Search Results (238)

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

Accurate and real-time classification of low-latency Internet traffic is critical for applications such as video conferencing, online gaming, financial trading, and autonomous systems, where millisecond-level delays can degrade user experience. Existing methods for low-latency traffic classification, reliant on raw temporal features or static statistical analyses, fail to capture dynamic frequency patterns inherent to real-time applications. These limitations hinder accurate resource allocation in heterogeneous networks. This paper proposes a novel framework integrating wavelet transform (WT) and artificial neural networks (ANNs) to address this gap. Unlike prior works, we systematically apply WT to commonly used temporal features—such as throughput, slope, ratio, and moving averages—transforming them into frequency-domain representations. This approach reveals hidden multi-scale patterns in low-latency traffic, akin to structured noise in signal processing, which traditional time-domain analyses often overlook. These wavelet-enhanced features train a multilayer perceptron (MLP) ANN, enabling dual-domain (time–frequency) analysis. We evaluate our approach on a dataset comprising FTP, video streaming, and low-latency traffic, including mixed scenarios with up to four concurrent traffic types. Experiments demonstrate 99.56% accuracy in distinguishing low-latency traffic (e.g., video conferencing) from FTP and streaming, outperforming k-NN, CNNs, and LSTMs. Notably, our method eliminates reliance on deep packet inspection (DPI), offering ISPs a privacy-preserving and scalable solution for prioritizing time-sensitive traffic. In mixed-traffic scenarios, the model achieves 74.2–92.8% accuracy, offering ISPs a scalable solution for prioritizing time-sensitive traffic without deep packet inspection. By bridging signal processing and deep learning, this work advances efficient bandwidth allocation and enables Internet Service Providers to prioritize time-sensitive flows without deep packet inspection, improving quality of service in heterogeneous network environments. Full article

Process control and quality assurance of wire + arc additive manufacturing (WAAM) and automated welding rely heavily on in-process monitoring videos to quantify variables such as melt pool geometry, location and size of droplet transfer, arc characteristics, etc. To enable feedback control based upon this information, an automatic and robust segmentation method for monitoring of videos and images is required. However, video segmentation in WAAM and welding is challenging due to constantly fluctuating arc brightness, which varies with deposition and welding configurations. Additionally, conventional computer vision algorithms based on greyscale value and gradient lack flexibility and robustness in this scenario. Deep learning offers a promising approach to WAAM video segmentation; however, the prohibitive time and cost associated with creating a well-labelled, suitably sized dataset have hindered its widespread adoption. The emergence of large computer vision models, however, has provided new solutions. In this study a semi-automatic annotation tool for WAAM videos was developed based upon the computer vision foundation model SAM and the video object tracking model XMem. The tool can enable annotation of the video frames hundreds of times faster than traditional manual annotation methods, thus making it possible to achieve rapid quantitative analysis of WAAM and welding videos with minimal user intervention. To demonstrate the effectiveness of the tool, three cases are demonstrated: online wire position closed-loop control, droplet transfer behaviour analysis, and assembling a dataset for dedicated deep learning segmentation models. This work provides a broader perspective on how to exploit large models in WAAM and weld deposits. Full article

This study explores the integration of digital media tools—specifically TikTok, online press news analysis, and digital portfolios—into pre-service chemistry teacher education to enhance student engagement, foster conceptual understanding, and highlight the relevance of chemistry in society. The educational intervention involved 138 pre-service teachers who analysed digital news articles to reflect on the societal and environmental implications of chemistry, promoting media literacy and awareness of socioscientific issues. Additionally, they created short-form TikTok videos, using social media to communicate scientific concepts creatively and interactively. All participants compiled their work into digital portfolios, which served as both a reflective and integrative tool. A post-course Likert-scale questionnaire (N = 77) revealed high overall satisfaction with the methodology, with 94.8% valuing the news analysis activity and 59.7% finding TikTok particularly engaging. Despite some limitations regarding access to technical infrastructure, the findings indicate that incorporating Information and Communication Technology (ICT) in this manner supports motivation, meaningful learning, and the development of key teaching competencies. This case study contributes practical insights into ICT use in science education. Full article

Objectives: The increasing reliance on online platforms for dental education necessitates an assessment of the quality and reliability of available resources. This study aimed to evaluate YouTube videos as educational tools for Class IV resin composite restorations. Methods: The first 100 YouTube videos were screened, and 73 met the inclusion criteria. The videos were evaluated using the Video Information and Quality Index (VIQI) and specific content criteria derived from the dental literature. Videos with a score below the mean were identified as low-content videos. Results: No significant differences were noted between high- and low-content videos when examining the number of views, number of likes, duration, days since upload, viewing rate, interaction index, and number of subscribers (p > 0.05). The high-content videos demonstrated higher mean values compared with the low-content videos in flow (4.11 vs. 3.21; p < 0.0001), accuracy (4.07 vs. 3.07; p < 0.0001), quality (4 vs. 2.66; p < 0.0001), and precision (4.16 vs. 2.86; p < 0.0001). The overall VIQI score was significantly higher (p < 0.0001) for high-content videos (Mean 16.34; SD 2.46) compared with low-content videos (Mean 11.79; SD 2.96). For content score, high-content videos (Mean 9.36; SD 1.33) had a higher score (p < 0.0001) than low-content videos (Mean 4.90; SD 2.04). The key areas lacking sufficient coverage included occlusion, shade selection, and light curing techniques. Conclusions: While a significant portion of YouTube videos provided high-quality educational content, notable deficiencies were identified. This analysis serves as a call to action for both content creators and educational institutions to prioritize the accuracy and completeness of online dental education. Full article

