Search Results (86)

This study presents the e[kent-im] model, a map-based crowdsourcing initiative that digitizes and safeguards urban memory and cultural heritage through community participation and digital tools. The model facilitates the collection, archiving, and dissemination of urban memories by fostering intergenerational knowledge transfer and encouraging civic engagement in heritage preservation. Implemented in the historical center of Kütahya/Türkiye, the project gathered 150 memories and stories from 12 senior participants aged 50–85, which were linked to 303 historical visuals sourced from personal archives. These materials were integrated into a custom-designed web and mobile interface (Mapotic Pro) enriched with metadata categories such as type, period, and location, enabling users to filter and navigate content effectively and watch the videos enriched with participant narratives. A digital city archive matrix was also developed to systematically organize the collected data and support the web-based platform. To assess the platform’s effectiveness, a pilot study with 15 young participants aged 18–28 was conducted. During a self-guided city tour, participants engaged with historical content on the platform and provided feedback through pre- and post-test evaluations. Results indicated heightened awareness of and interest in cultural heritage, demonstrating the model’s potential as both an interactive archive and a tool facilitating intergenerational heritage awareness. Overall, this study highlights the model’s adaptability, scalability, and capacity to bridge generational and technological divides. Full article

Romanian popular music has had a storied history across the last century and a half. Incorporating different influences at different times, today it boasts a wide range of both autochthonous and imported genres, such as traditional folk music, rock, rap, pop, and manele, to name a few. We aim to trace the linguistic differences between the lyrics of these genres using natural language processing and a computational linguistics approach by studying the prosodic, stylistic, syntactic, and sentiment-based features of each genre. For this purpose, we have crawled a dataset of ~14,000 Romanian songs from publicly available websites along with the user-provided genre labels, and characterized each song and each genre, respectively, with regard to these features, discussing similarities and differences. We improve on existing tools for Romanian language natural language processing by building a lexical analysis library well suited to song lyrics or poetry which encodes a set of 17 linguistic features. In addition, we build lexical analysis tools for profanity-based features and improve the SentiLex sentiment analysis library by manually rebalancing its lexemes to overcome the limitations introduced by it having been machine translated into Romanian. We estimate the accuracy gain using a benchmark Romanian sentiment analysis dataset and register a 25% increase in accuracy over the SentiLex baseline. The contribution is meant to describe the characteristics of the Romanian expression of autochthonous as well as international genres and provide technical support to researchers in natural language processing, musicology or the digital humanities in studying the lyrical content of Romanian music. We have released our data and code for research use. Full article

In the context of an increasingly hostile national political environment and federal cuts to refugee resettlement programs in the United States, advocates often highlight the economic contributions of immigrants and refugees to garner local support, especially in regions with histories of economic and population decline. While these narratives continue to be a centerpiece of pro-immigrant and -refugee advocacy, in practice advocates and refugees themselves use a diverse set of frames to promote belonging. In this paper, we examine pro-refugee advocacy frames in a small, nontraditional destination in the Midwest. We draw on survey and focus group research with young adult refugees and nonprofit advocates and content analysis of online stories about refugees. We found that pro-refugee values frames (humanitarian and faith-based) and contributions frames (economic, cultural and civic) coexisted across the local landscape and were used by not only nonprofit advocates and local officials, but also by refugees themselves. While advocacy groups emphasized the dominant frame highlighting refugees’ economic contributions, they were also strategic in using overlapping frames to highlight a less public frame, refugees’ contributions to civic engagement through community service and volunteering. Advocates tended to reproduce the economic contributions frame to appeal to key stakeholders, which consequently obscures refugees’ diverse contributions, but we argue that refugee self-advocates’ use of the civic engagement frame pushes back against economic and other frames that limit their contributions and helps them to create spaces of belonging. Full article

