scholarly journals Extracting Typhoon Disaster Information from VGI Based on Machine Learning

2019 ◽  
Vol 7 (9) ◽  
pp. 318 ◽  
Author(s):  
Yu ◽  
Zhao ◽  
Chin
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Classification System ◽  
High Reliability ◽  
Economic Losses ◽  
Application Range ◽  
Disaster Information ◽  
Disaster Situation ◽  
Typhoon Disaster ◽  
The Government

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.

scholarly journals A Typhoon Shelter Selection and Evacuee Allocation Model: A Case Study of Macao (SAR), China

2020 ◽  
Vol 12 (8) ◽  
pp. 3308 ◽  
Author(s):  
Xiujuan Zhao ◽  
Peng Du ◽  
Jianguo Chen ◽  
Dapeng Yu ◽  
Wei Xu ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Economic Losses ◽  
Distance Constraint ◽  
Special Administrative Region ◽  
Allocation Model ◽  
Shelter Selection ◽  
Typhoon Disaster ◽  
The Government ◽  
Shelter Construction

Typhoon disaster represent one of the most prominent threats to public safety in the Macao Special Administrative Region (SAR) of China and can cause severe economic losses and casualties. Prior to the landing of typhoons, affected people should be evacuated to shelters as soon as possible; this is crucial to prevent injuries and deaths. Various models aim to solve this problem, but the characteristics of disasters and evacuees are often overlooked. This study proposes a model based on the influence of a typhoon and its impact on evacuees. The model’s objective is to minimize the total evacuation distance, taking into account the distance constraint. The model is solved using the spatial analysis tools of Geographic Information Systems (GIS). It is then applied in Macao to solve the evacuation process for Typhoon Mangkhut 2018. The result is an evacuee allocation plan that can help the government organize evacuation efficiently. Furthermore, the number of evacuees allocated to shelters is compared with shelter capacities, which can inform government shelter construction in the future.

CLASSIFICATION OF COVID-19 SYMPTOM FOR CHATBOT USING BERT

2021 ◽  
Vol 10 (2) ◽  
pp. 1065-1069
Author(s):  
H. Park ◽  
G. Moon ◽  
K. Kim
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
News Media ◽  
Data Set ◽  
Present Information ◽  
Disaster Situation

Coronavirus disease (COVID-19) is a significant disaster worldwide from December 2019 to the present. Information on the COVID-19 is grasped through news media or social media, and researchers are conducting various research. This is because we are trying to shorten the time to be aware of the COVID-19 disaster situation. In this paper, we build a chatbot so that it can be used in emergencies using the COVID-19 data set and investigate how the analysis is changing the situation with deep learning.

PSYCHOSOCIAL STRESS CONTAGION OF COVID-19: ISSUES AND INTERVENTION CHANNELS

Ensemble ◽  
2021 ◽  
Vol SP-1 (1) ◽  
pp. 44-53
Author(s):  
DR. SOURAV MADHUR DEY ◽  
Keyword(s):  
Social Media ◽  
Substance Dependence ◽  
Virus Disease ◽  
Financial Burden ◽  
Psychosocial Impact ◽  
Economic Losses ◽  
Post Traumatic Stress ◽  
Crisis Prevention ◽  
The Government ◽  
Frontline Healthcare Workers

Along with its elevated contamination and fatality rates, the 2019 Corona Virus Disease (COVID-19) has caused worldwide psychosocial impact by causing mass hysteria, financial burden and economic losses. Mass fear of COVID-19, termed as “coronaphobia”, has generated a glut of psychiatric exposition across diverse strata of the society. So, this review has been set about to portray psychosocial impact of COVID-19. Disease itself conglomerated by mandatory quarantine to counter COVID-19 applied by countrywide lockdowns can generate acute panic, angst, obsessive behaviors, stock piling, paranoia and depression, and post-traumatic stress disorder in due course. These have been stirred up by an “infodemic” spread via various platforms of social media. Outbreak of racism, stigmatization, and xenophobia against particular communities are also being extensively reported. Nonetheless, frontline healthcare workers are at elevated risk of acquiring the disease as well as experiencing detrimental psychological outcomes in shape of exhaustion, worry, trepidation of transmitting infection, augmented substance-dependence and PTSD. The present article attempts to investigate these areas of contagion and suggest intervention dynamics to address the issue and therefore observes that psychosocial crisis prevention and intervention models should be urgently devised by the government, health care personnel and other stakeholders. Appropriate application of internet services, technology and social media to control both pandemic and ‘infodemic’ require to be initiated. Psychosocial alertness by setting up mental organizations particular for future pandemics is indeed crucial.

scholarly journals Using Social Media to Mine and Analyze Public Sentiment during a Disaster: A Case Study of the 2018 Shouguang City Flood in China

2019 ◽  
Vol 8 (4) ◽  
pp. 185 ◽  
Author(s):  
Xuehua Han ◽  
Juanle Wang
Keyword(s):  
Social Media ◽  
Spatial Distribution ◽  
Latent Dirichlet Allocation ◽  
Developmental Stages ◽  
Classification Model ◽  
Economic Losses ◽  
Public Servants ◽  
Public Sentiment ◽  
South Central ◽  
The Government

