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.

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 To Enhance the Impact of Deep Learning-Based Algorithms in Determining the Behavior of an Individual based on Communication on Social Media

2019 ◽  
Vol 8 (12) ◽  
pp. 4433-4435
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Opinion Mining ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Social Media Text ◽  
The Impact

In this digitized world, the Internet has become a prominent source to glean various kinds of information. In today’s scenario, people prefer virtual reality instead of one to one communication. The Majority of the population prefers social networking sites to voice themselves through posts, blogs, comments, likes, dislikes. Their sentiments can be found/traced using opinion mining or Sentiment analysis. Sentiment analysis of social media text is a useful technique for identifying peoples’ positive, negative or neutral emotions/sentiments/opinions. Sentiment analysis has gained special attention by researchers from last few years. Traditionally many machine learning algorithms were used to implement it like navie bays, Support Vector Machine and many more. But to overcome the drawbacks of ML in terms of complex classification algorithms different deep learning-based algorithms are introduced like CNN, RNN, and HNN. In this paper, we have studied different deep learning algorithms and intended to propose a deep learning-based model to analyze the behavior of an individual using social media text. Results given by the proposed model can utilize in a range of different fields like business, education, industry, politics, psychology, security, etc.

scholarly journals Rumour Detection Models & Tools for Social Networking Sites

2019 ◽  
Vol 9 (2) ◽  
pp. 3291-3296
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Networking ◽  
Social Networking Sites ◽  
Early Stage ◽  
Economic Losses ◽  
Learning Approaches ◽  
The People ◽  
Social Media Platforms ◽  
Fuzzy Logic Technique

Efficient utilization of social networking sites (SNS) had reduced communication delays, at the same time increased rumour messages. Subsequently, mischievous people started sharing of rumours via social networking sites for gaining personal benefits. This falsified information (i.e., rumour) creates misconception among the people of society influencing socio-economic losses by disrupting the routine businesses of private and government sectors. Communication of rumour information requires rigorous surveillance, before they become viral through social media platforms. Detecting these rumour words in an early stage from messaging applications needs to be predicted using robust Rumour Detection Models (RDM) and succinct tools. RDM are effectively used in detecting the rumours from social media platforms (Twitter, Linkedln, Instagram, WhatsApp, Weibo sena and others) with the help of bag of words and machine learning approaches to a limited extent. RDM fails in detecting the emerging rumours that contains linguistic words of a specific language during the chatting session. This survey compares the various RDM strategies and Tools that were proposed earlier for identifying the rumour words in social media platforms. It is found that many of earlier RDM make use of Deep learning approaches, Machine learning, Artificial Intelligence, Fuzzy logic technique, Graph theory and Data mining techniques. Finally, an improved RDM model is proposed in Figure 2, efficiency of this proposed RDM models is improved by embedding of Pre-defined rumour rules, WordNet Ontology and NLP/machine learning approach giving the precision rate of 83.33% when compared with other state-of-art systems.

scholarly journals A Novelty of Impact of Social Media from the Legal Perspective

2018 ◽  
Vol 8 (1) ◽  
pp. 28-33
Author(s):  
Syarifah Lisa Andriati
Keyword(s):  
Social Media ◽  
Mass Media ◽  
New Media ◽  
Social Networking ◽  
Social Networking Sites ◽  
Human Life ◽  
Internet Technology ◽  
Legal Problems ◽  
Legal Perspective ◽  
Information And Communication

Along with the development of science and technology, human life is growing dynamically, especially in the field of information and communication. The Cyber Era has produced internet technology and brought a new phenomenon in the area of mass media which also creates new media that is commonly called social media or social networking. Social media is like a two-edged knife. If used wisely, selectively and responsibly, various social networking sites can be useful, but if used irresponsibly, social media can have unfavorable consequences, and even cause legal problems.

scholarly journals Sentiment Analysis about Smart Phones Using Twitter Corpus by Deep Learning Approach

2019 ◽  
Vol 8 (S2) ◽  
pp. 39-45
Author(s):  
R. Pavithra ◽  
A. R. Mohamed Shanavas
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Deep Learning ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Convolutional Neural Networks ◽  
Social Networking Sites ◽  
Smart Phones ◽  
Learning Approach ◽  
Human Beings

Micro blogging websites are nothing but social media website to which user makes quick and frequent posts. Twitter is one of the well-known micro blog sites which offer the space for person which can read and put up messages that are 148 characters in duration. Twitter messages also are referred to as Tweets. And will use these tweets as raw facts. Then use a way that automatically extracts tweets into advantageous, bad or neutral sentiments. By the usage of the sentiment evaluation the consumer can recognize the feedback about the product or services before make a purchase. The organization can use sentiment evaluation to know the opinion of clients about their products, so can examine customer pleasure and in line with that they could improve their product. Now-a-days social networking sites are at the growth, so massive amount of data is generated. Millions of human beings are sharing their views each day on micro blogging sites, since it includes short and simple expressions. In this thesis, able to discuss approximately a paradigm to extract the sentiment from a famous micro running a blog carrier, Twitter, wherein customers submit their opinions for the whole thing. And can use the deep mastering algorithm to categories the twitters which incorporates Convolutional Neural Networks. The experimental end result is presented to demonstrate the use and effectiveness of the proposed system.

