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A clickbait is a deceptive headline designed to boost ad revenue without presenting closely relevant content. There are numerous negative repercussions of clickbait, such as causing viewers to feel tricked and unhappy, causing long-term confusion, and even attracting cyber criminals. Automatic detection algorithms for clickbait have been developed to address this issue. The fact that there is only one semantic representation for the same term and a limited dataset in Chinese is a need for the existing technologies for detecting clickbait. This study aims to solve the limitations of automated clickbait detection in the Chinese dataset.

This study combines both to train the model to capture the probable relationship between clickbait news headlines and news content. In addition, part-of-speech elements are used to generate the most appropriate semantic representation for clickbait detection, improving clickbait detection performance.

This research successfully compiled a dataset containing up to 20,896 Chinese clickbait news articles. This collection contains news headlines, articles, categories and supplementary metadata. The suggested context-aware clickbait detection (CA-CD) model outperforms existing clickbait detection approaches on many criteria, demonstrating the proposed strategy's efficacy.

The originality of this study resides in the newly compiled Chinese clickbait dataset and contextual semantic representation-based clickbait detection approach employing transfer learning. This method can modify the semantic representation of each word based on context and assist the model in more precisely interpreting the original meaning of news articles.

The existing traditional recommendations based on content-based filtering (CBF), collaborative filtering (CF) and hybrid approaches are inadequate for recommending practice challenges in programming online judge (POJ). These systems only consider the preferences of the target users or similar users to recommend items. In the learning environment, recommender systems should consider the learning path, knowledge level and ability of the learner. Another major problem in POJ is the learners don't give ratings to practice challenges like e-commerce and video streaming portals. This purpose of the proposed approach is to overcome the abovementioned shortcomings.

To achieve the context-aware practice challenge recommendation, the data preparation techniques including implicit rating extraction, data preprocessing to remove outliers, sequence-based learner clustering and utility sequence pattern mining approaches are used in the proposed approach. The approach ensures that the recommender system considers the knowledge level, learning path and learning goals of the learner to recommend practice challenges.

Experiments on practice challenge recommendations conducted using real-world POJ dataset show that the proposed system outperforms other traditional approaches. The experiment also demonstrates that the proposed system is recommending challenges based on the learner's current context. The implicit rating extracted using the proposed approach works accurately in the recommender system.

The proposed system contains the following novel approaches to address the lack of rating and context-aware recommendations. The mathematical model was used to extract ratings from learner submissions. The statistical approach was used in data preprocessing. The sequence similarity-based learner clustering was used in transition matrix. Utilizing the rating as a utility in the USPAN algorithm provides useful insights into learner–challenge relationships.

This paper aims to propose a conceptual framework for integrating information and communication technologies (ICT) into teaching practices for educators in rural Ghana lacking computer and internet access. Bridging the rural–urban digital divide is critical for equitable quality education. This research identifies distinct infrastructure, financial, human, and socio-cultural challenges of ICT adoption in underserved Ghanaian schools through an exhaustive literature review. The framework provides tailored strategies to empower teachers to leverage ICT for innovative pedagogy despite limitations. Ultimately, the goal is to offer pragmatic, evidence-based solutions to advance ICT-enabled instruction and digital equity in marginalized settings.

This study utilizes a qualitative literature review methodology to develop a conceptual framework for ICT teaching techniques tailored to the rural Ghanaian context. Published empirical studies, theoretical papers, and reports focused on ICT education in developing countries, particularly sub-Saharan Africa, were systematically reviewed across multidisciplinary databases. Relevant theories and models, like TPACK and SAMR, were analyzed to determine appropriate modifications for the framework design. The proposed strategies emphasize affordable technologies, teacher training, localized content, and culturally relevant pedagogies selected based on evidence from literature to address the unique barriers and needs of the rural setting.

The literature review revealed profound infrastructure, financial, human, and socio-cultural barriers to ICT adoption in rural Ghanaian schools. These include lack of electricity, internet, hardware, and software alongside inadequate teacher training and support. The proposed conceptual framework offers solutions including leveraging low-cost technologies like mobile devices, prioritizing teacher professional development, developing localized digitally-enabled resources, and fostering community participation. Culturally relevant pedagogies are also emphasized. This tailored, evidence-based approach holds promise for enhancing ICT-enabled instruction and bridging digital divides to promote equitable quality education. Further findings will emerge through framework implementation and evaluation.

This conceptual framework requires implementation and evaluation to truly determine its impact and effectiveness in the intended rural Ghanaian context. As a literature review, primary data collection was not undertaken. Practical application of the framework in schools can yield direct insights through surveys, interviews, observations, and analysis of key educational outcomes pre and post-implementation. Comparisons with control groups may further illuminate its efficacy. Monitoring and adaptation over time is also essential. Ultimately, this research sets the foundation for praxis-oriented work transforming ICT integration in marginalized communities to advance digital equity and inclusion.

