Search Results (422)

User-generated videos (UGVs) and professional-generated videos (PGVs) have become important information sources in movie marketing, yet prior research has largely examined user-generated content and professional-generated content as independent drivers of market performance. This study investigates how UGV influences movie box office revenue through PGV and identifies the boundary conditions of this process. Drawing on signaling theory and the elaboration likelihood model, we propose that UGV volume generates social attention and stimulates PGV production, while PGV serves as a more credible quality signal that affects consumers’ viewing decisions. Using panel data on 226 movies released in China from 2024 to 2025 and 245,890 videos collected from Weibo, we test the proposed framework with fixed-effects models, endogeneity tests, and robustness checks. The results show that UGV volume indirectly increases box office revenue through PGV volume. Moreover, UGV creator reputation strengthens the positive relationship between UGV volume and PGV volume, and PGV perceived usefulness strengthens the positive effect of PGV volume on box office revenue. These findings reveal a sequential pathway through which user-generated social attention is transformed into professional market persuasion, offering theoretical and managerial implications for video-based interactive marketing. Full article

The integration of Generative AI (GenAI) into non-STEM education introduces cognitive demands that challenge static interpretations of technology acceptance. Evidence on GenAI acceptance is drawn largely from cross-sectional designs applied to general-purpose academic tasks and for computing students who already hold prior technical knowledge, leaving unresolved how acceptance perceptions evolve when non-technical students confront sustained high-load programming work. To address this gap, a pre-test/post-test randomized field experiment with repeated measures was conducted with 117 Chilean Accounting and Auditing undergraduates assigned to a control group (instructional videos; n = 57) or an experimental group (Google Gemini 1.5; n = 60). Participants completed Java programming exercises that increased in cognitive complexity, ranging from basic conditionals to vector operations. The study did not assess objective learning outcomes; instead, it tracked changes in user perceptions through a hybrid motivational framework integrating Perceived Ease of Use and Behavioral Intention with Perceived Enjoyment and Value/Usefulness. Baseline results showed lower initial Perceived Ease of Use in the GenAI group, consistent with initial cognitive friction. Post-intervention analyses indicated that the control group showed a significant increase only in Behavioral Intention, whereas the experimental group showed statistically significant within-group gains across all measured dimensions. These findings suggest that, for non-STEM students, the perceived ease and value of GenAI are not necessarily immediate but may develop through sustained interaction with cognitively demanding tasks. The study contributes to technology acceptance research by framing ease of use as an acquired proficiency in high-friction GenAI learning environments. Full article

Over the last few years, TikTok has become one of the most notable social networking apps, allowing users to upload videos with the possibility of gaining popularity. This has given rise to the emergence of influencers native to the platform. While Global North–focused TikTok studies have empirically examined the media economics of this platform in various capacities, emerging markets have experienced a research dearth in this regard. To understand the strategies they employ to monetize, as well as the challenges they face in generating revenue, this study interviewed 23 Nigerian TikTok influencers between the ages of 19 and 26. We find that Nigerian TikTok influencers rely on a mixture of brand partnerships, sponsored promotions, referral systems, cross-platform visibility, collaborative growth strategies, and account-region bypass to generate income within a platform environment that offers limited direct monetization opportunities to creators. The findings further show that influencer labour in the Nigerian context is shaped by unstable payment systems, algorithmic dependence, brand distrust, and the constant pressure to remain visible and relevant. Beyond economic concerns, the study reveals that creators experience emotional strain arising from online harassment, body shaming, and the psychological demands of continuous self-presentation. Full article

Acoustic side-channel attacks (ASCAs) exploit unintended sound emitted by keyboards to infer typed input, but existing methods generally assume manually labelled training data and controlled environments, limiting their applicability to realistic scenarios such as online lectures. We develop a pipeline that automatically labels keystroke-sound samples captured from online coding tutorials: video frames are processed with optical character recognition (OCR) to extract the ground-truth character sequence, audio is segmented into clips centred on detected click events, and the two streams are aligned. A convolutional neural network (CNN) is trained on mel-spectrogram features, with transfer learning used to adapt the pretrained model to a target user with minimal samples. The classifier is trained on all 68 physical keys present in the recordings; of these, 50 produce a character or whitespace and the remaining 18 are control, navigation, and modifier keys. On a held-out test set, the CNN achieves 98.1% top-1, 99.4% top-2, and 100% top-3 accuracy. Transfer learning retains strong performance with as few as 13 samples per key. Pairing OCR-derived ground truth with acoustic CNN classification removes the labelling bottleneck that has limited previous ASCAs, and the transfer-learning stage makes the attack viable with minimal per-victim data. All code, trained models, and labelled datasets are released to support reproducible research. Full article

