Search Results (182)

This study examines how Environmental, Social, and Governance (ESG) reporting is communicated and perceived on YouTube. A dataset of 553 relevant videos and 5060 user comments was extracted on 2 April 2025 ranging between 2014 and 2025, and sentiment, topic, and stance analyses were applied to both transcripts and comments. The majority of video content strongly endorsed ESG reporting, emphasizing themes such as transparency, regulatory compliance, and financial performance. In contrast, viewer comments revealed diverse stances, including skepticism about methodological inconsistencies, accusations of greenwashing, and concerns over politicization. Notably, statistical analysis showed minimal correlation between video sentiment and audience sentiment, suggesting that user perceptions are shaped by factors beyond the tone of the videos themselves. These findings underscore the need for more rigorous ESG frameworks, enhanced standardization, and proactive stakeholder engagement strategies. The study highlights the value of online platforms for capturing stakeholder feedback in real time, offering practical insights for organizations and policymakers seeking to strengthen ESG disclosure and communication. Full article

Cystic Fibrosis Screen Positive Inconclusive Diagnosis/CFTR-related Metabolic Syndrome (CFSPID/CRMS) presents a significant clinical challenge due to its variable diagnostic outcomes and uncertain disease progression. Current diagnostic strategies, including sweat chloride testing and genetic analysis fall short in delivering clear guidance for clinical decision-making and risk assessment. Here, we comment on the potential of CFTR functional tests in patient-derived intestinal organoids (PDIOs) to enhance early risk stratification in CFSPID/CRMS cases. Using four hypothetical cases based on real-world data, we illustrate diverse clinical trajectories: diagnosis of cystic fibrosis (CF), reclassification as a CFTR-related disorder (CFTR-RD), non-CF designation, and persistent diagnostic uncertainty. Organoid-based assays—such as forskolin-induced swelling (FIS), steady-state lumen area (SLA) analysis, and rectal organoid morphology analysis (ROMA)—offer functional insights into CFTR activity and drug responsiveness. Compared to existing CFTR functional tests, such as Intestinal Current Measurement (ICM) and Nasal Potential Difference (NPD), these assays are more accessible, highly reproducible, and when needed support personalized medicine approaches. PDIO-based assays could help identify infants at high risk of disease progression, facilitating earlier interventions while minimizing unnecessary follow-ups for those unlikely to develop CF-related symptoms. Although not yet widely implemented, these assays hold promise for refining CFSPID diagnostics and management. Future research should focus on establishing standardized protocols allowing validation of clinical utility. Full article

Aspect-Based Sentiment Analysis (ABSA) has gained significant popularity in recent years, which emphasizes the aspect-level sentiment representation of sentences. Current methods for ABSA often use pre-trained models and graph convolution to represent word dependencies. However, they struggle with long-range dependency issues in lengthy texts, resulting in averaging and loss of contextual semantic information. In this paper, we explore how richer semantic relationships can be encoded more efficiently. Inspired by quantum theory, we construct superposition states from text sequences and utilize them with quantum measurements to explicitly capture complex semantic relationships within word sequences. Specifically, we propose an attention-based semantic dependency fusion method for ABSA, which employs a quantum embedding module to create a superposition state of real-valued word sequence features in a complex-valued Hilbert space. This approach yields a word sequence density matrix representation that enhances the handling of long-range dependencies. Furthermore, we introduce a quantum cross-attention mechanism to integrate sequence features with dependency relationships between specific word pairs, aiming to capture the associations between particular aspects and comments more comprehensively. Our experiments on the SemEval-2014 and Twitter datasets demonstrate the effectiveness of the quantum-inspired attention-based semantic dependency fusion model for the ABSA task. Full article

This study investigates Spanish-language public discourse on YouTube following the unprecedented Iberian Peninsula blackout of 28 April 2025. Leveraging comments extracted via the YouTube Data API and analyzed with the OpenAI GPT-4o-mini model, it systematically examined 76,398 comments from 360 of the most relevant videos posted on the day of the event. The analysis explored emotional responses, sentiment trends, misinformation prevalence, civic engagement, and attributions of blame within the immediate aftermath of the blackout. The results reveal a discourse dominated by negativity and anger, with 43% of comments classified as angry and an overall negative sentiment trend. Misinformation was pervasive, present in 46% of comments, with most falsehoods going unchallenged. The majority of users attributed the blackout to government or political failures rather than technical causes, reflecting a profound distrust in institutions. Notably, while one in five comments included a call to action, only a minority offered constructive solutions, focusing mainly on infrastructure and energy reform. These findings highlight the crucial role of multilingual, real-time crisis communication and the unique information needs of Spanish-speaking populations during emergencies. By illuminating how rumors, emotions, and calls for accountability manifest in digital spaces, this study contributes to the literature on crisis informatics, digital resilience, and inclusive sustainability policy. Full article

