Human Behavior and Emerging Technologies最新文献

Over the last four decades, as populations around the world have expanded their use of social networks, cyberbullying incidents have likewise risen. Although social networks, including Twitter (now known as X), provide numerous benefits, such as quick communication with people both locally and globally, they also have negative consequences, the most common of which is cyberbullying. Studies show that users who have experienced cyberbullying have more negative feelings about themselves than those who have not. Thus, having technology that can effectively detect cyberbullying instances on social networks, such as Twitter, flag them and find ways to prevent them in the future is of utmost importance. This paper evaluates the available literature on utilising sentiment analysis to detect cases of cyberbullying. The research then explores sentiment analysis by constructing a machine learning model and training and testing the model using a dataset from Twitter. The algorithms used are naive Bayes, recurrent neural network (RNN) and support vector machine (SVM). These are all built on Python with the aid of existing Python libraries. The models are then evaluated to establish their performance, including the recall score, which measures false negatives. A performance comparison is carried out across the three models to find the most suitable algorithm for the task. The SVM, RNN and naive Bayes achieved accuracy scores of 91.37%, 90.59% and 83.62%, respectively. The results reveal that the SVM algorithm consistently outperformed the other two in detecting cyberbullying tweets. SVM has the potential to alter the way social media platforms and online communities moderate content, offering a strong balance of performance, speed and interpretability, making it well-suited for real-time cyberbullying detection on large-scale platforms. This allows for faster intervention to safeguard users, particularly vulnerable persons, from harassment and abuse, resulting in safer digital environments and improved overall user well-being.

Anxiety is a prevalent issue among university students, with recent studies indicating that one in three students experiences it or another emotional disorder. To address this, the use of standardized scales has been proposed to assess anxiety in this population. However, large-scale assessment remains challenging due to the lack of digital tools that facilitate widespread application. Traditional paper-based scales are time-consuming to administer and difficult to analyze efficiently. This article introduces AMAS-Mobile, a digital version of the AMAS-C scale designed for mobile devices, and presents its evaluation of validity and reliability through a nonexperimental exploratory study with Mexican university students between the ages of 18 and 50. This evaluation implies that a statistical analysis was conducted, which included calculating McDonald’s omega coefficient (ω) to assess reliability, as well as performing an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA) to evaluate validity. The AMAS-Mobile is reliable since ω = 0.87, indicating satisfactory internal consistency for both the overall instrument and the individual subscales. EFA revealed a four-factor structure, explaining 37.48% of the total variance. In addition, CFA indicated that the model fit accuracy index was analyzed (χ² = 2055.554, p < 0.001), indicating differences between the observed and expected matrices. A model fit analysis was also performed (RMSEA = 0.056; CFI = 0.794), which indicated that the model presented an adequate fit but was outside the expected range. This finding suggests a new arrangement of items.

Online discrimination is an alarming phenomenon that draws growing attention across academic disciplines. However, this interest has led to fragmented knowledge, with research often confined within disciplinary boundaries. This study introduces an innovative hybrid approach that combines a scoping review with textual analysis to bridge this gap by (1) mapping the existing literature, (2) identifying key concepts across disciplines, and (3) offering an open, interactive tool for scholars, policymakers, and professionals. Following PRISMA guidelines, we selected 374 scientific publications from 2011 to 2024 across diverse fields (i.e., arts and humanities, history, information and communication technology, law, medicine, psychology, and social sciences). Then, key concepts were identified through textual analysis of the titles and abstracts of the selected contributions, revealing five thematic classes: “consequences on mental health,” “online discrimination detection,” “critical political discourse,” “laws and regulations,”, and “perceptions and reactions.” For each class, we conducted a similarity analysis to further explore its structure and associations. Based on our findings, we propose a transdisciplinary framework to better understand online discrimination and provide a publicly accessible interactive tool and database for further exploration. This tool enables practitioners to perform targeted analyses and support evidence-based decision-making.