The micro-excavation of funerary urns is a specialised archaeological procedure that requires technical expertise and access to fragile cultural heritage materials. Opportunities for hands-on training are limited due to the scarce availability of archaeological materials for educational purposes and the conservation requirements of archaeological remains. Digital technologies provide alternative approaches for supporting archaeological education while preserving original artefacts. This work presents an interactive web-based application for training in the micro-excavation of funerary urns, developed as part of the European ArchaeoPills project. The application builds on a previously developed Augmented Reality (AR) learning tool and guides learners through the complete funerary urn micro-excavation process within a virtual laboratory environment. To investigate the impact of different learning modalities in cultural heritage education, the web application was evaluated alongside an instructional video generated from a previously developed virtual reality application, providing access to the same training content without requiring immersive technologies. The results were also compared with those previously obtained using the AR version. Learning outcomes, perceived workload, and user experience were compared across the different conditions. Both the instructional video and the web application produced significant learning gains, and no significant differences were observed between the two modalities. The learning outcomes obtained with the video-based resource and the web application did not differ significantly from those of the AR version. Both the instructional video and the web application were perceived as accessible and useful educational resources. These findings suggest that video-based, web-based, and AR-based resources developed from the same archaeological content and educational materials can all effectively support learning. However, further studies with larger samples are needed to confirm these findings. The choice among these resources should therefore be guided not only by learning effectiveness but also by instructional objectives, technological resources, and the desired level of learner interaction. Full article

Generative artificial intelligence (GenAI) now produces synthetic text, images, audio, and video at a quality and cost that place convincing synthetic fabrication within reach of non-specialist users. Deepfakes, synthetic media that alter a person’s appearance, voice, or behavior through machine learning (ML), are one of the most contested applications of this capability, yet public willingness to accept them under regulation remains less understood. This study examines how perceived benefits, perceived risks, privacy concerns, and demographic characteristics relate to trust in deepfake technology under regulatory safeguards. Survey data from 924 respondents from several countries were analyzed using descriptive statistics, independent-samples t-tests, analysis of variance, multiple regression, and thematic analysis of open-ended responses. Respondents recognized the potential benefits of deepfake technology for digital content creation and education while expressing widespread concern about misinformation, privacy violations, and criminal misuse. When respondents evaluated deepfake technology under an assumed privacy-protecting regulatory scenario, perceived risks did not independently predict trust, while perceived benefits were the strongest predictors. The findings indicate that institutional confidence may contribute to public acceptance of beneficial applications of generative AI, although the cross-sectional design does not establish a causal effect of regulation. Full article

Real-world home monitoring requires sensing systems that can capture daily behaviour without continuous raw-video retention or excessive user burden. However, domestic environments present irregular activity timing, fragmented human presence, asynchronous multimodal events, and privacy-sensitive data management. This study proposes an event-driven multimodal sensing and computing framework for context-aware home monitoring using stereo vision and dietary event anchoring. The framework integrates stereo RGB-based three-dimensional human motion sensing, dining-zone-triggered meal image acquisition, runtime event orchestration, timestamp-based cross-modal synchronization, privacy-aware local storage, and large-language-model-assisted dietary context interpretation. Instead of continuously recording all sensor streams, the system activates and organizes sensing through human presence detection, debounce logic, cooldown-based session control, and dining-zone occupancy events. Meal-related events are used as contextual anchors to associate motion sessions and dietary observations into synchronized behavioural episodes. The prototype was deployed for 11 consecutive days in a real kitchen–dining environment, with the stabilized real-time monitoring phase evaluated from 11 to 14 February 2026. During this phase, the system generated 26 event-driven motion sessions and 51,165 captured pose frames, of which 25,925 were valid. Sustained active sessions accounted for 30.8% of all sessions but contributed 81.5% of captured pose frames, indicating that event-driven orchestration concentrated motion data within behaviourally meaningful activity windows. Eight meal-related records were obtained, seven of which overlapped with motion sessions, resulting in 87.5% meal-event overlap coverage. Structured pose outputs required approximately 550 kB/min, corresponding to about 33 MB/h of recorded pose data. LLM-assisted meal-image interpretation achieved a mean absolute percentage error of 25.44%, supporting its use for coarse dietary-context description rather than precise nutritional quantification. However, this result is interpreted only as evidence for coarse dietary-context description and not as validation of a precise nutritional or clinical dietary assessment method. These results demonstrate the system-level feasibility of transforming irregular domestic observations into structured, temporally indexed, and privacy-aware multimodal behavioural records for future home monitoring applications. Full article

