Analysis of Differences in User Groups and Post Sentiment of COVID-19 Vaccine Hesitators in Chinese Social-Media Platforms.

(1) Background: The COVID-19 epidemic is still global and no specific drug has been developed for COVID-19. Vaccination can both prevent infection and limit the spread of the epidemic. Eliminating hesitation to the COVID-19 vaccine and achieving early herd immunity is a common goal for all countries. However, efforts in this area have not been significant and there is still a long way to go to eliminate vaccine hesitancy. (2) Objective: This study aimed to uncover differences in the characteristics and sentiments of COVID-19 vaccine hesitators on Chinese social-media platforms and to achieve a classification of vaccine-hesitant groups. (3) Methods: COVID-19-vaccine-hesitation posts and user characteristics were collected on the Sina Microblog platform for posting times spanning one year, and posts were identified for hesitation types. Logistic regression was used to conduct user-group analysis. The differences in user characteristics between the various types of COVID-19 vaccine posts were analysed according to four user characteristics: gender, address type, degree of personal-information disclosure, and whether they followed health topics. Sentiment analysis was conducted using sentiment analysis tools to calculate the sentiment scores and sentiment polarity of various COVID-19 vaccine posts, and the K-W test was used to uncover the sentiment differences between various types of COVID-19-vaccine-hesitation posts. (4) Results: There are differences in the types of COVID-19-vaccine-hesitation posts posted by users with different characteristics, and different types of COVID-19-vaccine-hesitation posts differ in terms of sentiment. Differences in user attributes and user behaviors are found across the different COVID-19-vaccine-hesitation types. Ultimately, two COVID-19-vaccine-hesitant user groups were identified: Body-related and Non-bodily-related. Users who posted body-related vaccine-hesitation posts are more often female, disclose more personal information and follow health topics on social-media platforms. Users who posted non-bodily-related posts are more often male, disclose less personal information, and do not follow health topics. The average sentiment score for all COVID-19-vaccine-hesitant-type posts is less than 0.45, with negative-sentiment posts outweighing positive- and neutral-sentiment posts in each type, among which the "Individual rights" type is the most negative. (5) Conclusions: This paper complements the application of user groups in the field of vaccine hesitation, and the results of the analysis of group characteristics and post sentiment can help to provide an in-depth and comprehensive analysis of the concerns and needs of COVID-19 vaccine hesitators. This will help public-health agencies to implement more targeted strategies to eliminate vaccine hesitancy and improve their work related to the COVID-19 vaccine, with far-reaching implications for COVID-19-vaccine promotion and vaccination.

The YOPP Final Summit: Assessing Past and Forecasting Future Polar Prediction Research

