Background: As the number of COVID-19 cases grows worldwide, one solution to the global pandemic is vaccination. Unfortunately, the hesitancy of receiving vaccines is still high, particularly among younger age groups (eg, students). Because the hesitancy of receiving vaccines is an important issue, instruments have been developed to assess vaccine hesitancy. Moreover, the use of these instruments among specific groups such as students is of critical importance. Aim: The present study examined the psychometric properties of the nine-item MoVac-COVID19S (also known as the DrVac-COVID19S) including its measurement invariance among university students in three different countries (ie, Taiwan, Indonesia, and Malaysia). Methods: A multi-country, web-based cross-sectional survey study was conducted among 1809 university students, recruited from the three countries from May to September 2021. The nine-item scale was translated into Traditional Chinese, Bahasa Indonesian, and Malay. Results: The results of the confirmatory factor analysis indicated that the one-factor structure of the MoVac-COVID19S was fully supported among Indonesian and Malay participants (comparative fit index [CFI] = 0.991 and 0.998; Tucker-Lewis index [TLI] = 0.997 and 0.987; root mean sqaure error of approximation [RMSEA] = 0.040 and 0.071; and standardized root mean square residual [SRMR] = 0.014 and 0.039). Moreover, the four-factor structure was supported among Indonesian, Malay, and Taiwanese participants (CFI = 0.998, 0.998, and 0.985; TLI = 0.997, 0.996, and 0.973; RMSEA = 0.044, 0.038, and 0.091; and SRMR = 0.013, 0.018, and 0.049). Conclusion: The MoVac-COVID19S has good construct validity among university students from three different countries (ie, Taiwan, Indonesia, and Malaysia). The four-factor structure of the MoVac-COVID19S was supported. Therefore, health-care providers may want to assess the four underlying constructs to better understand why a university student accepts or declines COVID-19 vaccine uptake in the three countries. Using the findings, government policymakers and health-care authorities can design appropriate programs to help decrease vaccine hesitancy.

Social media slang, encompassing informal language, words, phrases, and acronyms on digital platforms, reflects the dynamic nature of online communication. Analyzing social media slang offers valuable insights for organizations and researchers, enabling a deeper understanding of communication trends, sentiment analysis, and user behavior in the digital sphere. It plays a pivotal role in shaping effective marketing strategies and enhancing communication, ultimately facilitating informed decision-making in the digital age. In our study, we conducted a systematic review of research articles from the Web of Science and Scopus databases, spanning the years 2016 to 2023. Our rigorous selection process, based on quality assessments as per PRISMA guidelines, revealed several following key findings. Social media slang exhibits a remarkable adaptability to different platforms, mirroring the communication styles and user cultures found on each. Notably, it influences user behavior, impacting interactions, content engagement, and decision-making, particularly in marketing and communication strategies. Furthermore, our research highlights the value of social media slang in sentiment analysis, providing insights into public sentiment and supporting well-informed decision-making. Our study underscores the versatile applications of slang analytics across various industries and research domains, emphasizing its pivotal role in providing specialized insights and enhancing communication strategies. In conclusion, our research offers a comprehensive understanding of the dynamic landscape of informal language in the context of contemporary digital communication, furnishing valuable insights that inform decision-making, refine marketing strategies, and enhance communication.

Ambiguous software requirements are a significant contributor to software project failure. Ambiguity in software requirements is characterized by the presence of multiple possible interpretations. As requirements documents often rely on natural language, ambiguity is a frequent challenge in industrial software construction, with the potential to result in software that fails to meet customer needs and generates issues for developers. Ambiguities arise from grammatical errors, inappropriate language use, multiple meanings, or a lack of detail. Previous studies have suggested the use of supervised machine learning for ambiguity detection, but limitations in addressing all ambiguity types and a lack of accuracy remain. In this paper, we introduce the fault-prone software requirements specification detection model (FPDM), which involves the ambiguity classification model (ACM). The ACM model identifies and selects the optimal algorithm to classify ambiguity in software requirements by employing the deep learning technique, while the FPDM model utilizes Boosting ensemble learning algorithms to detect fault-prone software requirements specifications. The ACM model achieved an accuracy of 0.9907, while the FPDM model achieved an accuracy of 0.9750. To validate the results, a case study was conducted to detect fault-prone software requirements specifications for 30 edge/cloud applications, as edge/cloud-based applications are becoming crucial and significant in the current digital world.

Background: In the current era of rapid technological innovation, our lives are becoming more closely intertwined with digital systems. Consequently, every human action generates a valuable repository of digital data. In this context, data-driven architectures are pivotal for organizing, manipulating, and presenting data to facilitate positive computing through ensemble machine learning models. Moreover, the COVID-19 pandemic underscored a substantial need for a flexible mental health care architecture. This architecture, inclusive of machine learning predictive models, has the potential to benefit a larger population by identifying individuals at a heightened risk of developing various mental disorders. Objective: Therefore, this research aims to create a flexible mental health care architecture that leverages data-driven methodologies and ensemble machine learning models. The objective is to proficiently structure, process, and present data for positive computing. The adaptive data-driven architecture facilitates customized interventions for diverse mental disorders, fostering positive computing. Consequently, improved mental health care outcomes and enhanced accessibility for individuals with varied mental health conditions are anticipated. Method: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, the researchers conducted a systematic literature review in databases indexed in Web of Science to identify the existing strengths and limitations of software architecture relevant to our adaptive design. The systematic review was registered in PROSPERO (CRD42023444661). Additionally, a mapping process was employed to derive essential paradigms serving as the foundation for the research architectural design. To validate the architecture based on its features, professional experts utilized a Likert scale. Results: Through the review, the authors identified six fundamental paradigms crucial for designing architecture. Leveraging these paradigms, the authors crafted an adaptive data-driven architecture, subsequently validated by professional experts. The validation resulted in a mean score exceeding four for each evaluated feature, confirming the architecture’s effectiveness. To further assess the architecture’s practical application, a prototype architecture for predicting pandemic anxiety was developed.

Human emotion is often a reflection of one’s behaviour that leads to awareness of one’s mental health. Emotion detection on Twitter users specifically gains attention as it generates information that is useful in the field of psychology and linguistics. However, most existing works related to the subject analysed the emotion of a tweet based on the number of keywords or phrases in it, thus resulting in false annotations and eventually false judgement of the user’s emotion. This paper proposes a two-step model with high and reliable accuracy to detect the emotion of Twitter users based on the semantic meaning of the tweets they posted. The first step classified the emotion of each tweet using four different deep learning techniques. The second step detected the emotion of the user based on the proposed statistical post-level features and a boosting technique. Then, Kappa’s agreement score method was implemented to validate both models. The first step achieved 0.9482 in accuracy while the second step achieved 0.9683 in accuracy. To further validate such highly accurate result, a case study was conducted, and a web-based system was developed to analyse the emotion of college students during the COVID-19 pandemic. Additionally, our analysis corresponded to most of the current reports on the pandemic which further proved the reliability of the developed model.