The study examines the associations between the adoption of AI, worker training, customer communication by the adoption of AI, and regulatory awareness and customer satisfaction in the Jordan context. Through the application of SmartPLS software on 308 participants who have knowledge of the variables, the study findings provide the centrality of the adoption of AI, worker training, and customer communication to customer satisfaction. Organizational culture also has the key role to play as the moderator between the adoption of AI and customer satisfaction. The study findings provide insightful policy recommendations to practitioners and policy implementers in the context of the first Arab Kingdom to embed the adoption of AI, prioritize worker education, and maintain the positive organizational culture to obtain customer satisfaction and realize the long-term business goals.

Recent research highlights an increasing trend of technology-based activities gaining popularity among children and adolescents. In particular, social media and video game usage, when dysfunctional, have shown potential to develop into addictive behaviors that may negatively impact mental health. This study was aimed at exploring and comparing, based on developmental stages and gender, the involvement of children and adolescents in daily activities, mobile phone ownership with internet access, social media behaviors, problematic social media use (PSMU), and internet gaming disorder (IGD). The study surveyed a sample of 5652 children and adolescents aged 9–16 from all Spanish autonomous communities. Daily routines primarily included sports or exercise, using social media, chatting with family, and watching TV, with variations across age groups and genders. Approximately half of the children and almost all adolescents owned a mobile phone, with findings indicating that the age of first ownership is progressively decreasing. Age-based differences in social media behaviors were observed, with higher usage among adolescents but no significant differences or even a slightly higher presence of some problematic behaviors among younger children. Girls generally used social media more frequently than boys, while boys engaged in video gaming to a greater extent. PSMU was identified in 6% of children and adolescents who use social media, while 2.4% of adolescents who play video games self-report symptoms aligned with IGD. These findings provide insights into current patterns of technology use among youth, highlighting the presence of addictive tendencies associated with social media and video games.

What drives people to have trust in using artificial intelligence (AI)? How does the institutional environment shape social trust in AI? This study addresses these questions to explain the role of institutions in allowing AI-based technologies to be socially accepted. In this study, social trust in AI is situated in three institutional entities, namely, the government, tech companies, and the scientific community. It is posited that the level of social trust in AI is correlated to the level of trust in these institutions. The stronger the trust in the institutions, the deeper the social trust in the use of AI. To test this hypothesis, we conducted a cross-country survey involving a total of 4037 respondents in Singapore, Taiwan, Japan, and the Republic of Korea (ROK). The results show convincing evidence of how institutions shape social trust in AI and its acceptance. Our empirical findings reveal that trust in institutions is positively associated with trust in AI technologies. Trust in institutions is based on perceived competence, benevolence, and integrity. It can directly affect people’s trust in AI technologies. Also, our empirical findings confirm that trust in AI technologies is positively associated with the intention to use these technologies. This means that a higher level of trust in AI technologies leads to a higher level of intention to use these technologies. In conclusion, institutions greatly matter in the construction and production of social trust in AI-based technologies. Trust in AI is not a direct affair between the user and the product, but it is mediated by the whole institutional setting. This has profound implications on the governance of AI in society. By taking into account institutional factors in the planning and implementation of AI regulations, we can be assured that social trust in AI is sufficiently founded.

Programming is widely recognized as a fundamental and practical skill applicable across diverse fields through various applications. However, novices often face challenges in learning programming, primarily due to the absence of a structured instructional framework and the complexity of underlying concepts. This obstacle can diminish learners’ motivation to pursue further education. To address this, gamification is employed as a strategy to engage and inspire beginners in their educational journey. Consequently, the utilization of a gamified online programming education system is proposed to simplify the learning process. Nevertheless, designing and implementing educational courses that effectively integrate gaming elements requires expertise in the gaming field. In this study, a model-driven approach creates a gamification framework for teaching programming. The methodology develops a domain-specific modeling language for programming concepts and gamification, designs a graphical editor for course design, and implements a model-to-code transformation engine requiring minimal prior knowledge. Evaluation through usability testing, questionnaires, and the GQM approach shows enhanced usability, improved effectiveness, and high satisfaction compared to traditional methods. The framework offers a solution for simplifying gamified course development and supporting novice programmers.

