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.

Challenges faced in biomedical waste management by waste handlers amidst the COVID-19 pandemic in a tertiary care hospital: a qualitative study

Introduction : Coronavirus disease 2019 (COVID-19) saw an overhaul in the biomedical waste management (BMWM) practices. Waste handlers were at the brunt of these changes. If the challenges pertaining to BMWM at the ground level are better understood, more effective measures to overcome them can be formulated. Objectives: 1. To identify myths and concerns regarding BMWM in the context of COVID-19 pandemic. 2. To explore the challenges faced in BMWM amidst the COVID-19 pandemic. 3.To explore opportunities and future perspectives of BMWM. Method: In-depth interviews were conducted among 17 purposively selected Class IV health care workers during August to November 2021 in a tertiary care institute in Mumbai. Data was reported using thematic analysis. Results: Three major themes - challenges and concerns faced by BMW handlers, enablers/motivators, opportunities and future practices were generated from the transcripts. Various challenges faced by waste handlers were- difficulties in segregation and transport of BMW, exhaustion from PPE usage and fear of acquiring and spreading COVID-19 from work, stigma faced from public, and handling COVID-19 deaths. Support from family and colleagues, incentives and a positive change in public perception enabled them to work. Forming redressal committees, addressing job security concerns and timely provision of good quality equipment can improve hospital waste management measures in the future. Conclusion: It is of utmost importance to address challenges faced by waste handlers in BMWM. Onus should also be on periodic training in BMWM.

Social determinants in association with postpartum blues during the transition period of COVID-19 pandemic

Background:The COVID-19 pandemic has considerably impacted individuals' lives, extensively from mental and socioeconomic aspects, that requires someone to adapt. For postpartum mothers who also need to go through the maternal psychological adaptation phase, the pandemic could impose overwhelming emotional tension on them, increasing the risk of experiencing postpartum blues. This study aims to analyze the relationship between social factors that are affected during a pandemic with the incidence of postpartum blues on screening test results during the transition period of the COVID-19 pandemic in Banyumanik, Semarang. Subjects and Method: This was a cross-sectional study conducted in Banyumanik, Semarang, from November to December 2022.39 subjects were selected using a consecutive sampling technique. The dependent variable is postpartum blues. The independent variables include marital status, employment status of the mother, employment status of the spouse, and family income level. The study instrument was EPDS questionnaire. The data were analyzed used Chi-square. Results: Out of 39 subjects, 13 (33.3%) were experiencing postpartum blues, and 26 (66.7%) were not experiencing it. Mother's employment status associated with postpartum blues. Mothers who unemployed have a risk of experiencing postpartum blues 1.65 times compared to employed, but these were not statistically significant (OR= 1.65;95% CI= 0.40 to 6.77;p= 0.727). Family income level associated with postpartum blues. Mothers with low to moderate family income reduced postpartum blues by 0.73 times compared to mothers with high income levels, but these were not statistically significant (OR= 0.73;95%CI= 0.19 to 2.80;p= 0.908). Meanwhile, marital status and spouse employment status were not related to the incidence of postpartum blues. Conclusion: Mother's employment status and family income status associated with postpartum blues. Meanwhile, marital status and spouse employment status were not related to the incidence of postpartum blues.

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.

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 pandemic and the war in Ukraine - psychological and social dimensions of collective trauma in children with cancer and their parents

The focus of the present study is on the psychological and social dimensions of collective trauma resulting from the intense impact of strong emotional and stress factors connected with the COVID-19 pandemic and the crisis in Ukraine in parents of children with oncological diseases. The process of overcoming collective trauma in its diversity is a long one and requires specific care. The challenge of recovery is to regain the sense of control over the mental, economic and social parameters of the individuals affected. For the group of parents studied this includes discovery of a way for the caregivers to be calm and focused on the accompanying care for the child with an oncological disease, even in a pandemic situation and war. The effective overcoming collective trauma of the sense of imminent danger in society is based on public support and personal responsibility. Fear, anger, depression, isolation and lack of resources that are a direct psychological and economic result of pandemic and war, aggravates the quality of life of patients. As socially determined parameters, they carry a high risk of the recurrence and mortality of children with malignant diseases.

