Construction of a disaster-support dynamic knowledge chatbot

This dissertation is aimed at devising a disaster-support chatbot system with the capacity to enhance citizens and first responders’ resilience in disaster scenarios, by gathering and processing information from crowd-sensing sources, and informing its users with relevant knowledge about detected disasters, and how to deal with them. This system is composed of two artifacts that interact via a mediator graph-structured knowledge base. Our first artifact is a crowd-sourced disaster-related knowledge extraction system, which uses social media as a means to exploit humans behaving as sensors. It consists in a pipeline of natural language processing (NLP) tools, and a mixture of convolutional neural networks (CNNs) and lexicon-based models for classifying and extracting disasters. It then outputs the extracted information to the knowledge graph (KG), for presenting connected insights. The second artifact, the disaster-support chatbot, uses a state-of-the-art Dual Intent Entity Transformer (DIET) architecture to classify user intents, and makes use of several dialogue policies for managing user conversations, as well as storing relevant information to be used in further dialogue turns. To generate responses, the chatbot uses local and official disaster-related knowledge, and infers the knowledge graph for dynamic knowledge extracted by the first artifact. According to the achieved results, our devised system is on par with the state-of-the- art on Disaster Extraction systems. Both artifacts have also been validated by field specialists, who have considered them to be valuable assets in disaster-management.Esta dissertação visa a conceção de um sistema de chatbot de apoio a desastres, com a capacidade de aumentar a resiliência dos cidadãos e socorristas nestes cenários, através da recolha e processamento de informação de fontes de crowdsensing, e informar os seus utilizadores com conhecimentos relevantes sobre os desastres detetados, e como lidar com eles. Este sistema é composto por dois artefactos que interagem através de uma base de conhecimento baseada em grafos. O primeiro artefacto é um sistema de extração de conhecimento relacionado com desastres, que utiliza redes sociais como forma de explorar o conceito humans as sensors. Este artefacto consiste numa sequência de ferramentas de processamento de língua natural, e uma mistura de redes neuronais convolucionais e modelos baseados em léxicos, para classificar e extrair informação sobre desastres. A informação extraída é então passada para o grafo de conhecimento. O segundo artefacto, o chatbot de apoio a desastres, utiliza uma arquitetura Dual Intent Entity Transformer (DIET) para classificar as intenções dos utilizadores, e faz uso de várias políticas de diálogo para gerir as conversas, bem como armazenar informação chave. Para gerar respostas, o chatbot utiliza conhecimento local relacionado com desastres, e infere o grafo de conhecimento para extrair o conhecimento inserido pelo primeiro artefacto. De acordo com os resultados alcançados, o nosso sistema está ao nível do estado da arte em sistemas de extração de informação sobre desastres. Ambos os artefactos foram também validados por especialistas da área, e considerados um contributo significativo na gestão de desastres

DisKnow: a social-driven disaster support knowledge extraction system

This research is aimed at creating and presenting DisKnow, a data extraction system with the capability of filtering and abstracting tweets, to improve community resilience and decision-making in disaster scenarios. Nowadays most people act as human sensors, exposing detailed information regarding occurring disasters, in social media. Through a pipeline of natural language processing (NLP) tools for text processing, convolutional neural networks (CNNs) for classifying and extracting disasters, and knowledge graphs (KG) for presenting connected insights, it is possible to generate real-time visual information about such disasters and affected stakeholders, to better the crisis management process, by disseminating such information to both relevant authorities and population alike. DisKnow has proved to be on par with the state-of-the-art Disaster Extraction systems, and it contributes with a way to easily manage and present such happenings.info:eu-repo/semantics/publishedVersio

A linguistically-driven methodology for detecting impending and unfolding emergencies from social media messages

Natural disasters have demonstrated the crucial role of social media before, during and after emergencies (Haddow & Haddow 2013). Within our EU project Sland \ub4 ail, we aim to ethically improve \ub4 the use of social media in enhancing the response of disaster-related agen-cies. To this end, we have collected corpora of social and formal media to study newsroom communication of emergency management organisations in English and Italian. Currently, emergency management agencies in English-speaking countries use social media in different measure and different degrees, whereas Italian National Protezione Civile only uses Twitter at the moment. Our method is developed with a view to identifying communicative strategies and detecting sentiment in order to distinguish warnings from actual disasters and major from minor disasters. Our linguistic analysis uses humans to classify alert/warning messages or emer-gency response and mitigation ones based on the terminology used and the sentiment expressed. Results of linguistic analysis are then used to train an application by tagging messages and detecting disaster- and/or emergency-related terminology and emotive language to simulate human rating and forward information to an emergency management system

Impact Estimation of Emergency Events Using Social Media Streams

In recent years, Social Media platforms have attracted millions of users, becoming a primary communication channel. They offer the possibility to massively ingest and instantly share big volumes of user-generated content before, during, and after emergency events. Being able to accurately quantify the impact of such hazardous events could greatly help all organizations involved in the emergency management cycle to adequately plan the required recovery operations. In this work, we propose a novel Natural Language Processing approach built on rule-based algorithms able to estimate, from tweets posted during natural hazards, the impact of emergency events in terms of affected population and infrastructures. We implement our approach in an operational environment and present its validation on a publicly released dataset of more than 1.4K manually annotated tweets, showing an overall weighted F1 score of 0.77

Real-Time Social Network Data Mining For Predicting The Path For A Disaster

Traditional communication channels like news channels are not able to provide spontaneous information about disasters unlike social networks namely, Twitter. The present research work proposes a framework by mining real-time disaster data from Twitter to predict the path a disaster like a tornado will take. The users of Twitter act as the sensors which provide useful information about the disaster by posting first-hand experience, warnings or location of a disaster. The steps involved in the framework are – data collection, data preprocessing, geo-locating the tweets, data filtering and extrapolation of the disaster curve for prediction of susceptible locations. The framework is validated by analyzing the past events. This framework has the potential to be developed into a full-fledged system to predict and warn people about disasters. The warnings can be sent to news channels or broadcasted for pro-active action

TriggerCit: Early Flood Alerting using Twitter and Geolocation - A Comparison with Alternative Sources

Rapid impact assessment in the immediate aftermath of a natural disaster is essential to provide adequate information to international organisations, local authorities, and first responders. Social media can support emergency response with evidence-based content posted by citizens and organisations during ongoing events. In the paper, we propose TriggerCit: an early flood alerting tool with a multilanguage approach focused on timeliness and geolocation. The paper focuses on assessing the reliability of the approach as a triggering system, comparing it with alternative sources for alerts, and evaluating the quality and amount of complementary information gathered. Geolocated visual evidence extracted from Twitter by TriggerCit was analysed in two case studies on floods in Thailand and Nepal in 2021.Comment: 12 pages Keywords Social Media, Disaster management, Early Alertin