The successes of YOPP from the presentations and keynote presentations included * a better understanding of the impact of key polar measurements (radiosondes and space-based instruments such as microwave radiometers), and recent advancements in the current NWP observing system, achieved through coordinated OSEs in both polar regions (e.g., Sandu et al. 2021);* enhanced understanding of the linkages between Arctic and midlatitude weather (e.g., Day et al. 2019);* advancements in the atmosphere–ocean–sea ice and atmosphere–land–cryosphere coupling in NWP, and in assessing and recognizing the added value of coupling in Earth system models (e.g., Bauer et al. 2016);* deployment of tailored polar observation campaigns to address yet-unresolved polar processes (e.g., Renfrew et al. 2019);* progress in verification and forecasting techniques for sea ice, including a novel headline score (e.g., Goessling and Jung 2018);* advances in process understanding and process-based evaluation with the establishment of the YOPPsiteMIP framework and tools (Svensson 2020);* better understanding of emerging societal and stakeholder needs in the Arctic and Antarctic (e.g., Dawson et al. 2017);and * innovative transdisciplinary methodologies for coproducing salient information services for various user groups (Jeuring and Lamers 2021). The YOPP Final Summit identified a number of areas worthy of prioritized research in the area of environmental prediction and services for the polar regions: * coupled atmosphere, sea ice, and ocean models with an emphasis on advanced parameterizations and enhanced resolution at which critical phenomena start to be resolved (e.g., ocean eddies);* improved definition and representation of stable boundary layer processes, including mixed-phase clouds and aerosols;incorporation of wave–ice–ocean interactions;* radiance assimilation over sea ice, land ice, and ice sheets;understanding of linkages between polar regions and lower latitudes from a prediction perspective;* exploring the limits of predictability of the atmosphere–cryosphere–ocean system;* an examination of the observational representativeness over land, sea ice, and ocean;better representation of the hydrological cycle;and * transdisciplinary work with the social science community around the use of forecasting services and operational decision-making to name but a few. The presentations and discussions at the YOPP Final Summit identified the major legacy elements of YOPP: the YOPPsiteMIP approach to enable easy comparison of collocated multivariate model and observational outputs with the aim of enhancing process understanding, the development of an international and multi-institutional community across many disciplines investigating aspects of polar prediction and services, the YOPP Data Portal3 (https://yopp.met.no/), and the education and training delivered to early-career polar researchers. Next steps Logistical issues, the COVID-19 pandemic, but also new scientific questions (e.g., the value of targeted observations in the Southern Hemisphere), as well as technical issues emerging toward the end of the YOPP Consolidation Phase, resulted in the decision to continue the following three YOPP activities to the end of 2023: (i) YOPP Southern Hemisphere (YOPP-SH);(ii) Model Intercomparison and Improvement Project (MIIP);of which YOPPSiteMIP is a critical element;and (iii) the Societal, Economics and Research Applications (PPP-SERA) Task Team.

Evaluating the quality of the public transport service during the COVID-19 pandemic from the perception of two user groups

The current paper focuses on a comparative analysis of both public transport (PT) and private vehicle (PV) users' perceptions on the quality of the service. To detect the key components of PT attributes a new hybrid methodology is applied, combining the importance-performance analysis and the importance-performance map analysis. The proposed hybrid approach is simpler and more integrated than the existing methods in the literature. The sample comprises an online panel and a total of 1028 questionnaires for PV and PT users surveyed during the pandemic period in Budapest. The results of the applied methods show that among the different groups, the service hour, the proximity, and the frequency attributes are important and performed well in the years of COVID-19. On the other hand, the temperature and the cleanliness factors are not significant predictors of the PV and PT users' general satisfaction. The obtained results can be used by local governments and authorities, who seek to identify areas to enhance the service quality of PT during movement restrictions in a pandemic wave. © 2023, The Author(s).

Design Justice in Practice: Community-led Design of an Online Maker Space for Refugee and Migrant Women

While varying degrees of participatory methods are often explored by the HCI community to enable design with different user groups, this paper seeks to add weight to the burgeoning demand for community-led design when engaging with diverse groups at the intersections of marginalisation. This paper presents a 24-month-long qualitative study, where the authors observed a community-based organisation that empowers refugee and migrant women in Australia through making. We report how the organisation led its own process to pivot from face-to-face to online delivery during the COVID-19 pandemic, analyzing the design and delivery of an app and the intersectional challenges faced by the women as they learnt to navigate online making. This paper expands feminist intersectional praxis in HCI to new contexts and critiques the positionality of researchers in this work. It contributes to the literature on design justice, providing an exemplar of how community-led design more effectively dismantles the compounding constraints experienced by intersectional communities. This paper also argues that the ethos of care and safe spaces, which are central to black feminist thought, are vital to community-led design and underpin the 10 design justice principles when executed in practice. © 2023 ACM.

Design of Game Data Acquisition System Based on Artificial Intelligence Algorithm

Game data collection system is a tool used to collect the behavior data of users about the game. It can be used for data analysis of user behavior so that game manufacturers can keep abreast of market dynamics and popular trends, and they also can have a deeper understanding of the behavioral habits and psychology of player user groups. The defects of the current data acquisition system include that the data are not encrypted. The network transmission efficiency is relatively low. The acquisition speed is slow, and the settings cannot be dynamically changed. This paper proposes to study how to enhance the acquisition ability and improve the analysis efficiency in the design of data acquisition system for solving these problems. Therefore, on the basis of artificial intelligence algorithm, this paper designs a game data collection system by using artificial neural network algorithm, support vector algorithm, and cluster analysis algorithm, which solves the basic problem of slow data collection in current data collection and plays a role in improving the efficiency of network transmission. The experimental results in this paper show that when the number of data is more than 300, the time-consuming time reaches more than 68 ms. When the number of written data is more than 300, it takes more than 181 ms. When the number of deleted data is more than 300, it takes more than 236 ms. From the above data, it shows that the designed game data collection system is rapid and efficient.

