Prioritising climate change actions post COVID-19 amongst university students, a Q methodology perspective in the United Arab Emirates

The COVID-19 pandemic caused strict regulations to lower transmission rates. Industries were shut down, people were in lockdown, and travel was curtailed. Restrictions were in effect for an enough period for people's behaviour to change. For example, online meetings rather than needing to travel. This opens the possibility for alterations to the perception that it is possible to commit to effective climate change actions. A Q methodology study was conducted to analyse how 33 university environmental students across the United Arab Emirates perceive the importance of prioritising climate change actions post-pandemic. Statistical analysis yielded four discourses. The first emphasises the need to learn lessons about climate sustainability and sustain them post-pandemic. The second, more pessimistic but advocates preventing a return to pre-pandemic norms by implementing post-pandemic climate change regulations. The third expects economic recovery to take priority over reducing emissions. The fourth raises opportunities and challenges for environmental sustainability post-COVID-19.

Visual Analysis of Social Robots in Twitter on China Agenda Setting

With the development and progress of intelligent algorithms, more and more social robots are used to interfere with the information transmission and direction of international public opinion. This paper takes the agenda of COVID-19 in Twitter as the breakthrough point, and through the methods of web crawler, Twitter robot detection, data processing and analysis, aims at the agenda setting of social robots for China issues, that is, to carry out data visualization analysis for the stigmatized China image. Through case analysis, concrete and operable countermeasures for building the international communication system of China image were provided. © 2022 IEEE.

Research on Key technologies of network public opinion warning based on improved Stacking algorithm

Online public opinion warning for emergencies can help people understand the real situation, avoid panic, timely remind people not to go to high-risk areas, and help the government to carry out epidemic work.In this paper, key technologies of network public opinion warning were studied based on improved Stacking algorithm. COVID-19, herpangina, hand, foot and mouth, varicella and several emergency outbreaks were selected as public opinion research objects, and rough set was used to screen indicators and determine the final warning indicators.Finally, the warning model was established by the 50% fold Stacking algorithm, and the training accuracy and prediction accuracy experiments were carried out.According to the empirical study, the prediction accuracy of 50% Stacking is good, and the early warning model is practical and robust.This study has strong practicability in the early warning of the online public opinion of the sudden epidemic. © 2023 SPIE.

COVID-19 and Distance Education: Evaluation in the Context of Twenty-First Century Skills

In this study, we aimed to investigate the prospective primary school teachers' opinions about their experiences in distance education within the scope of twenty-first century skills during COVID-19 pandemic. The phenomenological research method was used for the purpose of enlightening this specific context. The study group involved 16 prospective primary school teachers. Data were collected through semi-structured interviews. The credibility of the data were provided by obtaining the consent of the participants and by comparing the consistency of codes and themes created by experts in accordance with the twenty-first century skills. The key findings were: (1) no opinion is expressed on information and media literacy;also, participants were not aware of the importance of technology literacy. (2) Emergency remote education cannot provide effective learning and teaching. Participants' awareness of collaboration and communication skills was insufficient. (3) There were positive and negative aspects of emergency distance education towards face-to-face one. The educational environment, which has become digitalized with distance education, shows that there are changes in the views of the participants about the technology competence that they should have in their careers. As a result, remote education does not cause a significant difference in 21st century skills of participants. But the importance and need of twenty-first century skills in the distance education process become more apparent.

Process assessment of fencing coaches and referees within the scope of the COVID-19 normalization steps

The objective of this study is to measure and construe the opinions of the coaches and referees who are actively participating in fencing competitions in our country and who are licensed by the Turkish Fencing Federation (TFF) for the year of 2020, regarding the organizations to be planned and the issues to be paid attention, within the scope of the Covid-19 normalization steps. An online data collection form was used as a data collection tool in this study, which was structured through the qualitative research method. In the analysis process of the data obtained, the data analysis program called MAXQDA was used. While closed-ended questions were summarized by indicating percentages, the content analysis method was used in the process of analyzing open-ended question and answer reports. The data obtained were visualized with the help of frequency tables and code maps. Finally, some of the opinions of the participants, which were coded, were directly conveyed by interpreting the findings obtained through the tables and code maps. It was determined as a result of the study that the Covid-19 pandemic that poses impacts on the entire world affects the attitudes of fencing coaches and referees to participate in organizations. In the activities planned to be organized, the sub-codes like attending without an audience, open-air competitions, provision of spaciousness and ventilation means in the selection of a competition hall, and risk approval notification are classified.

