Research on the Construction of Typhoon Disaster Chain Based on Chinese Web Corpus (original) (raw)
Related papers
Concurrency and Computation: Practice and Experience, 2018
The southeastern coast of China suffers many typhoon disasters every year, causing huge casualties and economic losses. In addition, collecting statistics on typhoon disaster situations is hard work for the government. At the same time, near-real-time disaster-related information can be obtained on developed social media platforms like Twitter and Weibo. Many cases have proved that citizens are able to organize themselves promptly on the spot, and begin to share disaster information when a disaster strikes, producing massive VGI (volunteered geographic information) about the disaster situation, which could be valuable for disaster response if this VGI could be exploited efficiently and properly. However, this social media information has features such as large quantity, high noise, and unofficial modes of expression that make it difficult to obtain useful information. In order to solve this problem, we first designed a new classification system based on the characteristics of social medial data like Sina Weibo data, and made a microblogging dataset of typhoon damage with according category labels. Secondly, we used this social medial dataset to train the deep learning model, and constructed a typhoon disaster mining model based on a deep learning network, which could automatically extract information about the disaster situation. The model is different from the general classification system in that it automatically selected microblogs related to disasters from a large number of microblog data, and further subdivided them into different types of disasters to facilitate subsequent emergency response and loss estimation. The advantages of the model included a wide application range, high reliability, strong pertinence and fast speed. The research results of this thesis provide a new approach to typhoon disaster assessment in the southeastern coastal areas of China, and provide the necessary information for the authoritative information acquisition channel.
Interdisciplinary Journal of Information, Knowledge, and Management, 2019
Aim/Purpose: Vis-à-vis management of crisis and disaster situations, this paper focuses on important use cases of social media functions, such as information collection & dissemination, disaster event identification & monitoring, collaborative problem-solving mechanism, and decision-making process. With the prolific utilization of disaster-based ontological framework, a strong disambiguation system is realized, which further enhances the searching capabilities of the user request and provides a solution of unambiguous in nature. Background: Even though social media is information-rich, it has created a challenge for deriving a decision in critical crisis-related cases. In order to make the whole process effective and avail quality decision making, sufficiently clear semantics of such information is necessary, which can be supplemented through employing semantic web technologies. Methodology: This paper evolves a disaster ontology-based system availing a framework model for monitorin...
A study on extracting disaster information from tweets
Journal of Global Tourism Research, 2017
In Japan, where natural disasters occurs frequently, obtaining and delivering accurate information promptly when a disaster occurs is essential to minimize damage. Information from traditional mass media contain a number of general information unrelated to disaster, so there are limitations in delivering necessary information to the resident in affected area. On the other hand, Twitter, one of the popular social media, is expected to play an important role during disaster because of its simplicity, promptness and wide propagation. However, because of its huge size of users, there are too many tweets which hinders timely extraction of relevant information. Disaster information is also useful for business travellers and tourists. They are less informed about the area and the challenge is to provide them with accurate information promptly. Our study proposes to establish a system to assist real time understanding of disaster by extracting relevant information efficiently from messages tweeted during two typhoons. First, binary classification is applied to classify and extract disaster tweets from tweets group. By using BNS method, the improvement in accuracy is confirmed. Then clustering is applied to the disaster tweets. The tweets are classified by 15 clusters generated. The result yields F measure of 0.59.
A Simple Disaster-Related Knowledge Base for Intelligent Agents
2020
In this paper, we describe our efforts in establishing a simple knowledge base by building a semantic network composed of concepts and word relationships in the context of disasters in the Philippines. Our primary source of data is a collection of news articles scraped from various Philippine news websites. Using word embeddings, we extract semantically similar and co-occurring words from an initial seed words list. We arrive at an expanded ontology with a total of 450 word assertions. We let experts from the fields of linguistics, disasters, and weather science evaluate our knowledge base and arrived at an agreeability rate of 64%. We then perform a time-based analysis of the assertions to identify important semantic changes captured by the knowledge base such as the (a) trend of roles played by human entities, (b) memberships of human entities, and (c) common association of disaster-related words. The context-specific knowledge base developed from this study can be adapted by inte...
