes and floods. The system captures selected message streams from Twitter and offers decision support functionalities for acquiring situational awareness from textual content and for quantifying the impact of disasters. The software architecture of the system is described and the approaches adopted for messages filtering, emergency detection and emergency monitoring are discussed. For each module, the results of real-world experiments are reported. The modular design makes the system easy configurable and allowed us to conduct experiments on different crises, including Emilia earthquake in 2012 and Genoa flood in 2014. Finally, some possible functionalities relying on the analysis of multimedia information are introduced

Impromptu crisis mapping to prioritize emergency response

To visualize post-emergency damage, a crisis-mapping system uses readily available semantic annotators, a machine-learning classifier to analyze relevant tweets, and interactive maps to rank extracted situational information. The system was validated against data from two recent disasters in Italy

Nowcasting of Earthquake Consequences Using Big Social Data

Messages posted to social media in the aftermath of a natural disaster have value beyond detecting the event itself. Mining such deliberately dropped digital traces allows a precise situational awareness, to help provide a timely estimate of the disaster’s consequences on the population and infrastructures. Yet, to date, the automatic assessment of damage has received little attention. Here, the authors explore feeding predictive models by tweets conveying on-the-ground social sensors’ observations, to nowcast the perceived intensity of earthquakes

Social Media for the Common Good: the case of EARS

Natural disasters such as earthquakes, tornadoes and floods, are just some of the devastating events that may have catastrophic consequences on wide geographical areas. A quick and targeted response to emergencies greatly contributes in mitigating the losses. In recent years we have witnessed to many situations in which crowds of volunteer citizens have helped emergency responders via the use of widespread social media. Here we argue that technology can help in supporting the population, as well as the decision makers, by introducing tools that enhance the collective awareness level, providing quick yet accurate insights into the unfolding emergency. In this short paper we introduce the EARS system, a social media-based system that supports decision makers during earthquake emergencies in Italy. We discuss the implications and the responsibility related to the usage of such systems by the decision makers. Also, we discuss on how publicly opening systems like EARS to the population might change the problem approach and we introduce relevant opportunities and issues that this solution would imply.

Supporting evidence-based adaptation decision-making in Victoria: a synthesis of climate change adaptation research

This research synthesis provides policy-makers and practitioners with an understanding of the building blocks for effective adaptation decision-making, as evidenced through the NCCARF research program. It synthesised a portfolio of adaptation research for each Australian state and territory and addressing the complex relationships between research and policy development. Each state and territory synthesis report directs users to research relevant identified priorities