Abstract
Societies throughout today’s global village are increasingly aware of the social injustices that minorities face, and immigrants are no exception. Combined with the lack of adaptation resources and the prejudice of non-migrant residents, immigrants may feel powerless in foreign places as they try to find comfort and security in new and unfamiliar environments. It is increasingly urgent to address immigrant issues, considering the crucial role of enhancing diversity, combating prejudice, and raising awareness of minority experiences. This systematic literature review investigates the innovative use of gamification in exploring and addressing the experiences and issues immigrants face. The review follows the PRISMA statement guidelines and checklist. Scopus, CINAHL, and Medline databases were searched, resulting in 17 relevant articles that were carefully analyzed. This research highlights the diverse applications of gamification in studying immigrant experiences via role-playing, interactive storytelling, and empathy-building simulations. This work explores the potential of gamified interventions in addressing pressing issues immigrants face and assesses their effectiveness in fostering empathy and intercultural communication. It also identifies gaps in the existing information sciences literature and proposes directions for future research. In conclusion, this review sheds light on the emerging field of gamification in immigration studies and games studies in the information sciences, providing valuable insights for scholars, policymakers, and practitioners working with immigrant communities worldwide. © 2024 Master of Library Science Program, East Carolina University. All rights reserved.

Abstract
With the increasing popularity of social media platforms like Instagram, there is a growing need for effective methods to detect and analyze abnormal actions in user-generated content. Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning that can learn complex patterns. This article proposes a novel deep learning approach for detecting abnormal actions in social media clips, focusing on behavioural change analysis. The approach uses a combination of Deep Learning and textural, statistical, and edge features for semantic action detection in video clips. The local gradient of video frames, time difference, and Sobel and Canny edge detectors are among the operators used in the proposed method. The method was evaluated on a large dataset of Instagram and Telegram clips and demonstrated its effectiveness in detecting abnormal actions with about 86% of accuracy. The results demonstrate the applicability of deep learning-based systems in detecting abnormal actions in social media clips.

Abstract
In recent years, social media platforms have become an essential source of information. Therefore, with their increasing popularity, there is a growing need for effective methods for detecting and analyzing their content in real time. Deep learning is a machine learning technique that teaches computers to understand complex patterns. Deep learning techniques are promising for analyzing acoustic signals from social media posts. In this article, a novel deep learning approach is proposed for socially contextualized event detection based on acoustic signals. The approach integrates the power of deep learning and meaningful features such as Mel frequency cepstral coefficients. To evaluate the effectiveness of the proposed method, it was applied to a real dataset collected from social protests in Iran. The results show that the proposed system can find a protester's clip with an accuracy of approximately 82.57%. Thus, the proposed approach has the potential to significantly improve the accuracy of systems for filtering social media posts.

Abstract
Recent advancements in digital technology have significantly impacted healthcare, with the rise of chatbots as a promising avenue for healthcare services. These chatbots aim to provide prevention, diagnosis, and treatment services, thereby reducing the workload on medical professionals. Despite this trend, limited research has explored the variables influencing user experiences in the design of healthcare chatbots. While the impact of visual representation within chatbot systems is recognized, existing studies have primarily focused on efficiency and accuracy, neglecting graphical interfaces and non-verbal visual communication tools. This research aims to delve into user experience aspects of symptom checker chatbots, including identity design, interface layout, and visual communication mechanisms. Data was collected through a comprehensive questionnaire involving three distinct chatbots (Healthily, Mediktor and Adele - a self-developed solution) and underwent meticulous analysis, yielding valuable insights to aid the decision process when designing effective chatbots for symptom checking.

Abstract
Objective: This study aims to present the developmental stages of a Mobile App Prototype designed to enhance patient participation in the rehabilitation process after hip replacement. Methods: To ensure effective interaction between the system and the end user, a User-Centered Design methodology was followed, encompassing three phases: Requirements gathering, Prototyping, and Evaluation. Usability tests were conducted to assess the usability of the developed system. Results: The RehabApp for mobile devices was created, and the testing results were positive. Users expressed satisfaction with the outcome, deeming it a valuable tool for their recovery. This outcome demonstrates the high receptiveness of these technologies in the healthcare sector, making it a project that can readily be expanded into other areas of rehabilitation. Conclusion: This study demonstrated the potential of the RehabApp in the rehabilitation process after hip replacement surgery. This accomplishment was realized by ensuring the active participation of patients, potential users, and healthcare professionals throughout the app's development. Innovation: The RehabApp is a mobile application to provide users with all the necessary knowledge, enabling them to undergo a smoother and safer rehabilitation. Feedback from both patients and healthcare professionals played a crucial role in refining the app's features and addressing usability concerns.

Abstract
Objective: The study aimed to demonstrate the development of a mobile app prototype, BarrierBeGone, a system that identifies potential barriers for individuals with mobility disabilities and promotes accessibility using gamification strategies. The main goal is to raise awareness about mobility and accessibility difficulties, especially for wheelchair users, and to promote more responsible behaviours. Method: The User-Centred Design methodology was employed, going through three phases: requirements gathering, design and development, and evaluation. Additionally, interviews with five individuals with mobility disabilities helped define the initial system requirements. The development of the barrier identification system was followed by usability tests with nine representative users. Results: The results of the usability tests of the "BarrierBeGone" barrier identification system were extremely positive. Stakeholders recognized the utility and simplicity of the platform, considering it a motivating factor for future use. Conclusion: The results support the effectiveness of the proposed educational tool in increasing awareness about accessibility and social inclusion in smart cities. This study makes a significant contribution to the field of urban planning and inclusive design. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.