An IoT based Smart Robot that Aids in the Prevention of COVID19 Spread

The Internet of Things revolution is transforming current healthcare practices by combining technological, economic, and social aspects. Since December 2019, the global spread of COVID19 has influenced the global economy. The COVID19 epidemic has forced governments all around the world to implement lockdowns to prevent viral infections. Wearing a face mask in a public location, according to survey results, greatly minimizes the risk of infection. The suggested robotics design includes an IoT solution for facemask detection, body temperature detection, an automatic dispenser for hand sanitizing, and a social distance monitoring system that can be used in any public space as a single IoT solution. Our goal was to use IoT-enabled technology to help prevent the spread of COVID19, with encouraging results and a future Smart Robot that Aids in COVID19 Prevention. Arduino NANO, MCU unit, ultrasonic sensor, IR sensor, temperature sensor, and buzzer are all part of our suggested implementation system. Our system's processing components, the Arduino UNO and MCU modules are all employed to process and output data. Countries with large populations, such as India and Bangladesh, as well as any other developing country, will benefit from using our cost-effective, trustworthy, and portable smart robots to effectively reduce COVID-19 viral transmission. © 2022 IEEE.

Features of the course and medical and social aspects of a new coronavirus infection in the Republic of Tajikistan

The aim - to assess some medical and social aspects of the epidemic process during the first wave of a new coronavirus infectious disease - COVID-19 in the Republic of Tajikistan. Material and methods. The retrospective study was conducted on the basis of an epidemiological analysis of official statistics as part of the epidemiological surveillance of COVID-19 from April 2020 to April 2021. Results and discussion. At the beginning of April 2021, a total of 13 308 cases of COVID-19 were registered, of which the proportion of recovered was 99.3% (13 218 cases), and the number of cases with a fatal outcome was 0.68% (90 cases;the average age of the deceased was 62.3+/-0.07 years). The peak of infection during the first wave occurred in May and June 2020, when the average daily increase was 97 people. For 2 months of the epidemic in the republic, 44.6% of the total number of patients with COVID-19 became infected, and the number of deaths reached 52 people or 57.7%. Among the patients, men prevailed (65%). The largest number of deaths (76.7%;n=69) was among older people with comorbidities (diabetes mellitus, cardiovascular disease, chronic lung disease, metabolic syndrome, etc.). An analysis of the age structure showed that the main proportion of cases fell on the age group of 40-60 years (42.6%). It was found that a significant proportion of patients with COVID-19 was detected in the Sughd region (33.0%) and Dushanbe (30.1%). Conclusion. The COVID-19 pandemic showed that the national healthcare system of the Republic of Tajikistan was not sufficiently prepared for such a development of the COVID-19 epidemic process. There was an acute shortage of medical and preventive specialists in the republic. Given the current situation in the Republic of Tajikistan, within the framework of epidemiological surveillance, the features of the course of the COVID-19 epidemic process were analyzed, adequate emergency measures were developed and proposed to limit the spread of the virus and reduce the negative impact of COVID-19 on public health. The number of beds has been reasonably expanded, the capacity of the laboratory service has been increased, mass vaccination of the population has been started according to epidemic indications.Copyright © 2022 Geotar Media Publishing Group

A Deep Learning Approach for Sentiment Classification of COVID-19 Vaccination Tweets