This research provides an evidence-based, context-aware framework with pragmatic solutions to enhance ICT-enabled teaching and learning in underserved rural Ghanaian schools. It offers guidance to teachers, administrators, and policymakers on integrating technology despite infrastructure and resource constraints. The emphasis on cost-effective innovations, educator training, localized content, and community participation can inform planning and investments in ICT education. Broader applications include adapting the framework for other developing country contexts facing comparable challenges. Overall, this work promotes digital equity and quality education access, catalyzing social development and aligning with the UN Sustainable Development Goals.

This research highlights the urgent need to address digital divides exacerbating educational inequalities between rural and urban communities in Ghana. Lack of technology access and utilization in rural schools threatens to widen gaps in skills, capabilities, and opportunities for students. Strategic integration of ICT can promote social inclusion, cultivate digital literacy, and equip youth to participate in the global digital economy, thereby enhancing prospects. More broadly, advancing digital equity and ICT-enabled education empowers marginalized groups, fosters community empowerment, and drives progressive social change. This work underscores that localized, context-appropriate solutions are pivotal for promoting development and social justice.

This research fills a crucial gap by proposing a conceptual framework tailored specifically for integrating ICT in teaching practices in rural Ghanaian schools lacking digital access, responding to context-specific barriers and needs. While extensive literature examines ICT education models globally, few studies offer localized strategies for resource-constrained environments. This framework adapts established models based on a systematic literature review, emphasizing affordability, capacity building, and cultural relevance. By synthesizing evidence-based, pragmatic solutions to advance ICT-enabled instruction in marginalized settings, this work provides a valuable foundation for praxis transforming rural education. The localization approach could inform frameworks for other developing regions.

This paper aims to establish an efficient maintenance management system tailored for healthcare facilities, recognizing the crucial role of medical equipment in providing timely and precise patient care.

The system is designed to function both as an information portal and a decision-support system. A knowledge-based approach is adopted centered on Semantic Web Technologies (SWTs), leveraging a customized ontology model for healthcare facilities’ knowledge capitalization. Semantic Web Rule Language (SWRL) is integrated to address decision-support aspects, including equipment criticality assessment, maintenance strategies selection and contracting policies assignment. Additionally, Semantic Query-enhanced Web Rule Language (SQWRL) is incorporated to streamline the retrieval of decision-support outcomes and other useful information from the system’s knowledge base. A real-life case study conducted at the University Hospital Center of Oran (Algeria) illustrates the applicability and effectiveness of the proposed approach.

Case study results reveal that 40% of processed equipment is highly critical, 40% is of medium criticality, and 20% is of negligible criticality. The system demonstrates significant efficacy in determining optimal maintenance strategies and contracting policies for the equipment, leveraging combined knowledge and data-driven inference. Overall, SWTs showcases substantial potential in addressing maintenance management challenges within healthcare facilities.

An innovative model for healthcare equipment maintenance management is introduced, incorporating ontology, SWRL and SQWRL, and providing efficient data integration, coordinated workflows and data-driven context-aware decisions, while maintaining optimal flexibility and cross-departmental interoperability, which gives it substantial potential for further development.

Misinformation surrounding the Sustainable Development Goals (SDGs) has contributed to the formation of misbeliefs among the public. The purpose of this paper is to investigate public sentiment and misbeliefs about the SDGs on the YouTube platform.

The authors extracted 8,016 comments from YouTube videos associated with SDGs. The authors used a pre-trained Python library NRC lexicon for sentiment and emotion analysis, and to extract latent topics, the authors used BERTopic for topic modeling.

The authors found eight emotions, with negativity outweighing positivity, in the comment section. In addition, the authors identified the top 20 topics discussing various SDGs and SDG-related misbeliefs.

The authors reported topics related to public misbeliefs about SDGs and associated keywords. These keywords can be used to formulate social media content moderation strategies to screen out content that creates these misbeliefs. The result of hierarchical clustering can be used to devise and optimize response strategies by governments and policymakers to counter public misbeliefs.

This study represents an initial endeavor to gain a deeper understanding of the public’s misbeliefs regarding SDGs. The authors identified novel misbeliefs about SDGs that previous literature has not studied. Furthermore, the authors introduce an algorithm BERTopic for topic modeling that leverages transformer architecture for context-aware topic modeling.

Limited knowledge exists regarding the adverse effects of artificial intelligence adoption, including platforms like ChatGPT, on users’ mental well-being. The current research seeks to adopt the insight from the stressor-strain-outcome paradigm and a moderated mediation model to examine how technology anxiety moderates the direct and indirect relationships between compulsive use of ChatGPT, technostress, and life satisfaction.

Drawing data from a sample of 2,602 ChatGPT users in Vietnam, PROCESS macro was approached to test the moderated mediation model.