Social media and early warning systems for natural disasters: A case study of Typhoon Etau in Japan
International Journal of Disaster Risk Reduction
Due to the significant improvement of disaster-related information, further reduction of disaster risks requires not only governments to provide more scientifically accurate information but also the public to take appropriate action at the correct times. Meanwhile, there is ongoing work to integrate social media into Early Warning Systems (EWSs), but the ecology of social media information during crises remains poorly understood. This study seeks to understand public responses on social media to EWSs using the case of a 2015 typhoon (the Kanto-Tohoku Heavy Rain) in Japan. Using a corpus of 35 million tweets, computational methods such as topic modeling, and geospatial analysis we find that: 1) emergency warnings are likely to have people be more attentive to the warnings but this does not translate to an increased discussion of actions such as evacuation; 2) the expected shift of public attention (from awareness to preparation and then action) seems to happen on social media. Overall, we show that analysis of social media data can compliment traditional survey-based approaches to understand how the public respond to information from Early Warning Systems.
Proceedings of International Symposium on Grids and Clouds (ISGC) 2014 — PoS(ISGC2014), 2014
With the development of social networks, the mechanism of crowdsourcing is not limited to commercial application. In the event of disaster, information disseminated through social networks, and the mechanism of crowdsourcing thus is established. However, it is worth discussing whether the accuracy of the disaster information through the virtual community can be referred to the government agencies. This research extracted and analyzed the data from "the most serious disaster information in the major disaster information social network" of 2009 Taiwan Morakot typhoon. It also discussed the relevance and trust of crowdsourcing disaster information through extracting, comparing and analyzing. The result indicated that the disaster information extracted from the developed disaster information virtual community of the crowdsourcing mechanism was significant in its trust and relevance. It is recommended that the Federal Emergency Management Agency not only needs to pay attention on the information management of professional disaster prevention and protection along with the development and application of decision support system, bus also establishes virtual communities to manage professional social networks in peacetime. Thus, it can improve the efficiency on dealing with contingency or emergency through the diversification of information channels during calamity. The future studies of this research will focus on organizing the extracted database, introducing a complete evaluation model for trust analysis of disaster crowdsourcing information.
Collecting Typhoon Disaster Information from Twitter Based on Query Expansion
ISPRS International Journal of Geo-Information, 2018
Social media is a popular source of volunteered geographic information owing to its massive real-time data; however, the use of social media data in the context of geospatial analysis is challenging because complex semantic filters are required for the aggregation of geographic messages from the data streams. This article proposes a new query expansion method for social media streams which updates the query keywords periodically by the words extracted from the preceding search results. The proposed method has optimized the trade-off between precision and coverage of geographical messages by factoring in the influences of the keyword number and refresh cycle in the query process, and some improvements on the classic Term Frequency-Inverse Document Frequency (TF-IDF) method for short texts were achieved. Furthermore, a number of filters based upon relevance to the target topic were established and tested. This method was tested on a dataset from Twitter within the geographic extent of Macau in August 2017 during two consecutive typhoon hits. The result supports its effectiveness with a controllable precision and considerable increment of relevant information. Moreover, the query keywords can adjust themselves to the local language environment by discovering new keywords. To conclude, this query expansion method is able to provide a reliable method for social media-based information retrieval.
Deriving Disaster-Related Information from Social Media
2014 KDD Workshop on Learning about Emergencies from Social Information
During real-world crises individuals utilize their online social networks in several ways: to make observations about ongoing events, to request information from their networks and local communities, and to gain situational intelligence about unfolding events. In doing so, users often express highly useful emergency-relevant information, whether in- tentionally or not. Unfortunately, the massive amount of data generated make filtering and processing useful social media artifacts nearly impossible with naive methods, such as simple keyword searches. To address this problem, we offer two contributions: we demonstrate the efficacy of applying a generalizable and meaningful emergency-related on- tology for large-scale reasoning over social media text. Next, we show how topic models trained on a rich data set (here, Tweets collected during Hurricane Sandy) can immediately provide insightful knowledge for disparate disasters.
How Social Media Text Analysis Can Inform Disaster Management
Language Technologies for the Challenges of the Digital Age, 2018
Digitalization and the rise of social media have led disaster management to the insight that modern information technology will have to play a key role in dealing with a crisis. In this context, the paper introduces a NLP software for social media text analysis that has been developed in cooperation with disaster managers in the European project Slandail. The aim is to show how state-of-the-art techniques from text mining and information extraction can be applied to fulfil the requirements of the end-users. By way of example use cases the capacity of the approach will be demonstrated to make available social media as a valuable source of information for disaster management.
Pulling Information from social media in the aftermath of unpredictable disasters
2015 2nd International Conference on Information and Communication Technologies for Disaster Management (ICT-DM), 2015
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.