This study investigates the relationship between gender and global media consumption habits, platform preferences, and engagement types. Prior research suggests that gender significantly influences media preferences, with men being traditionally engaged with news, action-oriented content, and competitive media, and women with entertainment, lifestyle, and social media-driven content. The current primary quantitative research is based on a questionnaire that was distributed online and answered by 292 respondents, equally distributed between men and women. The research findings showed that men are more likely to consume news, video games, and discussion-based media. On the other hand, women showed higher engagement with entertainment content, music, and listening-based media. Pearson’s correlation analysis, conducted using SPSS (v23), further confirmed these gendered preferences. Despite the persistence of traditional gendered media patterns, this research also identified the areas in which digital platforms seem to facilitate more balanced engagement across the two genders. Full article

Aquatic ecosystems are essential for biodiversity and ecosystem services, but anthropogenic pressures threaten them. In this context, citizen science has emerged as an innovative strategy for biodiversity conservation and environmental monitoring. We conducted a scientometric analysis to identify patterns and gaps in the scientific literature on citizen science in aquatic ecology. We analyzed 185 articles published between 2003 and 2024 on the Web of Science and Scopus databases, with the highest number of publications on the topic (15.14%) in 2023. The United States, Australia, and the United Kingdom were the most productive and frequently studied countries. Studies focused on marine ecosystems (50.28%), while freshwater environments, such as rivers (12.99%), remain under-represented. Taxonomic groups such as fish (30.64%) and aquatic mammals (13.87%) were most commonly studied. The focus of monitoring was on ecology and species conservation. The projects adopted a contributory model of citizen engagement (92.97%), with a predominance of urban citizens (60.51%). Participants were trained through online platforms (25.75%) and in-person courses (21.56%), while communication methods involved sharing photos and videos (38.77%) and online uploads (23.79%). Therefore, expanding studies on freshwater ecosystems and rural and traditional communities, and integrating different levels of citizen participation, is essential. 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

Video-sharing platforms have significantly influenced social justice movements by creating unprecedented opportunities for mobilization and support. However, YouTube’s unique role and platform culture in facilitating social justice movements remain relatively understudied. This research addresses this gap by analyzing video content related to two prominent online social justice movements: #BLM and #StopAAPIHate. We conducted a comprehensive thematic analysis of a dataset comprising 489 videos obtained using the YouTube Data API. Thematic categories were developed to explore the identities of video creators, the type of information conveyed, storytelling techniques, and promotional features utilized. Our findings indicate that public figures, vloggers, and news reporters are the most frequent creators of videos supporting these movements. The primary purpose of these videos is to share movement-related knowledge and personal stories of discrimination. Most creators primarily promote their social media accounts and do not extensively utilize platform features such as live streaming, merchandise sales, donation requests, or sponsorships to actively support these social justice initiatives. Full article

The COVID-19 pandemic increased the number of video conferences, for example, through online teaching and home office meetings. Even in the medical environment, consultation sessions are now increasingly conducted in the form of video conferencing. This includes sessions between psychotherapists and one or more call participants (individual/group calls). To subsequently document and analyze patient conversations, as well as any other human–human dialog, it is possible to record these video conferences. This allows experts to concentrate better on the conversation during the dialog and to perform analysis afterward. Artificial intelligence (AI) and its machine learning approach, which has already been used extensively for innovations, can provide support for subsequent analyses. Among other things, emotion recognition algorithms can be used to determine dialog participants’ emotions and record them automatically. This can alert experts to any noticeable sections of the conversation during subsequent analysis, thus simplifying the analysis process. As a result, experts can identify the cause of such sections based on emotion sequence data and exchange ideas with other experts within the context of an analysis tool. Full article

The present study explores the emerging role of “public affairs live streamers” on the short video platform Kuaishou, examining how these individuals have evolved into new forms of rural elites, expanding opportunities for civic participation and rural governance in China. Through content analysis and case studies, the research identifies six key themes central to the streamers’ discourse: land and property issues, financial aid and subsidies, policy and governance, advocacy for the public, educational outreach, and customs. These themes underscore their role as rural knowledge elites who bridge the gap between the government and rural residents through digital platforms. The study further examines the mechanisms behind the emergence of “public affairs live streamers”. Empowered by the platform, these streamers effectively convert their cultural capital into online social capital, which may then be partially transformed into offline social influence and economic returns. However, their empowerment is constrained by the platform’s algorithm distribution mechanisms and opaque content regulation, making the conversion of capital unstable, traffic-dependent, and challenging to sustain. Linking to the broader socio-political landscape, the study delves into the empowering role of “public affairs live streamers” in society. These online rural elites blend knowledge with digital skills to facilitate public engagement and inspire civic awareness in rural areas through their activities. Nonetheless, their contribution to rural governance and civilization is often limited to informal mediation and emotional expression, with limited capacity to foster rational discourse or deeper improvements in rural governance. Overall, this study contextualizes the rise of rural elites within China’s rapid technological and rural development, offering a fresh perspective on how digital media can supplement governance strategies and enhance grassroots’ civic participation. Based on this analysis, the study proposes recommendations for improving platform governance and integrating public affairs streamers into broader participatory frameworks, thereby stabilizing their empowering effects and promoting sustainable rural governance. Full article