This study investigates the seismic performance of existing reinforced concrete (RC) buildings, focusing on the influence of near-fault ground motions caused by proximity to fault lines. Compared to ordinary or far-fault earthquakes, near-fault earthquakes may have diverse effects on the response of buildings resulting from directivity and intense velocity pulses, which significantly amplify seismic demands. For this purpose, nonlinear time history analyses were carried out on a seven-story RC residential building that was subjected to near-fault effects and sustained heavy damage during the Kahramanmaraş earthquakes on 6 February 2023. The analyses used both near-fault and far-fault ground motion records, and four structural models were developed by gradually reducing the number of shear wall elements to assess the impact of diminishing lateral-load-resisting capacity. The results revealed that near-fault ground motions led to significant increases in base shear, inter-story drift ratios, and structural damage levels. Furthermore, a reduction in shear wall content resulted in a noticeable decline in seismic performance. These findings underscore the necessity of accounting for near-fault effects in seismic design and the critical role of lateral stiffness. The study emphasizes that considering near-fault characteristics is essential for ensuring the seismic resilience of RC buildings located in active seismic zones. Full article

Background: Cognitive biases in information processing, particularly attentional and memory biases, play a crucial role in the development and maintenance of Major Depressive Disorder (MDD). These biases lead individuals with MDD to preferentially attend to and remember negative information, thereby maintaining a depressed mood. A recently proposed attentional resources model suggests that exposure to negative stimuli leads to deeper cognitive processing of subsequent information, regardless of its content. Based on this model, the current study investigated a novel paradigm that manipulated exposure to negative emotional stimuli and examined its effect on information processing and mood improvement. Method: Thirty-eight unmedicated participants with MDD and no comorbid disorders, and 37 healthy controls, completed three blocks of an emotional recall task, which involved watching a short emotional video followed by a recall task of neutral or positive valence stories. Mood changes were assessed throughout the task. Results: Results revealed that both the MDD and HC groups reported improved mood after exposure to a negative emotional video followed by a positive story. Conclusions: These results have important clinical implications. The paradigm may be applied in a broader sense as an active tool that may help to improve mood in depression treatment. Full article

The rise of streaming services and online story-sharing has led to a vast amount of cinema and television content being viewed and reviewed daily by a worldwide audience. It is a unique challenge to grasp the nuanced insights of these reviews, particularly as context, emotion, and specific components like acting, direction, and storyline intertwine extensively. The aim of this study is to address said complexity with a new hybrid Multi Criteria Decision-Making MCDM model that combines the Deck of Cards Method (DoCM) with the Circular Pythagorean Fuzzy Set (CPFS) framework, retaining the symmetry of information. The study is conducted on a simulated dataset to demonstrate the framework and outline the plan for approaching real-world press reviews. We postulate a more informed mechanism of assessing and choosing the most appropriate deep learning assembler, such as the transformer version, the hybrid Convolutional Neural Network CNN-RNN, and the attention-based framework of aspect-based sentiment mapping in film and television reviews. The model leverages both the cognitive ease of the DoCM and the expressive ability of the Pythagorean fuzzy set (PFS) in a circular relationship setting possessing symmetry, and can be applied to various decision-making situations other than the interpretation of media sentiments. This enables decision-makers to intuitively and flexibly compare alternatives based on many sentiment-relevant aspects, including classification accuracy, interpretability, computational efficiency, and generalization. The experiments are based on a hypothetical representation of media review datasets and test whether the model can combine human insight with algorithmic precision. Ultimately, this study presents a sound, structurally clear, and expandable framework of decision support to academicians and industry professionals involved in converging deep learning and opinion mining in entertainment analytics. Full article

Recent breakthroughs in AI-generated content (AIGC) have transformed video creation, empowering systems to translate text, images, or audio into visually compelling stories. Yet reliable evaluation of these machine-crafted videos remains elusive because quality is governed not only by spatial fidelity within individual frames but also by temporal coherence across frames and precise semantic alignment with the intended message. The foundational role of sensor technologies is critical, as they determine the physical plausibility of AIGC outputs. In this perspective, we argue that multimodal large language models (MLLMs) are poised to become the cornerstone of next-generation video quality assessment (VQA). By jointly encoding cues from multiple modalities such as vision, language, sound, and even depth, the MLLM can leverage its powerful language understanding capabilities to assess the quality of scene composition, motion dynamics, and narrative consistency, overcoming the fragmentation of hand-engineered metrics and the poor generalization ability of CNN-based methods. Furthermore, we provide a comprehensive analysis of current methodologies for assessing AIGC video quality, including the evolution of generation models, dataset design, quality dimensions, and evaluation frameworks. We argue that advances in sensor fusion enable MLLMs to combine low-level physical constraints with high-level semantic interpretations, further enhancing the accuracy of visual quality assessment. Full article