The new generation is growing up in a context where sustainability and climate change (CC) are considered the “issues of our time”. In line with this, education plays a crucial role in raising awareness of CC among young people. However, one of the main challenges is showing CC’s effects, as they are often perceived as temporally and spatially distant, and many individuals struggle to visualise the real impact that CC’s consequences will have on the future world. The present study describes the co-design of a 360° video showing the effects of CC in familiar environments, conducted with a class of pre-teens. Co-design is a method that enables end-users, who are not trained in design, to work alongside professionals in creative processes. Moreover, participating in collaborative workshops reduces the distance between users and the topic addressed, enhancing their engagement and awareness of it. The results show that participants were satisfied with the developed content and enjoyed taking part in the co-design process. These findings suggest the potential of participatory workshops for developing 360° videos with students and provide a foundation for future investigations about their educational impact. Full article

Motorcyclists are among the most vulnerable road users at roundabouts, particularly in mixed-traffic environments where rider behaviour may be influenced by geometric design. This study investigates the association between roundabout geometry and risky motorcyclist behaviour using the Chi-squared Automatic Interaction Detection (CHAID) method. Video-based observations were conducted at four selected roundabouts in Kuching, Sarawak, Malaysia, generating 15,937 risky-behaviour events from 5400 observed motorcyclists. Six risky-behaviour categories were analysed, covering entry, circulation, and exit manoeuvres. The CHAID results showed that entry radius was the primary geometric factor associated with risky motorcyclist behaviour, while exit radius and exit width acted as secondary variables under specific entry-radius conditions. Four behavioural scenarios were identified. Entry radii of 15–32 m were associated with mixed risky-behaviour patterns, with lane splitting during circulation being the most frequent behaviour. Entry radii of 37–40 m combined with exit radii ≤ 12.43 m were associated with failure to signal before exiting. Entry radii of 43–49 m combined with exit radii ≤ 25.3 m were associated with improper lane positioning when exiting. Larger entry radii of 49–64 m combined with exit widths of 7.38–9.06 m were associated with close stopping or potential blind-zone positioning. The model validation results indicated moderate internal classification performance, supporting the use of CHAID as an interpretable threshold-identification tool rather than a high-precision predictive model. The findings demonstrate that risky motorcyclist behaviour at roundabouts is shaped by non-linear interactions between entry and exit geometric elements. From a sustainability perspective, these results provide preliminary evidence for behaviour-sensitive roundabout design, safety assessment, and policy-oriented geometric improvements that support safer and more inclusive urban transport systems in motorcycle-dominant mixed-traffic contexts. Full article

Many politicians heavily leverage social media platforms to further their interests and professional goals. TikTok, the widely popular social network established in 2016, has amassed a vast user following, especially among the younger demographic known as Generation Z. This study conceptualizes it as part of a platformized political communication environment that enables direct-to-audience communication beyond traditional journalistic gatekeeping. The current study aims to analyze high-visibility TikTok content produced by active Israeli politicians by scrutinizing the best-performing videos created by politicians representing diverse political ideologies. High-visibility content refers to videos that generated relatively high engagement, exposure, and platform presence, and therefore had greater potential to influence or impact audiences compared with less visible posts. The goal is to characterize and understand the content patterns and visibility dynamics of these successful TikTok videos posted by politicians. The findings suggest that the popularity of TikTok videos posted by politicians is associated with gender, age, and political affiliation. Men, older individuals, and those with right-wing affiliations tend to create content that garners the highest levels of popularity. Moreover, a considerable proportion of the sampled high-visibility videos do not engage directly with political subject matters. This suggests that, among best-performing political TikTok posts, visibility and engagement may sometimes outweigh explicit political or ideological discourse. This implies that politicians feel compelled to participate on the platform, even when their most visible content does not specifically revolve around promoting political messages. Consequently, the findings lend cautious support to the view that platform logic shapes political visibility and communication practices, even in politicians’ use of TikTok. In doing so, this study contributes to understanding how commercial social media platforms such as TikTok shape political visibility, engagement, and direct-to-audience communication. Full article

Artificial intelligence (AI) has rapidly emerged as a transformative tool in virology, offering new opportunities for the detection, classification, and surveillance of viral pathogens. Recent advances in machine learning, deep neural networks, and multimodal data analysis now enable the identification of viral signatures from genomic sequences, medical images, environmental samples, and social-media-derived epidemiological signals. This review provides a comprehensive overview of state-of-the-art AI methodologies applied to viral pathogen research, with a particular focus on image-based diagnostics, automated quality assessment of virology-related digital content, and predictive modelling for outbreak monitoring. We discuss how convolutional and transformer-based architectures are being used to classify infected tissues, detect viral particles, and support laboratory workflows. Furthermore, we highlight the emerging role of AI in evaluating the reliability of user-generated images and short videos related to infectious diseases, an area increasingly relevant in the age of misinformation. Challenges such as dataset bias, limited annotated virological images, ethical concerns, and the need for standardized quality-assessment pipelines are critically examined. Finally, we outline future research directions, including hybrid AI–biological models, AI-supported viral surveillance in healthcare environments, and the integration of explainable AI to enhance clinical trust. Full article

