scholarly journals Analysis on an Auto Increment Detection System of Chinese Disaster Weibo Text

2021 ◽  
Vol 27 (2) ◽  
pp. 230-252
Author(s):  
Hua Bai ◽  
Hualong Yu ◽  
Guang Yu ◽  
Alvaro Rocha ◽  
Xing Huang
Keyword(s):  
Social Media ◽  
Emergency Management ◽  
Real Time ◽  
Large Scale ◽  
Rapid Development ◽  
Vector Model ◽  
Collection System ◽  
Single Text ◽  
Text Feature ◽  
Disaster Situation

With the rapid development of Internet information technology, the advantages of social media in terms of speed, content, form, and effect of communication are becoming increasingly significant. In recent years, more and more researchers have paid attention to the special value and role of social media tools in disaster information emergency management. Weibo is the most widely used Chinese social media tool. To effectively mine and apply the emergency function of disaster situation microblogs, a disaster situation information discovery and collection system capable of online incremental identification and collection are constructed for massive and disordered disaster microblog text streams. First, based on the deep learning- trained word vector model and a large-scale corpus, an unsupervised short-text feature representation method of disaster situation Weibo information is developed. According to the experimental results of the feature combination test and the training set scale test, the SVM algorithm was selected for disaster microblog information classification, which realized effective identification of disaster situation micro-bloggings. Then, the temporal information similarity and geographic information similarity are used to improve the single text similarity algorithm, and a Chinese disaster event online real-time detection model is constructed. Furthermore, the disaster-affected areas can be achieved in real-time based on the detection results. By crawling and classifying the micro-bloggings from the disaster-affected areas, it is possible to realize the incremental identification and collection of online disaster situation Weibo information. Finally, the empirical analysis of disaster events such as the “Leshan Earthquake” shows that the real- time intelligent identification and collection system for disaster situation Weibo micro-bloggings developed in this paper can obtain large-scale and useful data for disaster emergency management, which proving that this system is effective and efficient.

Analyst-Ready Large Scale Real Time Information Retrieval Tool for E-Governance

2009 ◽  
pp. 268-294
Author(s):  
Eugene Santos Jr. ◽  
Eunice E. Santos ◽  
Hien Nguyen ◽  
Long Pan ◽  
John Korah
Keyword(s):  
Information Retrieval ◽  
Real Time ◽  
Homeland Security ◽  
Large Scale ◽  
Rapid Development ◽  
Empirical Evaluation ◽  
Heterogeneous Data ◽  
Free Text ◽  
Unified Framework ◽  
Sophisticated User

With the proliferation of the Internet and rapid development of information and communication infrastructure, E-governance has become a viable option for effective deployment of government services and programs. Areas of E-governance such as Homeland security and disaster relief have to deal with vast amounts of dynamic heterogeneous data. Providing rapid real-time search capabilities for such databases/sources is a challenge. Intelligent Foraging, Gathering, and Matching (I-FGM) is an established framework developed to assist analysts to find information quickly and effectively by incrementally collecting, processing and matching information nuggets. This framework has previously been used to develop a distributed, free text information retrieval application. In this chapter, we provide a comprehensive solution for the E-GOV analyst by extending the I-FGM framework to image collections and creating a “live” version of I-FGM deployable for real-world use. We present a Content Based Image Retrieval (CBIR) technique that incrementally processes the images, extracts low-level features and map them to higher level concepts. Our empirical evaluation of the algorithm shows that our approach performs competitively compared to some existing approaches in terms of retrieving relevant images while offering the speed advantages of a distributed and incremental process, and unified framework for both text and images. We describe our production level prototype that has a sophisticated user interface which can also deal with multiple queries from multiple users. The interface provides real-time updating of the search results and provides “under the hood” details of I-FGM processes as the queries are being processed.

scholarly journals E2mC: Improving Emergency Management Service Practice through Social Media and Crowdsourcing Analysis in Near Real Time

Sensors ◽  
2017 ◽  
Vol 17 (12) ◽  
pp. 2766 ◽  
Author(s):  
Clemens Havas ◽  
Bernd Resch ◽  
Chiara Francalanci ◽  
Barbara Pernici ◽  
Gabriele Scalia ◽  
...  
Keyword(s):  
Social Media ◽  
Emergency Management ◽  
Real Time ◽  
Management Service

scholarly journals Research and Practice of Agricultural Product Quality Traceability Social Intercourse Application

2021 ◽  
Vol 235 ◽  
pp. 03007
Author(s):  
Yong Yang ◽  
Tongyang Wei ◽  
Min Li
Keyword(s):  
Social Media ◽  
Product Quality ◽  
Real Time ◽  
Social Services ◽  
Rapid Development ◽  
Agricultural Product ◽  
Qr Code ◽  
Research And Practice ◽  
The Social ◽  
Social Intercourse

The social intercourse application of agricultural product quality traceability is facing historical opportunities such as the growth of member agriculture, consumption upgrading and grading, rapid development of social media and social services in the current internet era. This research divides the users of social intercourse application of agricultural product quality traceability into producers, consumers and distributors, and points out that the application should have the features of being open and easy to spread, free of charge and easy to access, interesting and attractive, real-time and interactive and for dissemination, it can be carried out through searching and subscribing the producers, recording and forwarding the content, as well as scanning agricultural product traceability QR code. Based on the above-mentioned methods and ideas, the “Nongdu Easy to Trace” Wechat program based on Wechat platform has been developed and has been widely promoted and used in many provinces in China.