A Machine Learning Approach for COVID-19 Tweet Classification

Vaccine-related information is awash on social media platforms like Twitter and Facebook. One party supports vaccination, while the other opposes vaccination and promotes misconceptions and misleading information about the risks of vaccination. The analysis of social media posts can give significant information into public opinion on vaccines, which can help government authorities in decision-making.This paper describes the dataset used in the shared task, and compares the performance of different classification that are provax, antivax and last neutral for identifying effective tweets related to Covid vaccines.We experimented with a classification-based approach. Our experiment shows that SVM classification performs well in order to effiective post.We're going to do this because vaccination is an important step for Covid19 so people can easily fix the news about the vaccine and grab their own slot and symptom detection is also playing a important part to arrest the spread of disease. © 2022 Copyright for this paper by its authors.

Network Public Opinion Detection During the Coronavirus Pandemic: A Short-Text Relational Topic Model

Online social media provides rich and varied information reflecting the significant concerns of the public during the coronavirus pandemic. Analyzing what the public is concerned with from social media information can support policy-makers to maintain the stability of the social economy and life of the society. In this article, we focus on the detection of the network public opinions during the coronavirus pandemic. We propose a novel Relational Topic Model for Short texts (RTMS) to draw opinion topics from social media data. RTMS exploits the feature of texts in online social media and the opinion propagation patterns among individuals. Moreover, a dynamic version of RTMS (DRTMS) is proposed to capture the evolution of public opinions. Our experiment is conducted on a real-world dataset which includes 67,592 comments from 14,992 users. The results demonstrate that, compared with the benchmark methods, the proposed RTMS and DRTMS models can detect meaningful public opinions by leveraging the feature of social media data. It can also effectively capture the evolution of public concerns during different phases of the coronavirus pandemic. © 2021 Association for Computing Machinery.

The Impact of the COVID-19 Pandemic on Quality of Financial Reports

The Corona Virus Disease pandemic has significant adverse effects on the economy, health, and society that have hampered global economic growth. Taiwan is one of the countries impacted by this pandemic. The pandemic had an enormous influence on the world economy, making the role of financial report quality an even more critical issue. This study aims to examine and evaluate the impact of the COVID-19 pandemic on the quality of financial reports. Additionally, it intends to examine and evaluate the differences between the impact of audit opinion and audit quality on the quality of financial reports before and after the COVID-19 pandemic. This study adopts secondary data, i.e., annual financial reports and audit quality data of public listing firms on the Taiwan Stock Exchange (TSE). Furthermore, this study selected data from 2016 to 2021. This study proves that the COVID-19 pandemic affects the quality of financial reports. Furthermore, this study suggests that the COVID-19 pandemic strengthens the negative impacts of audit opinion on the quality of financial reports using accrual earnings management proxies. However, it also shows that the audit quality did not impact the quality of financial reports proxied by accrual and real earnings management at the beginning of the COVID-19 outbreak.

Lights and Shadows of Studying Online: University Students' Perspective

The outburst and the spread of the COVID-19 pandemic has influenced every aspect of contemporary life. Since March 2020, education has moved to the online mode. Practically overnight the teaching and learning activities shifted from the physical to virtual spaces, which brought about both positive and negative opinions about this form of education. The chapter discusses advantages and disadvantages of distance teaching from the university students' perspective. Eighteen MA students attending a teacher training programme, majoring in English Philology, participated in the study. Their opinions were gathered by means of an online questionnaire that was administered twice: the first time at the end of the first semester of online teaching, the second time at the end of the second semester of online teaching. The collected data were both quantitative (statements on the Likert scale) and qualitative (answers to open-ended questions). The students' views show a complexity and dynamics of the situation incurred by the COVID-19 pandemic. They realise that online teaching has many advantages, for example saving time and money, having time for reflection about the activities they are engaged in, or time management. Yet, they also perceive the disadvantages of the situation which are related mostly to work overload, lack of face-to-face meetings and physical discomfort, for example eye-pain and poor physical condition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Opinions and Attitudes of Nursing Students Towards Distance Education During the COVID-19 Pandemic