Now-a-days, social media platforms enable people to continuously express their opinions and thoughts about different topics. Monitoring and analyzing the sentiments of people is essential for governments and business organizations to better understand people's feelings and thoughts. The Coronavirus disease 2019 (COVID-19) has been one of the most trending topics on social media over the last two years. Consequently, one of the preventative measures to control and prevent the spread of the virus was vaccination. A dataset was formed by collecting tweets from Twitter for over a month from November 13th to December 31st, 2021. After data cleaning, the tweets were assigned a positive, negative, or neutral label using a natural language processing (NLP) sentiment analysis tool. This study aims to analyze people's public opinion towards the vaccination process against COVID-19. To fulfil this goal, an ensemble model based on deep learning (LSTM-2BiGRU) is proposed that combines long short-term memory (LSTM) and bidirectional gated recurrent unit (BiGRU). The performance of the proposed model is compared to five traditional machine learning models, two deep learning models in addition to state-of-the-art models. By comparing the results of the models used in this study, the results reveal that the proposed model outperforms all the machine and deep learning models employed in this work with a 92.46% accuracy score. This study also shows that the number of tweets that involve neutral, positive, and negative sentiments is 517496 (37%) tweets, 484258 (34%) tweets, and 409570 (29%) tweets, respectively. The findings indicate that the number of people carrying neutral sentiments towards COVID-19 immunization through vaccines is the highest among others. © 2023, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Contraceptive usage and social barriers during the COVID-19 pandemic: a prospective cross-sectional study

Objective: To determine the frequency of contraceptive usage, the social barriers affecting their use, and the frequency of unplanned pregnancies during the COVID-19 pandemic Methodology: This is a prospective cross-sectional study carried out at the Department of Obstetrics and Gynaecology, Fazaia Ruth Pfau Medical College & PAF Hospitals from July 2020 to September 2020. All women of reproductive age attending the outpatient department were consecutively included. A pre-structured questionnaire was used for the purpose of collection of data. We obtained information regarding the use of contraception before and during the COVID-19 pandemic and the contraception methods used by these women. Furthermore, reasons for discontinuing contraception amongst those women who were using it earlier. The occurrence of pregnancy during pandemic was also noted. Results: Of the 350 women, 306 (87.4%) women practiced contraception before and 288 (82.3%) practiced it during the lockdown. Of 306 women practicing contraception before the lockdown, 265 (86.6%) continued practicing during the lockdown as well. Condom 145 (50.3%) and withdrawal method 116 (40.3%) were the most used methods amongst the 288 women practicing contraception during the lockdown. The noticeable increase in the number of those using withdrawal method was due to the lack of consultation following the fear of getting COVID (17 women, 41.5%) and no access to the clinic (14 women, 34.1%). These were the most common reasons for not using contraception, amongst the 41 women practicing contraception before the pandemic. Pregnancies were reported by 93 (26.6%) women out of whom 75 (80.6%) reported these to have been unplanned. Conclusion: The COVID pandemic has largely affected the utilization of contraceptives among women who were already practicing different contraceptive methods. Moreover, unplanned pregnancies are increasingly reported by women.

THE FOURTH ETHICAL COMMENT TOWARDS COVID-19 (three years later). TRUST AND FAITH: SOCIAL AND MORAL PROFILE OF VACCINATION.

The summarizing up the semantic and systemic results should comprise the next phase to provide insights into COVID-19 pandemic and consider it as a modern epidemic and humanitarian crisis on global level. The journal <<Infection and Immunity>> regularly and consistently present the results of ethically viewed legal framework of the pandemic and the administrative regulation of the public health system. Analysis and ethical assessment of the situation covers a wide range of issues, including the provision and operational adaptation of the regulatory framework, the problems of medical care, the processes and conditions for developing diagnostics, treatment and prevention, as well as all aspects related to the organization and implementing vaccination. Three previous ethical comments presented in 2020-2022 during the pandemic were devoted to these issues. Current study within the framework of the <<fourth ethical commentary>> follows directly from the data obtained while evaluating and analysing real-world experience on vaccination in the context of a regional cluster - the CIS member states, presented in the previous article. The perceived need and obvious significance of the study is to highlight objective factors of vulnerability in the vaccination during the COVID-19 pandemic and identify the response spectrum to form trust/or distrust to vaccination in various sectors of society, depending on a set of social and moral factors, including those coupled to a religious denomination. The data obtained are of paramount importance to find the moral ways to support and stabilize a responsible attitude with the aim to protect moral, social and physical health in emergency situations.Copyright © 2023 Saint Petersburg Pasteur Institute. All rights reserved.

