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 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 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.

Techniques of Viral Marketing

2019 ◽  
Vol 118 (6) ◽  
pp. 97-99
Author(s):  
Arockia Jeyasheela A ◽  
Dr.S. Chandramohan
Keyword(s):  
Social Media ◽  
Real Time ◽  
Word Of Mouth ◽  
Viral Marketing ◽  
Consumer Perception ◽  
Key Word ◽  
The Right ◽  
A Company

This study is discussed about the viral marketing. It is a one of the key success of marketing. This paper gave the techniques of viral marketing. It can be delivered word of mouth. It can be created by both the representatives of a company and consumer (individuals or communities). The right viral message with go to right consumer to the right time. Viral marketing is easy to attract the consumer. It is most important advertising to consumer. It involves consumer perception, organization contribution, blogs, SMO (Social Media Optimize), SEO (Social Engine Optimize). Principles of viral marketing are social profile gathering, Proximity Market, Real time Key word density.

Holonic System for Real-Time Emergency Management in Buildings

2019 ◽  
Author(s):  
Berardo Naticchia ◽  
Leonardo Messi ◽  
Massimiliano Pirani ◽  
Andrea Bonci ◽  
Alessandro Carbonari ◽  
...  
Keyword(s):  
Emergency Management ◽  
Real Time ◽  
Holonic System

Social Listening to Create Bespoke Customer Experiences: Best Practices for Hospitality Operators

2021 ◽  
pp. 193896552199308
Author(s):  
Kathryn A. LaTour ◽  
Ana Brant
Keyword(s):  
Social Media ◽  
Best Practices ◽  
Real Time ◽  
Brand Image ◽  
Customer Experience ◽  
Current Experience ◽  
Social Listening

Most hospitality operators use social media in their communications as a means to communicate brand image and provide information to customers. Our focus is on a two-way exchange whereby a customer’s social posting is reacted to in real-time by the provider to enhance the customer’s current experience. Using social media in this way is new, and the provider needs to carefully balance privacy and personalization. We describe the process by which the Dorchester Collection Customer Experience (CX) Team approached its social listening program and share lessons to identify best practices for hospitality operators wanting to delight their customers through insights gained from social listening.

scholarly journals DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suppawong Tuarob ◽  
Poom Wettayakorn ◽  
Ponpat Phetchai ◽  
Siripong Traivijitkhun ◽  
Sunghoon Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Collection ◽  
Stock Market ◽  
Real Time ◽  
Stock Prices ◽  
Contextual Information ◽  
Online News ◽  
Stock Prediction ◽  
Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

scholarly journals Real-time clinician text feeds from electronic health records

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
James T. H. Teo ◽  
Vlad Dinu ◽  
William Bernal ◽  
Phil Davidson ◽  
Vitaliy Oliynyk ◽  
...  
Keyword(s):  
Social Media ◽  
Electronic Health Records ◽  
Real Time ◽  
Capacity Planning ◽  
Low Cost ◽  
Disease Outbreaks ◽  
Record System ◽  
Health Records ◽  
Keywords And Phrases ◽  
Electronic Health

AbstractAnalyses of search engine and social media feeds have been attempted for infectious disease outbreaks, but have been found to be susceptible to artefactual distortions from health scares or keyword spamming in social media or the public internet. We describe an approach using real-time aggregation of keywords and phrases of freetext from real-time clinician-generated documentation in electronic health records to produce a customisable real-time viral pneumonia signal providing up to 4 days warning for secondary care capacity planning. This low-cost approach is open-source, is locally customisable, is not dependent on any specific electronic health record system and can provide an ensemble of signals if deployed at multiple organisational scales.

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