BACKGROUND/AIMS: This study aimed to determine the opinions and attitudes of nursing students towards distance education during the coronavirus disease-19 (COVID-19) pandemic.MATERIALS AND METHODS: This research was designed as a descriptive study. Two hundred ten students of a nursing department of a private university in the Turkish Republic of Northern Cyprus, who received distance education in the spring semester of 2019-2020 academic year due to the COVID-19 pandemic, constituted the sample of this study. The participants were asked to complete an online survey which included a descriptive information form, an Opinions on Distance Education Scale (ODES) and an Attitude Scale towards Distance Education (ASDE).RESULTS: The mean age of the participants was 21.62 +/- 1.90 and 55.7% used their mobile phone to participate in distance education. The mean internet use was 6.58 +/- 0.27 hours per day and 74.4% had internet access problems. The mean scores obtained from the ODES and ASDE were 45.50 +/- 0.77 and 95.74 +/- 2.15, respectively. There was a positive and moderate correlation between the mean ODES and ASDE scores.CONCLUSION: The findings of this study imply that lectures with lab and clinical practice are not appropriate for distance education and so any missed lab or clinical practice might be compensated for via face-to-face education after the COVID-19 pandemic ends.

Federalism and pandemic policies: variety as the spice of life.

In the ordinary course of life, choices vary with age and other factors because one's opportunities vary with one's circumstances. Thus, investments in and expenditures on healthcare (and most other things) vary with age and a variety of other factors, including whether one lives in a rural area, suburb, or central city, health risks, risk aversion, and beliefs about the nature of a good life. Because assessment of the effects of illnesses vary with the same factors, the conclusions reached about best private and governmental health policies also tend to vary. This implies that conformity to "ideal" pandemic policies is more likely to be generated by a federal or polycentric system of policy making than a unitary system, especially ones that are constrained by a generality principle.

On COVID-19 vaccination in Nigeria: an empirical study

This study assessed the knowledge and perception of Nigerians about COVID-19 vaccination. A cross-sectional survey was conducted comprising Health and Non-health workers in Nigeria. The knowledge, attitude, and perception of respondents on COVID-19 vaccination in Nigeria was obtained through an online. Logistic regression was employed to determine which factor imparted on COVID-19 vaccination decision. The study showed a significant relationship between COVID-19 vaccination and immigration requirements. The survey showed that 74.07% of the health workers had been vaccinated, while 47.06% of non-Health Workers had been vaccinated. This study recommends that Governments at all levels should create more awareness of the importance of COVID-19 vaccination to increase the number of vaccinated individuals.

Research on sentiment classification algorithm for Corona Virus Disease 2019 Weibo text

Since the outbreak of Corona Virus Disease 2019, it has had a significant impact on people's lives. In order to help the government grasp the social opinion and do more scientific and practical propaganda and public opinion guidance for prevention and control, and to fully reflect people's attitude toward the epidemic and provide data support for government departments to release epidemic prevention measures. This paper uses Corona Virus Disease 2019-related Weibo comments as the research object and analyzes their sentiment using deep learning algorithms. The number of characters in Weibo comments is usually less than 140, which belongs to the category of short texts. Due to the use of few words, random user language, and irregular grammar, these texts have poor performance in text separation and word vector expression, adversely affecting sentiment classification. In order to solve this problem, this paper constructs the BERT-DPCNN model for sentiment analysis of epidemic short texts, which can not only extract the sentence-level text dependencies but also effectively avoid the problem of gradient disappearance of deep neural networks. The experiments show that the BERT-DPCNN model has the best effect and is of great value for the sentiment classification of short epidemic text. © 2022 IEEE.

Performance of Machine Learning Methods Using Tweets

Public opinions shared in common platforms like Twitter, Facebook, Instagram, etc. act as the sources of information for experts. Transportation and analysis of such data is very important and difficult due to data regulations and its structure. The pre-processing approaches and word-based dictionaries are used to understand the unprocessed data and make possible the opinions/tweets to be analyzed. Machine learning algorithms learn from past experience and use a variety of statistical, probabilistic and optimization algorithms to detect useful patterns from unstructured data sets. Our study aims to compare the performance of classification algorithms to predict individuals with COVID-19(+ ) or COVID-19(−) using the emotions among the tweets by text mining procedures. Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Trees (DT), Random Forest (RF), Artificial Neural Networks (ANN), Gradient Boost (GBM) and XGradient algorithms were used to extract the accuracy of model performance of each model for the detection and identification of the disease related to the COVID-19 virus, which has been on the agenda recently. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Multi-National Topics Maps for Parliamentary Debate Analysis

In recent years, automated political text processing became an indispensable requirement for providing automatic access to political debate. During the Covid-19 worldwide pandemic, this need became visible not only in social sciences but also in public opinion. We provide a path to operationalize this need in a multi-lingual topic-oriented manner. Using a publicly available data set consisting of parliamentary speeches, we create a novel process pipeline to identify a good reference model and to link national topics to the cross-national topics. We use design science research to create this process pipeline as an artifact. © 2022 IEEE Computer Society. All rights reserved.