Sociological aspects of Tokyo Olympics

The Olympic and Paralympic Games in Tokyo in July-September 2021 took place in a challenging social environment that seriously affected the public perception of events. When preparing for the Olympics in 2013-2019, the Japanese people actively supported the Games, which was confirmed by the results of numerous sociological studies. In March 2020, the COVID-19 pandemic began, followed by several waves of infection. The competition was postponed for a year. Vaccination in Japan was delayed compared to most G7 countries. Against this background, in the summer of 2021, the most dangerous Delta strain of coronavirus began to spread in the country, bringing the rise in mortality rates, and the overflowing of hospitals in large cities. In such a difficult epidemiological and social situation, surveys recorded a negative attitude towards the Olympics. However, during the competition, the majority opinion once again turned positive, mainly due to the athletic successes of the Japanese team and effective anti-virus control measures. The absence of spectators in the venues, most probably, did not affect the sporting achievements significantly. At least, Japanese Olympic team won a record number of medals. Infection prevention measures proved effective in limiting the transmission of the virus among the athletes and the Japanese service personnel. The economic and symbolic achievements of the Games did not meet expectations, as, during the Olympics, it was not possible to properly address its significance as the end point of the low-growth "lost decades", evidence of economic recovery after the triple disaster of 2011, and as a tool to increase Japan's tourist attractiveness. Therefore, during a pandemic, major sports events should be held primarily to train top-class athletes and to increase populace satisfaction with the success of the national team rather than to obtain direct economic benefits or improve the host country's image.

My journey as a SalviYorker

I am a daughter of the war. I was born in San Salvador, El Salvador, in 1975. Four years later, a coup d'etat overthrew an authoritarian government and soon the country found itself on the brink of a civil war that lasted more than a decade. For me, that meant a childhood without regular things like outdoor fun, nice clothes, or expensive toys. Every day, my parents fought for my siblings and I to have everything we needed, especially a good education. Although we lived in the capital city and the confrontation between the military and the guerrillas was in the countryside, we were afraid. However, we were very fortunate to survive the challenges--not only those of the war--, but also those of violent earthquakes, hurricanes, and other natural disasters that took place during those years. All of that has made Salvadorans a strong and resilient people. But resilience is one of the reasons why we often don't like to tell our stories. I do like to tell them though. I discovered this when I was in high school, while struggled with Mathematics and Chemistry. Fortunately for me, the Jesuit priests who taught us Literature and Writing motivated me to explore and find my inner voice. Shortly after I graduated in 1993 - a year after the signing of the Peace Accords, which ended the civil war - I was hired as an assistant to the editor at one of El Salvador's largest circulation newspapers.After a year, I joined the newsroom without having studied journalism. It was very hard. But here I am, twenty-five years later. Today, September 12, 2020, I have the opportunity to write these lines while waiting for the result of the International Latino Book Awards where my book SalviYorkers is a finalist in two categories. The awards ceremony will be held online in Los Angeles for the first time due to the COVID-19 pandemic. Regardless of the outcome, I consider myself a winner. Launching a book in 2020, having the possibility to present it to a wide variety of audiences, and selling copies during this unprecedented time is already a success.

I am what I am-Convergence Behaviors on Online Discussion about the Safety of COVID-19 Vaccines

Using data from an online discussion on the risk of getting blood clot from Johnson & Johnson vaccine moderated by the New York Times Facebook page, we investigated the presence of eleven convergence behaviors, and the interaction between them. While recent research focuses on misinformation or fake news as the object of analysis, we argue in this exploratory research that it is equally important to analyze who and, whenever possible, why people engage in information exchange given a particular crisis, hence their convergence behaviors. Mapping the types of postings to their authors would be an additional step to design, develop, implement, and possibly, regulate online discussions for a more effective and just civic engagement. As we witness a mass manipulation of public opinion, our findings suggest that the number of netizens that seek to correct misinformation is growing. If the society goal is to swiftly rebut as many conspiracy theories as possible, we advocate for a dual social media control strategy: restrain as much as possible the misinformation spreaders/manipulators and encourage correctors to help propagate countervailing facts. © 2022 IEEE Computer Society. All rights reserved.