The current study presents the design, implementation, and evaluation of a real-time face recognition system for automated access control. The system uses Python libraries to build an accurate and secure identification platform that incorporates dedicated stages for facial data processing and recognition. During data preparation, 128-dimensional facial embedding vectors are generated for authorized users through a command-line interface and protected using authenticated encryption. In real-time operation, the system captures video frames, detects faces, and verifies identities by matching them against the encrypted database. Experimental results demonstrate high recognition accuracy, real-time throughput, and robust performance, highlighting the system’s suitability for GDPR-oriented deployment in small institutional environments. Full article

Beginning with the notion that short-form video sites represent key digital commerce destinations for food influencers in general, there is very little research explaining how specific persuasion cues lead to varying degrees of consumer engagement. Based upon the Elaboration Likelihood Model (ELM), Social Proof theory and the COBRA framework, this study aims to understand whether argument quality as a central-route cue and perceived emotional appeal as a peripheral-route cue affect consumption, contribution, creation engagement behaviors. Additionally, the study will determine if perceived post popularity, a platform-generated social proof signal, affects these relationships. A survey was conducted of 386 users of short food influencer videos. PLS-SEM analysis was performed on the survey data. Results indicated that both argument quality and perceived emotional appeal positively influence all three engagement levels. Perceived emotional appeal exerts a stronger effect, particularly on higher-engagement behaviors. The moderating effects of perceived post popularity were limited and applied solely to contribution, where it weakened the positive relationship between argument quality and contribution but strengthened the positive relationship between perceived emotional appeal and contribution. This study builds upon existing knowledge of how consumers respond to food influencer content by exploring differences between types of engagement and integrating platform-generated signals within an ELM-based theoretical model. Full article

Prolonged short-video engagement in the generative-AI era may be shaped by interface cues that encourage or interrupt repeated continuation decisions in algorithmic feeds. This study examines whether AI-generated content disclosure functions as interface-level digital friction for prolonged short-video engagement among late-adolescent and emerging-adult TikTok users. Prolonged watching intention is treated as a cognitive-behavioral proximal tendency relevant to problematic social media use (PSMU), rather than as a clinical diagnosis or an emotional-disturbance outcome. Drawing on the heuristic-systematic model, we tested a dual-pathway risk-trust model in which disclosure directly affects prolonged watching intention, while perceived risk and content trust operate as mediators and AI literacy operates as a person-level boundary condition. An online between-subjects experiment was conducted with 720 valid participants aged 18–24. Disclosure had a positive direct effect on prolonged watching intention, suggesting that AI labels can initially work as salient curiosity and novelty cues. At the same time, disclosure increased perceived risk and reduced content trust, generating negative indirect pathways that constrained prolonged watching intention. AI literacy strengthened both appraisal pathways. The findings reposition AI disclosure from a mere transparency notice to a behavioral cue that can simultaneously attract attention and activate protective appraisal. They contribute to developmental and media-psychological research on prolonged engagement and PSMU-relevant mechanisms without overstating clinical implications. Full article

Background/Objectives: Short-form motion graphics are increasingly used in digital health communication. However, limited research has examined their accessibility, interaction quality, and usability for older adults. This study explores the design and evaluation of short-form motion graphics as human–computer interaction systems for depression-related health communication in aging populations. Methods: A data-informed and human-centered approach was adopted, integrating clustering-based analysis, expert evaluation, and user-based assessment. Short-form motion graphics videos and user-generated comments were analyzed to identify design-related themes associated with accessible digital health communication. These insights informed the development of motion graphics prototypes. The evaluation involved independent expert groups and 200 older adult participants. Cognitive load and usability were assessed using a structured questionnaire. Results: The clustering analysis showed moderate cluster separation and provided an exploratory source of design insights. Expert evaluation highlighted the importance of visual clarity, structured content organization, and appropriate motion pacing. User evaluation yielded a mean usability score of 3.95 and a mean cognitive load score of 3.72, indicating generally positive perceptions of the developed motion graphics among participants. Conclusions: The findings suggest that combining data-informed analysis, expert review, and user evaluation may be useful for designing and assessing digital health communication systems for older adults. As this study was exploratory and did not include a control group, the findings should be interpreted within the context of the study and should not be considered evidence of causal relationships. Full article