The success of touristic businesses, attractions, and destinations heavily relies on travel agents’ recommendations, which significantly impact client satisfaction. However, the underlying recommendation process employed by travel agents remains poorly understood. This study presents a conceptual model of the recommendation process and empirically investigates the influence of tourism categories on agents’ destination recommendations. By employing collaborative filtering-based recommendation systems and comparing various algorithms, including matrix factorization and deep learning models, such as the bilateral variational autoencoder (BiVAE) and light graph convolutional neural network, this research provides insights into the performance of different techniques in the context of tourism. The models were evaluated using a tourism dataset and assessed through a range of metrics. The results indicate that the BiVAE algorithm outperformed others in terms of ranking and prediction metrics, underscoring the significance of considering multiple measurements and exploring diverse techniques. The findings have practical implications for tourism marketers seeking to influence travel agents and offer valuable insights for researchers investigating this domain. Additionally, the proposed model holds potential for applications in travel recommendation systems, including attraction recommendations.

Stuttering is a widespread speech disorder affecting people globally, and it impacts effective communication and quality of life. Recent advancements in artificial intelligence (AI) and computational intelligence have introduced new possibilities for augmenting stuttering detection and treatment procedures. In this systematic review, the latest AI advancements and computational intelligence techniques in the context of stuttering are explored. By examining the existing literature, we investigated the application of AI in accurately determining and classifying stuttering manifestations. Furthermore, we explored how computational intelligence can contribute to developing innovative assessment tools and intervention strategies for persons who stutter (PWS). We reviewed and analyzed 14 refereed journal articles that were indexed on the Web of Science from 2019 onward. The potential of AI and computational intelligence in revolutionizing stuttering assessment and treatment, which can enable personalized and effective approaches, is also highlighted in this review. By elucidating these advancements, we aim to encourage further research and development in this crucial area, enhancing in due course the lives of PWS.

People receive a wide variety of news from social media. They especially look for information on social media in times of crisis with the desire to assess the risk they face. This risk assessment, and other aspects of user reactions, may be affected by characteristics of the social media post relaying certain information. Thus, it is critical to understand these characteristics to deliver information with the reactions in mind. This study investigated various types of imagery used as thumbnails in social media posts regarding news about the COVID-19 pandemic. In an experimental design, 300 participants viewed social media posts containing each of the three types of imagery: data visualization (directly about risk information), advisory (not containing direct risk information, but instead help on how to lower risk), or clickbait (containing no risk-related information, just generic visuals). After observing the social media posts, they answered questionnaires measuring their emotions (valence, arousal, and dominance), risk perception, perceived credibility of the post, and engagement. The participants also indicated their emotions towards and interest in COVID-19 news coverage, age, gender, and how often and actively they use social media. These variables acted as controls. The data were analysed using mixed linear models. Results indicated that advisory imagery positively influenced valence, arousal, dominance, credibility, and (lower) risk perception. Alternatively, imagery showing data visualizations yielded low valence, arousal, dominance, credibility, and high risk perception. Clickbait-styled thumbnails which carry no information are usually measured between the other two types. The type of imagery did not affect the motivation to engage with a post. Aside from visual imagery, most variables were affected by COVID sentiment and the usual activity on social media. These study results indicate that one should use advisory imagery for more comfortable news delivery and data visualization when the poster wishes to warn users of existing risks.

Recently, generative AI has gained increased attention and impact across a variety of areas. Fake news can be used to manipulate public opinion, it is an important topic of investigation. Many factors affect how people perceive news in terms of believability and veracity, including the context. This study investigated how the platform (Facebook, X, and LinkedIn) on which an article is posted and its origin (AI-generated or human-written) affect perceptions of believability and veracity. The findings indicate that LinkedIn is the most trusted site, followed by X and Facebook. Participants who trusted Facebook content were more likely to find articles from all platforms real and believable. AI-generated articles were perceived as less believable and more likely to be fake than human-written ones when posted on Facebook or X, but the opposite was the case for LinkedIn. These findings indicate that trust in a platform may be connected to the overall perception of news articles and that the context in which an AI-generated article is posted may affect the way it is perceived

This systematic review analyzes the ethical and emotional challenges associated with Human-Digital Twin Interaction (HDTI), an emerging area that integrates real-time data modeling and human-centered artificial intelligence. The review synthesizes findings from 39 peer-reviewed studies in healthcare, education, and industrial sectors, highlighting key issues such as data privacy, algorithmic bias, emotional authenticity, and user autonomy. A thematic analysis demonstrates the manifestation of these challenges at the intersection of technical design and human experience, impacting user trust, emotional well-being, and ethical compliance. The review presents a multidimensional framework that connects essential design elements namely personalization, empathy modeling, and explainability with their ethical implications, emotional effects, and practical implementation strategies. This study emphasizes the significance of emotional calibration, participatory design, and ethical auditing as essential mechanisms for ensuring the responsible deployment of HDTI. The review examines not only individual user concerns but also system-level and societal implications, such as institutional trust, social equity, and the cultural formation of emotional norms. The findings highlight the necessity for interdisciplinary collaboration and policy innovation to ensure that HDTI systems are consistent with the principles of transparency, fairness, and emotional integrity. This study seeks to direct subsequent research and influence the development of ethically and emotionally sustainable digital twin technologies.