The findings indicate that compulsive use of ChatGPT exhibited a substantial and positive impact on technostress, while technostress was found to have a negative influence on life satisfaction. Moreover, although compulsive use of ChatGPT did not show a significant direct effect, it indirectly impacts life satisfaction via technostress. Remarkably, technology anxiety was found to significantly moderate both direct and indirect associations between compulsive use of ChatGPT, technostress, and life satisfaction.

Based on the findings of this research, some practical implications are provided.

The research offers a fresh perspective by applying the stressor-strain-outcome perspective to provide empirical evidence on the moderated mediation effects of technology anxiety and technostress on the relationship between compulsive use of ChatGPT and users’ life satisfaction. The research thus sheds new light on artificial intelligence adoption and its effects on users’ mental health.

ChatGPT is a versatile technology with practical use cases spanning many professional disciplines including marketing. Being a recent innovation, however, there is a lack of academic insight into its tangible applications in the marketing realm. To address this gap, the current study explores ChatGPT’s application in marketing by mining social media data. Additionally, the study employs the stages-of- growth model to assess the current state of ChatGPT’s adoption in marketing organizations.

The study collected tweets related to ChatGPT and marketing using a web-scraping technique (N = 23,757). A topic model was trained on the tweet corpus using latent Dirichlet allocation to delineate ChatGPT’s major areas of applications in marketing.

The topic model produced seven latent topics that encapsulated ChatGPT’s major areas of applications in marketing including content marketing, digital marketing, search engine optimization, customer strategy, B2B marketing and prompt engineering. Further analyses reveal the popularity of and interest in these topics among marketing practitioners.

The findings contribute to the literature by offering empirical evidence of ChatGPT’s applications in marketing. They demonstrate the core use cases of ChatGPT in marketing. Further, the study applies the stages-of-growth model to situate ChatGPT’s current state of adoption in marketing organizations and anticipate its future trajectory.

This paper aims to give an overview of the history and evolution of commercial search engines. It traces the development of search engines from their early days to their current form as complex technology-powered systems that offer a wide range of features and services.

In recent years, advancements in artificial intelligence (AI) technology have led to the development of AI-powered chat services. This study explores official announcements and releases of three major search engines, Google, Bing and Baidu, of AI-powered chat services.

Three major players in the search engine market, Google, Microsoft and Baidu started to integrate AI chat into their search results. Google has released Bard, later upgraded to Gemini, a LaMDA-powered conversational AI service. Microsoft has launched Bing Chat, renamed later to Copilot, a GPT-powered by OpenAI search engine. The largest search engine in China, Baidu, released a similar service called Ernie. There are also new AI-based search engines, which are briefly described.

This paper discusses the strengths and weaknesses of the traditional – algorithmic powered search engines and modern search with generative AI support, and the possibilities of merging them into one service. This study stresses the types of inquiries provided to search engines, users’ habits of using search engines and the technological advantage of search engine infrastructure.

For the provision of smart library services to end users, tools of the Internet of Things (IoT) play a significant role. The study aims to discover the factors influencing the adoption of IoT in university libraries, investigate the impact of IoT on university library services and identify challenges to adopt IoT applications in university libraries.

A systematic literature review was carried out to address the objectives of the study. The 40 most relevant research papers published in the world’s leading digital databases were selected to conduct the study.

The findings illustrated that rapid growth in technology, perceived benefits, the networked world and the changing landscape of librarianship positively influenced the adoption of IoT in university libraries. The study also displayed that IoT supported library professionals to initiate smart library services, assisted in service efficiency, offered context-based library services, provided tracking facilities and delivered effective management of library systems. Results also revealed that a lack of technical infrastructure, security and privacy concerns, a lack of technological skills and unavailability of policy and strategic planning caused barriers to the successful adoption of IoT applications in university libraries.

The study has provided theoretical implications through a valuable addition to the current literature. It has also offered managerial implications for policymakers to construct productive policies for the implementation of IoT applications in university libraries for the attainment of fruitful outcomes. Finally, the study provides a baseline for understanding the adoption of IoT in academic libraries.

Mobile learning has seen tremendous growth over the years. Like any other software application, usability is one of the key concerns in its successful implementation. There is a lack of study that provides a comprehensive overview of usability testing of mobile learning applications. Motivated by this a mapping study is conducted.

A systematic mapping study was conducted using 51 papers retrieved from the Scopus database published between 2005 and 2022 that reported on usability testing of mobile learning applications.

The key findings suggest that research is expected to expand in the near future. User-based testing is the commonly used method, while data are collected mainly through questionnaires, observation and interviews. Testing is mainly conducted in a controlled environment.

The study provides (1) an evidence-based discussion on usability testing of mobile learning applications, (2) an up-to-date map on state of the art on usability testing of mobile learning applications and (3) providing direction for further research to scientifically strengthen the field.