The application of artificial intelligence has significantly advanced interactive storytelling. However, current research has predominantly concentrated on the content generation capabilities of AI, primarily following a one-way ‘input-direct generation’ model. This has led to limited practicality in AI story writing, mainly due to the absence of investigations into user-driven creative processes. Consequently, users often perceive AI-generated suggestions as unhelpful and unsatisfactory. This study introduces a novel creative tool named Story Forge, which incorporates a card-based interactive narrative approach. By utilizing interactive story element cards, the tool facilitates the integration of narrative components with artificial intelligence-generated content to establish an interactive story writing framework. To evaluate the efficacy of Story Forge, two tests were conducted with a focus on user engagement, decision-making, narrative outcomes, the replay value of meta-narratives, and their impact on the users’ emotions and self-reflection. In the comparative assessment, the participants were randomly assigned to either the experimental group or the control group, in which they would use either a web-based AI story tool or Story Forge for story creation. Statistical analyses, including independent-sample t-tests, p-values, and effect size calculation (Cohen’s d), were employed to validate the effectiveness of the framework design. The findings suggest that Story Forge enhances users’ intuitive creativity, real-time story development, and emotional expression while empowering their creative autonomy. Full article

This study introduces MediaWatchers4Climate, a methodological framework that leverages machine learning to evaluate the accuracy and rhetorical framing of climate change narratives in Greek online media. The model is designed to analyze large-scale textual data from over 1500 certified digital outlets registered in the Greek Online Media Registry. Through keyword-based filtering, thematic clustering, and content comparison techniques, the framework aims to detect discursive shifts, trace the replication of news stories, and identify misinformation patterns. While the current phase focuses on model development and data structuring, preliminary observations suggest significant content repetition across sources and a lack of original reporting on climate issues. The project ultimately seeks to promote evidence-based reasoning and enhance public resilience to misinformation related to the climate crisis. Full article

The content in this autoethnography manuscript is significant because it takes a different angle than the typical discourse surrounding sport-based youth development (SBYD). Typically, the discourse on SBYD focuses on the positive outcomes of improved social–emotional learning and academic achievement. In using an autoethnographic approach, I share stories from my personal experience as a practitioner in the field to illustrate a new perspective on how to think about the impact of sport-based youth development on young people. Though those outcomes are positive and impactful, they fail to capture the continued inequity in the quality of youth sports programs in underserved communities compared to others. I utilize research in SBYD to analyze each story as a practitioner in the field to thoroughly reflect on my personal experiences and their relation to social justice. The stories are also a tool for making the connection between the individual work of various organizations pursuing sport equity. Through storytelling, reflection, and analysis, I connect the mission of each organization I worked with to the concept of social justice youth development in a more personalized way than numbers and data can illustrate. Additionally, this autoethnography highlights non-traditional sport spaces and advocates for a way to fuse social justice into them. This manuscript seeks to simultaneously refresh the way equity in sport has been looked at, while also illuminating the ways it is already being examined. The paper presents new questions that can be used to better analyze the presence of social justice in youth sports and provides a potential pathway forward by grounding in a definition of quality SBYD programming. These questions imply that the measures of the impact and potential benefits of SBYD may need to be redefined to better match the real lived experiences of individual youth participating in such programs. Full article

Artificial intelligence (AI) is transforming how news is produced, curated, and consumed, challenging traditional gatekeeping theories rooted in human editorial control. We develop a robust theoretical framework to reconceptualize gatekeeping in the AI era. We integrate classic media theories—gatekeeping, agenda-setting, and framing—with contemporary insights from algorithmic news recommender systems, large language model (LLM)–based news writing, and platform studies. Our review reveals that AI-driven content curation systems (e.g., social media feeds, news aggregators) increasingly mediate what news is visible, sometimes reinforcing mainstream agendas, according to Nechushtai & Lewis, while, at other times, introducing new biases or echo chambers. Simultaneously, automated news generation via LLMs raises questions about how training data and optimization goals (engagement vs. diversity) act as new “gatekeepers” in story selection and framing. We found pervasive Simon’s theory that reliance on third-party AI platforms transfers authority from newsrooms, creating power dependencies that may undercut journalistic autonomy. Moreover, adaptive algorithms learn from user behavior, creating feedback loops that dynamically shape news diversity and bias over time. Drawing on communication studies, science & technology studies (STS), and AI ethics, we propose an updated theoretical framework of “algorithmic gatekeeping” that accounts for the hybrid human–AI processes governing news flow. We outline key research gaps—including opaque algorithmic decision-making and normative questions of accountability—and suggest directions for future theory-building to ensure journalism’s core values survive in the age of AI-driven news. Full article