Social media has been applied to all natural disaster risk-reduction phases, including pre-warning, response, and recovery. However, using it to accurately acquire and reveal public sentiment during a disaster still presents a significant challenge. To explore public sentiment in depth during a disaster, this study analyzed Sina-Weibo (Weibo) texts in terms of space, time, and content related to the 2018 Shouguang flood, which caused casualties and economic losses, arousing widespread public concern in China. The temporal changes within six-hour intervals and spatial distribution on sub-district and city levels of flood-related Weibo were analyzed. Based on the Latent Dirichlet Allocation (LDA) model and the Random Forest (RF) algorithm, a topic extraction and classification model was built to hierarchically identify six flood-relevant topics and nine types of public sentiment responses in Weibo texts. The majority of Weibo texts about the Shouguang flood were related to “public sentiment”, among which “questioning the government and media” was the most commonly expressed. The Weibo text numbers varied over time for different topics and sentiments that corresponded to the different developmental stages of the flood. On a sub-district level, the spatial distribution of flood-relevant Weibo was mainly concentrated in high population areas in the south-central and eastern parts of Shouguang, near the river and the downtown area. At the city level, the Weibo texts were mainly distributed in Beijing and cities in the Shandong Province, centering in Weifang City. The results indicated that the classification model developed in this study was accurate and viable for analyzing social media texts during a disaster. The findings can be used to help researchers, public servants, and officials to better understand public sentiments towards disaster events, to accelerate disaster responses, and to support post-disaster management.

scholarly journals VRBagged-Net: Ensemble Based Deep Learning Model for Disaster Event Classification

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1411
Author(s):  
Muhammad Hanif ◽  
Muhammad Atif Tahir ◽  
Muhammad Rafi
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Social Networking Sites ◽  
Human Life ◽  
Slight Modification ◽  
Economic Losses ◽  
Average Precision ◽  
Dry Land ◽  
Classification Framework ◽  
Disaster Event

A flood is an overflow of water that swamps dry land. The gravest effects of flooding are the loss of human life and economic losses. An early warning of these events can be very effective in minimizing the losses. Social media websites such as Twitter and Facebook are quite effective in the efficient dissemination of information pertinent to any emergency. Users on these social networking sites share both textual and rich content images and videos. The Multimedia Evaluation Benchmark (MediaEval) offers challenges in the form of shared tasks to develop and evaluate new algorithms, approaches and technologies for explorations and exploitations of multimedia in decision making for real time problems. Since 2015, the MediaEval has been running a shared task of predicting several aspects of flooding and through these shared tasks, many improvements have been observed. In this paper, the classification framework VRBagged-Net is proposed and implemented for flood classification. The framework utilizes the deep learning models Visual Geometry Group (VGG) and Residual Network (ResNet), along with the technique of Bootstrap aggregating (Bagging). Various disaster-based datasets were selected for the validation of the VRBagged-Net framework. All the datasets belong to the MediaEval Benchmark Workshop, this includes Disaster Image Retrieval from Social Media (DIRSM), Flood Classification for Social Multimedia (FCSM) and Image based News Topic Disambiguation (INTD). VRBagged-Net performed encouraging well in all these datasets with slightly different but relevant tasks. It produces Mean Average Precision at different levels of 98.12, and Average Precision at 480 of 93.64 on DIRSM. On the FCSM dataset, it produces an F1 score of 90.58. Moreover, the framework has been applied on the dataset of Image-Based News Topic Disambiguation (INTD), and exceeds the previous best result by producing an F1 evaluation of 93.76. The VRBagged-Net with a slight modification also ranked first in the flood-related Multimedia Task at the MediaEval Workshop 2020.

EFFECT OF DIGITAL COMMUNITY TOWARDS DEMOCRACY PROCESS: DIGITAL PARTISIPATION ANALYSIS IN INDONESIA

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Alih Aji Nugroho
Keyword(s):  
Public Policy ◽  
Social Media ◽  
Political Participation ◽  
Public Space ◽  
Rapid Development ◽  
Critical Mass ◽  
Digital Society ◽  
Digital World ◽  
Digital Community ◽  
The Government

The world is entering a new phase of the digital era, including Indonesia. The unification of the real world and cyberspace is a sign, where the conditions of both can influence each other (Hyung Jun, 2018). The patterns of behavior and public relations in the virtual universe gave rise to new social interactions called the Digital Society. One part of Global Megatrends has also influenced public policy in Indonesia in recent years. Critical mass previously carried out conventionally is now a virtual movement. War of hashtags, petitions, and digital community comments are new tools and strategies for influencing policy. This paper attempts to analyze the extent of digital society's influence on public policy in Indonesia. As well as what public policy models are needed. Methodology used in this analysis is qualitative descriptive. Data collection through literature studies by critical mass digital recognition in Indonesia and trying to find a relationship between political participation through social media and democracy. By processing the pro and contra views regarding the selection of social media as a level of participation, this paper finds that there are overlapping interests that have the potential to distort the articulation of freedom of opinion and participation. - which is characteristic of a democratic state. The result is the rapid development of digital society which greatly influences the public policy process. Digital society imagines being able to participate formally in influencing policy in Indonesia. The democracy that developed in the digital society is cyberdemocracy. Public space in the digital world must be guaranteed security and its impact on the policies that will be determined. The recommendation given to the government is that a cyber data analyst is needed to oversee the issues that are developing in the digital world. Regulations related to the security of digital public spaces must be maximized. The government maximizes cooperation with related stakeholders.Keywords: Digital Society; Democracy; Public policy; Political Participation