Emergency preparedness collaboration on Twitter

2018 ◽  
Vol 16 (3) ◽  
pp. 191 ◽  
Author(s):  
DeeDee Bennett, PhD
Keyword(s):  
Social Media ◽  
Public Education ◽  
Emergency Management ◽  
Emergency Preparedness ◽  
Large Scale ◽  
Emergency Operations ◽  
Governmental Agencies ◽  
Intergovernmental Collaboration ◽  
Social Media Platforms ◽  
Use Of Social Media

Agency collaboration is an important function in the management of disasters and catastrophes. For effective emergency management, the need for intergovernmental collaboration grows as the scale of the disaster increases. Several researchers have examined the use of social media by emergency management (and other governmental agencies) during large-scale disasters; however, few have examined the use of social media for intergovernmental collaboration. This study explores the use of social media platforms as a means to establish and maintain intergovernmental collaboration for emergency management-related agencies. More salient is the focus on social media during the preparedness and planning stages of emergency management. Using qualitative observational and coding analysis, this study identifies the types of connections made by topic, level of governance, and established affiliation in the local emergency operations plan (LEOP). The findings show that more than 50 percent of the connections made were established on Twitter and not present in the current LEOP. Furthermore, the most popular topic to initiate online connections was related to public education information. The findings from this study can assist emergency management practitioners in developing social media strategies, which incorporate methods to connect with other agencies on Twitter.

scholarly journals Portability of semantic and spatial–temporal machine learning methods to analyse social media for near-real-time disaster monitoring

2021 ◽  
Author(s):  
Clemens Havas ◽  
Bernd Resch
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Social Media ◽  
Emergency Management ◽  
Natural Disasters ◽  
Real Time ◽  
Monitoring Systems ◽  
Management Service ◽  
Machine Learning Methods ◽  
Spatiotemporal Information

AbstractUp-to-date information about an emergency is crucial for effective disaster management. However, severe restrictions impede the creation of spatiotemporal information by current remote sensing-based monitoring systems, especially at the beginning of a disaster. Multiple publications have shown promising results in complementing monitoring systems through spatiotemporal information extracted from social media data. However, various monitoring system criteria, such as near-real-time capabilities or applicability for different disaster types and use cases, have not yet been addressed. This paper presents an improved version of a recently proposed methodology to identify disaster-impacted areas (hot spots and cold spots) by combining semantic and geospatial machine learning methods. The process of identifying impacted areas is automated using semi-supervised topic models for various kinds of natural disasters. We validated the portability of our approach through experiments with multiple natural disasters and disaster types with differing characteristics, whereby one use case served to prove the near-real-time capability of our approach. We demonstrated the validity of the produced information by comparing the results with official authority datasets provided by the United States Geological Survey and the National Hurricane Centre. The validation shows that our approach produces reliable results that match the official authority datasets. Furthermore, the analysis result values are shown and compared to the outputs of the remote sensing-based Copernicus Emergency Management Service. The information derived from different sources can thus be considered to reliably detect disaster-impacted areas that were not detected by the Copernicus Emergency Management Service, particularly in densely populated cities.

scholarly journals Social Bots Detection via Fusing BERT and Graph Convolutional Networks

Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 30
Author(s):  
Qinglang Guo ◽  
Haiyong Xie ◽  
Yangyang Li ◽  
Wen Ma ◽  
Chao Zhang
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Social Robot ◽  
Training Data ◽  
User Privacy ◽  
Transductive Learning ◽  
Convolutional Network ◽  
Online Social Media ◽  
Single Text ◽  
Wide Range

The online social media ecosystem is becoming more and more confused because of more and more fake information and the social media of malicious users’ fake content; at the same time, unspeakable pain has been brought to mankind. Social robot detection uses supervised classification based on artificial feature extraction. However, user privacy is also involved in using these methods, and the hidden feature information is also ignored, such as semi-supervised algorithms with low utilization rates and graph features. In this work, we symmetrically combine BERT and GCN (Graph Convolutional Network, GCN) and propose a novel model that combines large scale pretraining and transductive learning for social robot detection, BGSRD. BGSRD constructs a heterogeneous graph over the dataset and represents Twitter as nodes using BERT representations. Corpus learning via text graph convolution network is a single text graph, which is mainly built for corpus-based on word co-occurrence and document word relationship. BERT and GCN modules can be jointly trained in BGSRD to achieve the best of merit, training data and unlabeled test data can spread label influence through graph convolution and can be carried out in the large-scale pre-training of massive raw data and the transduction learning of joint learning representation. The experiment shows that a better performance can also be achieved by BGSRD on a wide range of social robot detection datasets.

Big Data Analytics in Social Media

2019 ◽  
pp. 107-124
Author(s):  
Janani Balakumar ◽  
Vijayarani Mohan
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
Rapid Development ◽  
Big Data Analytics ◽  
Information Control ◽  
Social Media Platforms ◽  
Current Scenario ◽  
Processing Techniques

The rapid development of online social media is the method of collaboratively produced content material presents new possibilities and challenges to both producers and patrons of knowledge. The term big data refers to large-scale information control and evaluation technologies that exceed the functionality of conventional data processing techniques. In the current scenario, social media has gained amazing attention within the last decade. Accessing social media platforms and websites such as Facebook, Twitter, YouTube, LinkedIn, Instagram, and Google+, web technologies have become more responsible. People are becoming more fascinated about and relying on social media platform for records, news, and opinion of other customers on diverse topics. Hence, these situations produce a large volume of data. The main objective of this chapter is to provide knowledge about big data analytics in social media. A brief overview of big data and social media are discussed. Research challenges in social media are also discussed.

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