The content in digital intervention therapies for children with attention deficit hyperactivity disorder (ADHD) requires various technical elements to interest and motivate the children. Their structure is often quiz-based, which allows easy access to quantitative assessments. However, in this study, I verify the effectiveness of digital intervention therapy by implementing story-based game content with active participation. In this study, 48 children aged 6 to 13 years diagnosed with ADHD were recruited and assigned to experimental (story-based content) and control (quiz-based content) groups; their attention improvements were compared. The improvement in attention was assessed by comparing the change rate of the Comprehension Attention Test (CAT) and Korean ADHD Rating Scale (K-ARS) scores before and after the intervention. At 4 weeks, the CAT score change rate was significantly different between the groups (p = 0.039, p = 0.040); the CAT score change rate before and after the intervention was significantly greater in the experimental than in the control group (p = 0.038). After adjusting for the baseline, the experimental group showed a significantly greater reduction in the K-ARS impulsivity and total K-ARS scores compared with the control group (p = 0.018, p = 0.012). Therefore, story-based content is more effective than quiz-based content in digital intervention therapy for children with ADHD. Full article

In the 2017 Danzy Senna novel, New People, the mixed-race protagonist is described as a white ‘passing’ mixed-race woman who interprets the death of her adopted Black mother as a symbol of the death of her Black identity. The book’s themes parallel ongoing multiracial political debates that explore the extent to which mixed-race people with proximity to whiteness perceive individual agency in identity negotiations. This paper examines how mixed-race people in Britain discuss the experience of loss and separation, thereby demonstrating how loss and separation interact with their sense of self. Employing a content and thematic analysis of 19 stories from the British-based organisation Mixedracefaces, my findings show that the mixed-race respondents saw their racially marginalised family members as critical connections to their own. Thus, a process of identity de/construction was instigated when they experienced a loss that perpetuated and/or challenged monoracism. I argue that we must disrupt oppressive monoracial paradigms of ‘race’ that uphold monoracial whiteness and prevent mixed-race identity agency. Through mixed-race counterstories, we can reveal further generational histories of struggles, resistance, love, and refusal in Britain. I intentionally provide a safe space for the millions of mixed people looking for connection through this experience. Full article

Natural language processing (NLP) has numerous applications and has been extensively developed in deep learning. In recent years, language models such as Transformer, BERT, and GPT have frequently been the foundation for related research. However, relatively few studies have focused on evaluating the quality of generated sentences. While traditional evaluation methods like BLEU can be applied, the challenge is that there is no ground truth reference for generated sentences, making it difficult to establish a reliable evaluation criterion. Therefore, this study examines the content generated by Bidirectional Encoder Representations and related recurrent methods based on the Transformer model. Specifically, we focus on analyzing sentence fluency by assessing the degree of part-of-speech (PoS) matching and the coherence of PoS context ordering. Determining whether the generated sentences align with the expected PoS structure of the model is crucial, as it significantly impacts the readability of the generated text. Full article

Podcasts have established themselves in the digital media landscape as an integral part of information gathering and opinion formation for many users. The number of podcast users has stabilized at a high level in recent years. However, podcast producers, including podcast journalists, remain a largely unexplored group. This study focuses on podcast journalists and aims to identify the perceptions, motivations, and quality standards relating to their roles in podcasting. It is based on the results of an online survey of 378 podcast journalists from Germany, Austria, and Switzerland. Against a background of the concept of pioneer journalism, this article argues that podcast journalists are innovative contributors to the journalism ecosystem and have positioned themselves as new actors within the field. The findings of this study show that podcast journalists create, produce, and present journalistic content, for instance news or background stories, in the form of audio episodes, and see themselves as both educators and entertainers. They use the creative freedom of podcasting to engage deeply with their audiences and achieve high levels of listener loyalty. While financial gain is not their primary motivation, they have innovated new revenue models. They are committed to the quality of their content and emphasize comprehensibility and accuracy of information. Full article