The Korean popular music (K-pop) industry, with its global popularity and increasing multilingual orientation, serves as a suitable context for exploring language perceptions. This research examines the metalinguistic commentary on K-pop idols’ English accents on YouTube. Specifically, we investigate how online users evaluate the idols’ English accents and how their metalinguistic comments communicate linguistic ideologies that favor a “native” way of speaking. Our dataset consists of 602 metalinguistic comments drawn from four popular YouTube videos featuring the evaluation of K-pop idols’ accents. We employ content analysis to first categorize comments into positive, negative, and neutral evaluations, then focus on aspects being evaluated in the users’ comments (e.g., social attractiveness and (non-)nativeness). The results indicate that a vast majority of comments (88.1%) convey positive evaluations, largely associating the idols’ accents with social appeal and native-like accents. Although a few neutral and negative evaluations exist, our result shows a dominant preference for inner circle accents and complex attitudes toward accented speech in digital spaces. We conclude by highlighting the influence of digital platforms in shaping language perceptions and the implications for linguistic stereotyping in the context of K-pop culture. Full article

The growing popularity of online learning brings with it inherent challenges that must be addressed, particularly in enhancing teaching effectiveness. Artificial intelligence (AI) offers potential solutions by identifying learning gaps and providing targeted improvements. However, to ensure their reliability and effectiveness in educational contexts, AI models must be rigorously evaluated. This study aimed to evaluate the performance and reliability of an AI model designed to identify the characteristics and indicators of engaging teaching videos. The research employed a design-based approach, incorporating statistical analysis to evaluate the AI model’s accuracy by comparing its assessments with expert evaluations of teaching videos. Multiple metrics were employed, including Cohen’s Kappa, Bland–Altman analysis, the Intraclass Correlation Coefficient (ICC), and Pearson/Spearman correlation coefficients, to compare the AI model’s results with those of the experts. The findings indicated low agreement between the AI model’s assessments and those of the experts. Cohen’s Kappa values were low, suggesting minimal categorical agreement. Bland–Altman analysis showed moderate variability with substantial differences in results, and both Pearson and Spearman correlations revealed weak relationships, with values close to zero. The ICC indicated moderate reliability in quantitative measurements. Overall, these results suggest that the AI model requires continuous updates to improve its accuracy and effectiveness. Future work should focus on expanding the dataset and utilise continual learning methods to enhance the model’s ability to learn from new data and improve its performance over time. Full article

Massive Open Online Courses (MOOCs) have become increasingly prevalent in higher education, with the COVID-19 pandemic further accelerating their integration, particularly in developing countries. While MOOCs offered a vital solution for educational continuity during the pandemic, factors influencing students’ sustained engagement with them remain understudied. This longitudinal study examines the factors influencing learners’ sustained engagement with ThaiMOOC, incorporating demographic characteristics, usage log data, and key predictors of adoption and completion. Our research collected primary data from 841 university students who enrolled in ThaiMOOC as a mandatory curriculum component, using online surveys with open-ended questions and post-course usage log analysis. Logistic regression analysis indicates that adoption intention, course content, and perceived effectiveness significantly predict students’ Actual Continued Usage (ACU). Moreover, gender, prior MOOC experience, and specific usage behaviors emerge as influential factors. Content analysis highlights the importance of local language support and the desire for safety during the COVID-19 pandemic. Key elements driving ACU include video design, course content, assessment, and learner-to-learner interaction. Full article

With the rapid expansion of online shopping, website design has become a critical factor influencing user experience and consumer satisfaction. This study examines the relationship between visual marketing elements embedded in e-commerce web page design and consumer satisfaction by analyzing 1500 product pages across five major categories (furniture, small items, food, home appliances, and clothing) on the Japanese platform Rakuten. The analysis reveals that in the furniture category, a higher proportion of images featuring visible faces and video explanations demonstrated positive correlations with consumer satisfaction. For food products, text color diversity demonstrated positive correlations with consumer satisfaction. In the home appliance category, text color and video explanations showed positive correlations with satisfaction, while the number of human images and video music showed negative correlations. For accessory products, images positioned at the website periphery, the number of human images, and video music showed negative correlations with consumer satisfaction. In the apparel category, text color and the number of human images demonstrated negative correlations with consumer satisfaction. However, in the analysis of the entire sample, no significant correlations were observed between visual marketing elements and consumer satisfaction. These findings suggest that visual marketing strategies should be tailored to specific product categories, which may contribute to improving consumer satisfaction with e-commerce platforms. Full article