Sentiment analysis of students' Facebook comments toward university announcements

Students' opinions are among the critical indicators to evaluate the university teaching process. However, due to the absence of an official online system in most universities that provides a mechanism for obtaining students' opinions on several university announcements, most students use various social networks to express their feelings and provide their opinions toward these announcements. We present, through this paper, sentiment analysis of Facebook comments written in the Moroccan Arabic dialect. These comments reflect the opinions of students about university announcements during the COVID-19 pandemic, especially those related to teaching mode and ex-am planning. Then, the comments collected were cleaned, preprocessed, and manually classified into four categories, namely positive, neutral, negative, and bipolar. Further, data dimensionality reduction is applied using TF-IDF and Chi-square test. Finally, we evaluated the performance of three standard classifiers, i.e., Naïve Bayesian (NB), Support Vector Machines (SVM), and Random Forests (RF) using k-fold cross-validation. The results showed that the SVM-based classifier performs as well as the RF-based classifier regarding the classification's accuracy and F1-score, while the NB-based classifier lags behind them. © 2022 IEEE.

Specific mining of COVID-19 reviews using deep learning methods

Sentiment classification is a valid measure to monitor public opinion on the COVID-19 epidemic. This study provides a significant basis for preventing the spread of adverse public opinion. Firstly, in epidemic texts, we use a convolutional neural network and bidirectional long short-term memory neural network BiLSTM model to classify and analyze the sentiment of the comment texts about the epidemic situation on Weibo. Secondly, embedded in the model layer to generate adversarial samples and extract semantics. Then, semantic information is weighted using the attention mechanism. Finally, the RMS optimizer is used to update the neural network weights iteratively. According to comparative experiments, the experimental results show that such four evaluation metrics as accuracy, precision, recall, and f1-score with our proposed model have obtained better classification performance. © 2022 IEEE.

DFM: Deep Fusion Model for COVID-19 Vaccine Sentiment Analysis

The coronavirus disease (COVID-19) pandemic has created a lot of healthcare concerns. Over the past two years, healthcare professionals worked hard to develop numerous vaccines to combat this virus which is truly remarkable. However, a large proportion of the global population is skeptical about the vaccines and the sudden emergence of the new strain of the virus is stirring up mixed emotions causing the use of opinion terms having varying polarities in different contexts which poses a challenge to predict the accurate sentiments from the user-generated data. In this work, a novel architecture namely a deep fusion model (DFM) with a meta-learning ensemble method is proposed for sentiment analysis of public opinions on COVID-19 vaccines and omicron variant on Twitter. The proposed model employed using natural language processing with deep learning models such as LSTM, GRU, CNN, and their various combinations. The purpose of this study is to understand the public opinion around COVID-19 vaccines and omicron variant through the proposed model. In addition, the experiment demonstrated effectiveness with an accuracy of up to 88% in comparison with state-of-the-art models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Infodemic: the epidemic of information during COVID19

India is one of the world's worst affected countries due to COVID-19 pandemic. The world is struglling to fight agaisnt centuries pandmemic. Globally goverments have been imposed lockdown and restrictions to control situation and minimise spread of infection. Social media was found the most practical and efficiant mediam to share information and opnions about pandmemic. At time of social distancing, social media helped people to share their feelings and find support. Same time overuse of social media palteform created panic and misinformation across countries. People sharing unconfirmed information about covid pandmemic and goverments were found it difficult to handle.

Information-Centric Networking based content collection and caching mechanism for network public opinion

With the global influence of the COVID epidemic, network public opinion control is particularly important especially for the purpose of stabilizing the panic at home and abroad. Effective public opinion collection and caching mechanism has a positive significance for the rapid spread of network public opinion. Therefore, by analyzing the accurate and rapid requirements of public opinion communication, this paper introduces the concept of Information-Centric Networking (ICN) to build a public opinion communication system. At the same time, the corresponding public opinion collection and caching mechanism is designed to optimize the dissemination process of the public opinion. The natural distributed structure of ICN makes the process of public opinion collection and caching distributed. Specifically, a suitable cache server is added between different public opinion collection servers via the distributed search engines. The experimental results show that the proposed distributed public opinion collection and caching mechanism can effectively deal with the spread of public opinion under the environment of the global COVID epidemic, including improving the accuracy of the public opinion transmission in time. © 2022 John Wiley & Sons, Ltd.