The interpretation of archaeological heritage encounters inherent challenges due to the fragmentation and contextual loss of the physical site. Virtual reality has emerged as an innovative medium for enhancing user engagement and promoting meaningful dissemination of culture. This exploratory study investigates the design and preliminary expert-based evaluation of arkeOyun, a virtual reality game created to better understand archaeological sites’ spatial and cultural significance, by sampling the Kültepe Archaeological Site. The aim of this study is to evaluate the usefulness of virtual game-based approaches in the dissemination of cultural heritage and user interaction, emphasising spatial clarity, narrative integration, and immersive engagement. Our study incorporates qualitative and quantitative methods, utilising concurrent think-aloud and heuristic evaluation with participants who were selected due to their expertise in heritage, design, and human–computer interaction domains. Participants engaged with arkeOyun via a head-mounted display, and their real-time comments and post-experience evaluations were systematically evaluated. Results indicate that although participants responded positively to the game’s immersive design, interface simplicity, and spatial organisation, notable deficiencies were seen in narrative coherence, emotional resonance, and multimodal feedback. Navigation and the presentation of informative content were seen as critical areas requiring improvement. The data triangulation revealed both consistent and varying assessments, highlighting the need for context-specific support, varied task structures, and emotionally compelling narratives for enhanced interpretation of cultural significance. The findings of our study illustrate the potential of virtual reality games as a medium for cultural heritage interpretation via arkeOyun. For experiences to evolve from immersive simulations to major interpretative platforms, it is vital to integrate narrative frameworks, multimodal scaffolding, and user-centred interaction tactics more deeply. The results of this exploratory pilot study present preliminary findings on integrating virtual reality games in archaeological heritage interpretation and contribute to further projects. Full article

Unlike static comments, which are typically located below online videos, live comments affect consumers’ video-watching experiences in real time and may influence their evaluation of the video in distinct ways. Despite the significance of live comments, few studies have explored the differentiated effects of live comments vs. static comments on user evaluation of online videos. Utilizing a dataset comprising approximately two million pieces of textual data from a leading Chinese online video platform, our findings reveal substantial differences between the effects of live and static comments, with these effects varying by video type (informational versus emotional) and showing notable changes during health threats. This study examines the differential impact of live vs. static comments, providing empirical evidence for distinct information processing pathways under varying time constraints. Our results shed light on the underlying mechanisms responsible for the diverse effects of different forms of social interaction, offering valuable theoretical insights. They also have managerial implications regarding how online video platforms can facilitate engagement among viewers and between video creators and their audiences. Full article

Cyberhate presents a multifaceted, context-sensitive challenge that existing detection methods often struggle to tackle effectively. Large language models (LLMs) exhibit considerable potential for improving cyberhate detection due to their advanced contextual understanding. However, detection alone is insufficient; it is crucial for software to also promote healthier user behaviors and empower individuals to actively confront the spread of cyberhate. This study investigates whether integrating large language models (LLMs) with persuasive technology (PT) can effectively detect cyberhate and encourage prosocial user behavior in digital spaces. Through an empirical study, we examine users’ perceptions of a self-monitoring persuasive strategy designed to reduce cyberhate. Specifically, the study introduces the Comment Analysis Feature to limit cyberhate spread, utilizing a prompt-based fine-tuning approach combined with LLMs. By framing users’ comments within the relevant context of cyberhate, the feature classifies input as either cyberhate or non-cyberhate and generates context-aware alternative statements when necessary to encourage more positive communication. A case study evaluated its real-world performance, examining user comments, detection accuracy, and the impact of alternative statements on user engagement and perception. The findings indicate that while most of the users (83%) found the suggestions clear and helpful, some resisted them, either because they felt the changes were irrelevant or misaligned with their intended expression (15%) or because they perceived them as a form of censorship (36%). However, a substantial number of users (40%) believed the interventions enhanced their language and overall commenting tone, with 68% suggesting they could have a positive long-term impact on reducing cyberhate. These insights highlight the potential of combining LLMs and PT to promote healthier online discourse while underscoring the need to address user concerns regarding relevance, intent, and freedom of expression. Full article