scholarly journals COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques

2022 ◽  
Vol 9 ◽  
Author(s):  
Zunera Jalil ◽  
Ahmed Abbasi ◽  
Abdul Rehman Javed ◽  
Muhammad Badruddin Khan ◽  
Mozaherul Hoque Abul Hasanat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Daily Lives ◽  
The Public ◽  
Learning Techniques ◽  
Social Media Platforms ◽  
Twitter Users

The coronavirus disease 2019 (COVID-19) pandemic has influenced the everyday life of people around the globe. In general and during lockdown phases, people worldwide use social media network to state their viewpoints and general feelings concerning the pandemic that has hampered their daily lives. Twitter is one of the most commonly used social media platforms, and it showed a massive increase in tweets related to coronavirus, including positive, negative, and neutral tweets, in a minimal period. The researchers move toward the sentiment analysis and analyze the various emotions of the public toward COVID-19 due to the diverse nature of tweets. Meanwhile, people have expressed their feelings regarding the vaccinations' safety and effectiveness on social networking sites such as Twitter. As an advanced step, in this paper, our proposed approach analyzes COVID-19 by focusing on Twitter users who share their opinions on this social media networking site. The proposed approach analyzes collected tweets' sentiments for sentiment classification using various feature sets and classifiers. The early detection of COVID-19 sentiments from collected tweets allow for a better understanding and handling of the pandemic. Tweets are categorized into positive, negative, and neutral sentiment classes. We evaluate the performance of machine learning (ML) and deep learning (DL) classifiers using evaluation metrics (i.e., accuracy, precision, recall, and F1-score). Experiments prove that the proposed approach provides better accuracy of 96.66, 95.22, 94.33, and 93.88% for COVISenti, COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C, respectively, compared to all other methods used in this study as well as compared to the existing approaches and traditional ML and DL algorithms.

A Review on Natural Disaster Detection in Social Media and Satellite Imagery Using Machine Learning and Deep Learning

2021 ◽  
Author(s):  
Swapandeep Kaur ◽  
Sheifali Gupta ◽  
Swati Singh ◽  
Tanvi Arora
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Natural Disaster ◽  
Satellite Imagery ◽  
Human Life ◽  
High Volume ◽  
Volume Data ◽  
The Social ◽  
Social Media Platforms

A disaster is a devastating incident that causes a serious disruption of the functions of a community. It leads to loss of human life and environmental and financial losses. Natural disasters cause damage and privation that could last for months and even years. Immediate steps need to be taken and social media platforms like Twitter help to provide relief to the affected public. However, it is difficult to analyze high-volume data obtained from social media posts. Therefore, the efficiency and accuracy of useful data extracted from the enormous posts related to disaster are low. Satellite imagery is gaining popularity because of its ability to cover large temporal and spatial areas. But, both the social media and satellite imagery require the use of automated methods to avoid the errors caused by humans. Deep learning and machine learning have become extremely popular for text and image classification tasks. In this paper, a review has been done on natural disaster detection through information obtained from social media and satellite images using deep learning and machine learning.

scholarly journals Social Networking Sites’ Gag Order: Commercial Content Moderation’s Adverse Implications for Fundamental Sexual Rights and Wellbeing

2021 ◽  
Vol 7 (2) ◽  
pp. 205630512110249
Author(s):  
Sanna Spišák ◽  
Elina Pirjatanniemi ◽  
Tommi Paalanen ◽  
Susanna Paasonen ◽  
Maria Vihlman
Keyword(s):  
Social Media ◽  
Human Rights ◽  
Social Networking Sites ◽  
Freedom Of Expression ◽  
Human Life ◽  
Media Content ◽  
Sexual Content ◽  
Sexual Display ◽  
Social Media Platforms ◽  
Sexual Talk

This article critically investigates the reasoning behind social media content policies and opaque data politics operations regarding sexual visual social media practices and sexual talk, asking what is at stake when social media giants govern sexual sociability on an international scale. Focusing on Facebook, in particular, this article proposes an alternative perspective for handling various expressions of sexuality in social media platforms by exploring the wide-ranging ramifications of community standards and commercial content moderation policies based on them. Given that sexuality is an integral part of human life and as such protected by fundamental human rights, we endorse the freedom of expression as an essential legal and ethical tool for supporting wellbeing, visibility, and non-discrimination. We suggest that social media content policies should be guided by the interpretive lens of fundamental human rights. Furthermore, we propose that social media content policies inclusive of the option to express consent to access sexual content are more ethical and just than those structurally erasing nudity and sexual display.

The role of social networking sites in the revolution of 25 January 2011 Egyptian

2019 ◽  
Vol 2 (3) ◽  
pp. 1
Author(s):  
Qassim Alwan Saeed ◽  
Khairallah Sabhan Abdullah Al-Jubouri
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Social Networking Sites ◽  
Arab Spring ◽  
The World ◽  
The Revolution

Social media sites have recently gain an essential importance in the contemporary societies، actually، these sites isn't simply a personal or social tool of communication among people، its role had been expanded to become "political"، words such as "Facebook، Twitter and YouTube" are common words in political fields of our modern days since the uprisings of Arab spring، which sometimes called (Facebook revolutions) as a result of the major impact of these sites in broadcasting process of the revolution message over the world by organize and manage the revolution progresses in spite of the governmental ascendance and official prohibition.

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