Assistive technologies may have an important role in fulfilling unmet needs and increasing quality of life for people living with dementia. The type and design of user interfaces (e.g. touchscreen and voice activation) may impact how people with dementia use these technologies. We aimed to understand which types of user interfaces have been developed for this population, how interfaces are chosen, how their effectiveness is tested and what recommendations there are for optimizing interface design for people with dementia. This systematic scoping review summarized findings from 87 journal articles. Two-thirds of included studies incorporated codesign. Very few (14%) experimentally tested the effectiveness of a user interface, and many lacked ecological validity (52%). Common recommendations for user interface design included tailoring the interface to the user, providing multiple modalities, and incorporating principles of universal design. Training users on how to interface with the technology may not be beneficial for devices that are intended to be used entirely independently by a person living with dementia. Instead, designers should focus on harnessing retained or existing skills so that interaction is intuitive. More research is needed that directly compares different interface options to each other to gain evidence of what is most useful for people with dementia, as well as technology development that is deeply and meaningfully grounded in the lived experiences, values, preferences and priorities of people living with dementia.

Public safety remains a critical concern in Eswatini, as it prevents crime, reduces delayed response mechanisms, and optimizes police resources. This study applied machine learning techniques in predictive policing within the Kingdom of Eswatini (formerly Swaziland) to improve proactive law enforcement strategies and public safety. Crime has been a challenge in many societies and continues to threaten public safety, social cohesion, and economic development. Law enforcement agents often use reactive approaches to handle criminal incidents, which are generally associated with various impediments, such as delayed responses to crime incidents, resource-intensive operations, victimization, and insufficient proactive crime prevention measures. Integrating machine learning techniques for predictive policing emerges as a new panacea for effective policing and crime prevention. However, there is a dearth of literature advocating proactive policing through predictive policing. Therefore, this study proposes a proactive approach to crime prediction and prevention by using machine learning models such as XGBoost, random forest, multilayer perceptron (MLP), and K-nearest neighbors (KNN) models. These models were trained and tested using data from the Royal Eswatini Police Services (REPS). Our findings indicate that XGBoost provides the highest predictive accuracy at approximately 71.4%, with precision ranging from 0.65 to 0.81 and recall from 0.34 to 0.81, making it the preferred model for balanced performance across the metrics. Random forest recorded an accuracy of 66.2%, while MLP and KNN have 62.2% and 55.5% accuracy, respectively. The study recommends the integration of intelligence-based models to enhance proactive crime prediction and identify potential crime hotspots. This can assist in optimizing resource allocation to prevent crime. Additionally, collaboration among stakeholders, including national security agents, policymakers, and the community, is essential to effectively adopt and utilize predictive policing technologies to enhance security operations.

Given the contemporary landscape of social media interactions and their profound influence on consumer behavior, this study is aimed at exploring the intricate connections between subjective norms, social media stalking, peer influence, and their impact on internal cognitive and emotional processes. Specifically, we explore how these factors, including envy, the need to belong, and self-congruence, lead to transformative interactions that manifest as impulse buying, customer satisfaction, and emotional attachment. We utilized an online survey to collect data from 659 participants and subsequently employed SmartPLS to analyze the data collected via structural equation modeling. The findings showed the significant positive impact of subjective norms and social media stalking on peer influence, which enhances the chain relationship from peer influence to envy and then impulse buying. The mediating role of obsessive passion between peer influence and emotional attachment is supported in contrast to self-congruence. Contrary to earlier research findings indicating a direct link between customer satisfaction and emotional attachment in the field of impulse buying, the satisfaction resulting from impulse buying does not influence emotional attachment in this paper. Both theoretical and practical implications were discussed.