Expected lives of mechanical parts and structures depend upon the environmental conditions, their dynamic behaviours and the task-oriented spectra of different loadings. This paper exploits contactless full-field mobilities, estimated by Scanner Laser Doppler Vibrometry (SLDV), in the real manufacturing, assembling and loading conditions of the thin plate tested, whose structural dynamics can be described in broad frequency bands, with no distorting inertia of sensors and no numerical models. The paper derives the mobilities into full-field strain Frequency Response Functions (FRFs), which map, by selecting the proper complex-valued broad frequency band excitation spectrum, the surface strains. From the latter, by means of the constitutive model, dynamic stress distributions are computed, to be exploited in fatigue spectral methods to map the expected life of the component, according to the selected tasks’ spectra and the excitation locations. The results of this experiment-based approach are thoroughly commented in sight of non-destructive-testing, damage and failure prognosis, Structural Health Monitoring, manufacturing and maintenance actions. Full article

Background/Objectives: Nutritional needs for people with chronic spinal cord injury (SCI) are inadequately addressed due to the lack of comprehensive evidence and scattered research. We established a consensus-based framework for addressing the nutritional needs of community-dwelling adults with chronic SCI who can ingest food orally. Methods: A web-based Delphi design was employed to ascertain an expert consensus. The Delphi panel consisted of physicians, registered dietitians (RDs), and researchers knowledgeable in SCI and nutrition. Informed by a literature review, 18 nutrition statements were rated by 15 panelists. The survey included statements about SCI-specific dietary energy assessments and macro- and micronutrients. Results: The response rate for the panel (N = 15) was 100%. Consensus levels, scores, stability levels, and response numbers were documented for each statement. The statements received consensus scores ranging from 4.14 to 8.13 on a 9-point Likert scale. Alternative expert comments and suggestions were also provided for each statement. Conclusion: Engaging a diverse panel of experts, the real-time Delphi process yielded expert-reviewed nutrition statements based on an extensive literature review and expert opinions. The rated statements contribute to the ongoing dialogue in SCI-specific nutrition, providing a practical resource for healthcare professionals working with adults with chronic SCI. Full article

The frequency with which fake news or misinformation is published on social networks is constantly increasing. Users of social networks are confronted with many different posts every day, often with sensationalist titles and content of dubious veracity. The problem is particularly common in times of sensitive social or political situations, such as epidemics of contagious diseases or elections. As such messages can have an impact on democratic processes or cause panic among the population, many countries and the European Commission itself have recently stepped up their activities to combat disinformation campaigns on social networks. Since previous research has shown that there are no tools available to combat disinformation in the Croatian language, we proposed a framework to detect potentially misinforming content in the comments on social media. The case study was conducted with real public comments published on Croatian Facebook pages. The initial results of this framework were encouraging as it can successfully classify and detect disinformation content. Full article

Selection bias can cause recommendation systems to over-rely on users’ historical behavior and ignore potential interests, thus reducing the diversity and accuracy of recommendations. Our research on selection bias reveals that the existing literature often overlooks the impact of sentiment factors on selection bias. In recommendation tasks, sentiment bias—stemming from users’ sentiment reactions—can lead to the suggestion of low-quality products to important users and unfair recommendations of niche items (targeted at specific markets or purposes). Addressing sentiment bias and enhancing recommendations for key users could help balance research on selection bias. Sentiment bias is embedded in user ratings and reviews. To mitigate this bias, it is essential to analyze user ratings and comments to uncover genuine sentiments. To this end, we have developed a sentiment analysis module aimed at eliminating discrepancies between reviews and ratings, providing accurate sentiment scores, extracting users’ true opinions, and reducing sentiment bias. Additionally, we have designed a combinatorial function that adapts to three distinct scenarios for bias correction. Moreover, we introduce the concept of dynamic debiasing, where the modeling time is not fixed but varies over time. On this basis, we propose a dynamic selection debiased recommendation method based on sentiment analysis. This paper demonstrates how the three approaches—sentiment analysis for data sparsity, combinatorial functions for dataset optimization, and time-dynamic modeling with inverse propensity weighting—can effectively mitigate selection bias. Our experiments with multiple real-world datasets show that our model can significantly enhance recommendation performance. Full article