The Use of Social Media by the Botswana Government and Governance of Liquid Communication

Mousaion ◽  
2019 ◽  
Vol 37 (1) ◽  
Author(s):  
Tshepho Lydia Mosweu
Keyword(s):  
Social Media ◽  
Customer Service ◽  
Community Involvement ◽  
Media Content ◽  
Service Access ◽  
Communication Tool ◽  
African Countries ◽  
The World ◽  
The Government ◽  
Use Of Social Media

Social media as a communication tool has enabled governments around the world to interact with citizens for customer service, access to information and to direct community involvement needs. The trends around the world show recognition by governments that social media content may constitute records and should be managed accordingly. The literature shows that governments and organisations in other countries, particularly in Europe, have social media policies and strategies to guide the management of social media content, but there is less evidence among African countries. Thus the purpose of this paper is to examine the extent of usage of social media by the Botswana government in order to determine the necessity for the governance of liquid communication. Liquid communication here refers to the type of communication that goes easily back and forth between participants involved through social media. The ARMA principle of availability requires that where there is information governance, an organisation shall maintain its information assets in a manner that ensures their timely, efficient and accurate retrieval. The study adopted a qualitative case study approach where data were collected through documentary reviews and interviews among purposively selected employees of the Botswana government. This study revealed that the Botswana government has been actively using social media platforms to interact with its citizens since 2011 for increased access, usage and awareness of services offered by the government. Nonetheless, the study revealed that the government had no official documentation on the use of social media, and policies and strategies that dealt with the governance of liquid communication. This study recommends the governance of liquid communication to ensure timely, efficient and accurate retrieval when needed for business purposes.

Utilizing Twitter Data Analysis and Deep Learning to Identify Drug Use (Preprint)

2019 ◽  
Author(s):  
Joseph Tassone ◽  
Peizhi Yan ◽  
Mackenzie Simpson ◽  
Chetan Mendhe ◽  
Vijay Mago ◽  
...  
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Deep Learning ◽  
Decision Tree ◽  
Semantic Meaning ◽  
Predictive Capability ◽  
Logistic Regression Models ◽  
Twitter Data ◽  
Data Points ◽  
Positive Classification

BACKGROUND The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. OBJECTIVE Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined. METHODS Twitter social media tweets and attribute data were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset 3,696,150 rows. The predictive classification power of multiple methods was compared including regression, decision trees, and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets. RESULTS The logistic regression and decision tree models utilized 12,142 data points for training and 1041 data points for testing. The results calculated from the logistic regression models respectively displayed an accuracy of 54.56% and 57.44%, and an AUC of 0.58. While an improvement, the decision tree concluded with an accuracy of 63.40% and an AUC of 0.68. All these values implied a low predictive capability with little to no discrimination. Conversely, the CNN-based classifiers presented a heavy improvement, between the two models tested. The first was trained with 2,661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as “smoke”, “cocaine”, and “marijuana” triggering a drug-positive classification. CONCLUSIONS Predictive analysis without a CNN is limited and possibly fruitless. Attribute-based models presented little predictive capability and were not suitable for analyzing this type of data. The semantic meaning of the tweets needed to be utilized, giving the CNN-based classifier an advantage over other solutions. Additionally, commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of this system. Lastly, the synthetically generated set provided increased scores, improving the predictive capability. CLINICALTRIAL None

Lawyers as Activists

2018 ◽  
Author(s):  
Michael C. Dorf ◽  
Michael S. Chu
Keyword(s):  
United States ◽  
Social Media ◽  
Social Change ◽  
Civil Rights ◽  
Rule Of Law ◽  
The United States ◽  
The Rule Of Law ◽  
Court Action ◽  
The Government ◽  
Trump Administration

Lawyers played a key role in challenging the Trump administration’s Travel Ban on entry into the United States of nationals from various majority-Muslim nations. Responding to calls from nongovernmental organizations (NGOs), which were amplified by social media, lawyers responded to the Travel Ban’s chaotic rollout by providing assistance to foreign travelers at airports. Their efforts led to initial court victories, which in turn led the government to soften the Ban somewhat in two superseding executive actions. The lawyers’ work also contributed to the broader resistance to the Trump administration by dramatizing its bigotry, callousness, cruelty, and lawlessness. The efficacy of the lawyers’ resistance to the Travel Ban shows that, contrary to strong claims about the limits of court action, litigation can promote social change. General lessons about lawyer activism in ordinary times are difficult to draw, however, because of the extraordinary threat Trump poses to civil rights and the rule of law.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

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