Multimodal negative sentiment recognition of online public opinion on public health emergencies based on graph convolutional networks and ensemble learning

To improve the effect of multimodal negative sentiment recognition of online public opinion on public health emergencies, we constructed a novel multimodal fine-grained negative sentiment recognition model based on graph convolutional networks (GCN) and ensemble learning. This model comprises BERT and ViT-based multimodal feature representation, GCN-based feature fusion, multiple classifiers, and ensemble learning-based decision fusion. Firstly, the image-text data about COVID-19 is collected from Sina Weibo, and the text and image features are extracted through BERT and ViT, respectively. Secondly, the image-text fused features are generated through GCN in the constructed microblog graph. Finally, AdaBoost is trained to decide the final sentiments recognized by the best classifiers in image, text, and image-text fused features. The results show that the F1-score of this model is 84.13% in sentiment polarity recognition and 82.06% in fine-grained negative sentiment recognition, improved by 4.13% and 7.55% compared to the optimal recognition effect of image-text feature fusion, respectively. © 2023 Elsevier Ltd

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.

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.

What Remains from Covid-19? Agile Software Development in Hybrid Work Organization: A Single Case Study

Context: The Covid-19 pandemic led to a comprehensive transformation of how organizations operate and people work. Many companies, especially in the software development area, sent their employees to work from home in order to contain the spread of the virus. Nowadays, after two years of the pandemic, the companies introduce new regulations concerning the work organization for their employees. We notice a high variety of work organization types in practice, like work from home, work from anywhere, onsite work or mixed approaches. While some organizations integrated approaches like work from anywhere, others specify rules for part-time organizations onsite and work from home. This situation leads to new challenges for agile software development teams, as the agile practices in use are originally designed for onsite work and focusing on social aspects like collaboration. Also, the adaptions of specific agile practices due to the remote work during Covid-19 by the teams may do not cover the new circumstances of hybrid (distributed) work settings, while some team members work from home, others onsite in the office. Method: We conducted a quantitative survey in two agile software development teams in one company operating in a work from anywhere setting. Results: The results of this study show that the team members prefer to work remotely in future. However, some team members also want to work one or two days per week onsite in the office. This heterogeneous preferences of the workplace will lead to hybrid work organization and thus, to new challenges and advantages for agile software development team members. Conclusion: Based on our results, we discuss the needs of agile software development teams working in hybrid settings concerning equipment in the office, new software tools supporting the agile approach in use and the adaption of agile practices. Furthermore, we provide practical implications related to the need for sustainable flexibility on employee side concerning selecting the workplace. © 2022 IEEE.

Resilient supply chain network design without lagging sustainability responsibilities

In the 21st century, global supply chains have experienced severe risks due to disruptions caused by crises and serious diseases, such as the great tsunami, SARS, and, more recently, COVID-19. Building a resilient supply chain is necessary for business survival and growth. Similarly, there is increasing regulatory and social pressure for managers to continuously design and implement sustainable supply chain networks, encompassing economic, social, and environmental components. Hence, a panacea approach is required to establish a compromise position between resiliency concerns and sustainability responsibilities. To address this, this work presents a hybrid integrated BWM-CoCoSo-multi-objective programming model (BC-MOPM) formulated to deliver a compromise between resilience and sustainability supply chain network design (RS-SCND). First, a thorough literature review analysis is conducted to explore the relationship and correlation between resilience and sustainability to develop a framework for the resiliency and sustainability criteria, in a supply chain context. Second, four objectives were formulated, including the minimisation of total cost and environmental impact and the maximisation of social and resilience paradigms. A real two-tier supply chain network is deployed to evaluate the applicability of the developed BC-MOPM. Furthermore, sensitivity analysis is conducted to establish the relative importance of the identified criteria to prove the model's robustness. Results demonstrate the capability of the BC-MOPM in revealing trade-offs between the resiliency and sustainability aspects. © 2023 Elsevier B.V.