Aesthetics primarily focuses on the study of art, encompassing the aesthetic categories of beauty and ugliness, as well as human aesthetic activities. Image Aesthetics Assessment (IAA) seeks to automatically evaluate the aesthetic quality of images by mimicking the perceptual mechanisms of humans. Recently, researchers have increasingly explored using user comments to assist in IAA tasks. However, human aesthetics are subjective, and individuals may have varying preferences for the same image, leading to diverse comments that can influence model decisions. Moreover, in practical scenarios, user comments are often unavailable. Thus, this paper proposes a CLIP-based method for IAA (IAACLIP) using generative descriptions and prompts. First, leveraging the growing interest in multimodal large language models (MLLMs), we generate objective and consistent aesthetic descriptions (GADs) for images. Second, based on aesthetic images, labels, and GADs, we introduce a unified contrast pre-training approach to transition the network from the general domain to the aesthetic domain. Lastly, we employ prompt templates for perceptual training to address the lack of real-world comments. Experimental validation on three mainstream IAA datasets demonstrates the effectiveness of our proposed method. Full article

Background/Objectives: The study explores the integration of human feedback into the control loop of mobile robots for real-time obstacle detection and avoidance using EEG brain–computer interface (BCI) methods. The goal is to assess the possible paradigms applicable to the most current navigation system to enhance safety and interaction between humans and robots. Methods: The research explores passive and active brain–computer interface (BCI) technologies to enhance a wheelchair-mobile robot’s navigation. In the passive approach, error-related potentials (ErrPs), neural signals triggered when users comment or perceive errors, enable automatic correction of the robot navigation mistakes without direct input or command from the user. In contrast, the active approach leverages steady-state visually evoked potentials (SSVEPs), where users focus on flickering stimuli to control the robot’s movements directly. This study evaluates both paradigms to determine the most effective method for integrating human feedback into assistive robotic navigation. This study involves experimental setups where participants control a robot through a simulated environment, and their brain signals are recorded and analyzed to measure the system’s responsiveness and the user’s mental workload. Results: The results show that a passive BCI requires lower mental effort but suffers from lower engagement, with a classification accuracy of 72.9%, whereas an active BCI demands more cognitive effort but achieves 84.9% accuracy. Despite this, task achievement accuracy is higher in the passive method (e.g., 71% vs. 43% for subject S2) as a single correct ErrP classification enables autonomous obstacle avoidance, whereas SSVEP requires multiple accurate commands. Conclusions: This research highlights the trade-offs between accuracy, mental load, and engagement in BCI-based robot control. The findings support the development of more intuitive assistive robotics, particularly for disabled and elderly users. Full article

Graph-based retrieval-augmented generation (GraphRAG) represents an innovative advancement in natural language processing, leveraging the power of large language models (LLMs) for complex tasks such as policy generation. This research presents a GraphRAG model trained on YouTube data containing keywords related to logistics issues to generate policy proposals addressing these challenges. The collected data include both video subtitles and user comments, which are used to fine-tune the GraphRAG model. To evaluate the effectiveness of this approach, the performance of the proposed model is compared to a standard generative pre-trained transformer (GPT) model. The results show that the GraphRAG model outperforms the GPT model in most prompts, highlighting its potential to generate more accurate and contextually relevant policy recommendations. This study not only contributes to the evolving field of LLM-based natural language processing (NLP) applications but also explores new methods for improving model efficiency and scalability in real-world domains like logistics policy making. Full article

Bioplastics have been presented as a sustainable alternative to products derived from fossil sources. In response, industries have developed innovative products using biopolymers across various sectors, such as food, packaging, biomedical, and construction. However, consumer acceptance remains crucial for their widespread adoption. This study aims to explore public sentiment toward bioplastics, focusing on emotions expressed on Reddit. A dataset of 5041 Reddit comments was collected using keywords associated with bioplastics and the extraction process was facilitated by Python-based libraries like pandas, NLTK, and NumPy. The sentiment analysis was conducted using the NRCLex, a broadly used lexicon. The overall findings suggest that trust, anticipation, and joy were the most dominant emotions in the time frame 2014–2024, indicating that the public emotional response towards bioplastics has been mostly positive. Negative emotions such as fear, sadness, and anger were less prevalent, although an intense response was noted in 2018. Findings also indicate a temporal co-occurrence between significant events related to bioplastics and changes in sentiment among Reddit users. Although the representativeness of the sample is limited, the results of this study support the need to develop real-time monitoring of the public’s emotional responses. Thus, it will be possible to design communication campaigns more aligned with public needs. Full article