MISOhungry: A Platform that Encourages Easy Access to Recipe Search, Management, and Traditional Recipes

Cooking has played an essential role in the growth of culture and civilization for the past 1.8 million years. However, the lockdown in various countries, including Germany, has prompted people to improve their health and well-being due to the coronavirus pandemic. While doing this, searching for recipes becomes one of the popular and essential activities as it allows people worldwide to prepare dishes from various countries. But finding recipes on the internet is like searching in the wild with thousands of recipes available for a single dish. Traditional recipes are essential in a human being’s life. However, for students away from home or working young people who have little time to cook, many recipes have been forgotten for a long time. Therefore, MISOhungry gives solutions to both the user groups through this platform. The recipes provided are by scraping data from online food blogs to create recipes complete with ingredients nutritional information. On the same site, youngsters may also access traditional recipes provided by the elderly. Studies show that sharing recipes linked with memories stimulates generative activity in older adults and makes them happy later. The study demonstrates that the platform is accessible to both user groups, young people are interested in receiving traditional recipes, and they would like to use this platform which directly bridges the generation gap in recipe sharing, search, and management. MISOhungry promotes the idea of “Happiness is Homemade” by making cooking more accessible to both user groups. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Collection of End User Requirements and Use Cases during a Pandemic—Towards a Framework for Applied Research Projects

Research projects in the security domain often aim to develop innovative technology-based solutions for end users (e.g., situational awareness tools, crisis management tools). The pandemic crisis hit hard and without warning, not only influencing our everyday life but also the scientific community. To continue applied research projects during a pandemic, work structures needed to be adapted (e.g., user requirements collection, use case development), as face-to-face events were impossible but crucial to collect high quality requirements with a variety of different stakeholders. To ensure continued multi-stakeholder engagement we developed an overarching framework for collecting user requirements and use cases in an online setting and applied the framework within two research projects. The framework consists of four steps with the aim to assure high quality user requirements and use case collection (first analysis, stakeholder consultation, evaluation and prioritization, technical evaluation). The two projects presented in this paper provide insight on the potential of the framework. The framework offers a structured approach that fits for many different security research projects in terms of the easy application and its transferability. The main advantages (e.g., easily adaptable, reduced workshop time, no need to travel, suitability for different contexts and project types, etc.) and drawbacks (e.g., organization of online events, feedback collection time, etc.) of the framework are presented and discussed in this paper to offer increased stakeholder engagement. Empirical testing of the framework is proposed.

The Influence of Social Factors on Mental Health and Wellbeing during the COVID-19 Pandemic

This study uses Natural Language Processing and Machine Learning techniques to understand the effect of the COVID-19 pandemic on mental wellbeing. We considered different user groups and locations in the USA to analyze the influence contrasting social factors, such as political stance, had on wellbeing. We measured the mental wellbeing of the social media users through understanding negative sentiment and mental health topic discussion in Twitter posts added by users from the top 10 Democrat and top 10 Republican cities in the USA. To measure the topic discussion, we used a mental health keyword list and developed machine learning models to classify the topic of a tweet. The primary findings include the similarity of the effect the pandemic had on Republican and Democrat cities when considering a timeline of tweets, whilst an increase in 'Anxiety' was discussed across different user groups and cities. Enforcement strategies had an influence on mental wellbeing with results differing for Republican and Democrat cities. An accurate text classifier was developed and used to categorize tweets into different mental health topics. The results showed how medical and unemployed users discussed topics like 'anxiety' and 'depression' more than a control set of users. The best machine learning model was developed using a Decision Tree algorithm which achieved an accuracy of 87% on unseen data. © 2021 IEEE.