Search Results (324)

This research was designed to explore science-fiction and fantasy (SFF) posters, specifically those related to films and television shows, from the perspective of their owners, examining their potential as sources of social and cultural significance and meaning. The research explored these in terms of the content of the poster, placement, media texts they reference, morals, behavior, identity, sense of self, well-being and self-expression. Data collection took place between 2020 and 2022 via an online survey (N = 273) and follow-up semi-structured interviews (N = 28) with adult science-fiction and fantasy film and television show poster owners. The significance and meaning of SFF posters were framed by two conceptual models: ‘The Three Significances’—esthetics, functionality, and significance (both spatial and personal)—and ‘The Big Three’—content, design, and color. Among these, content held the greatest significance for owners. Posters served as tools for self-expression, reflecting their owners’ identities, affinities, and convictions, while also reinforcing their connection to the media they reference. Posters helped to reinforce a sense of self and fan identity and evoke emotional responses, and the space in which they were displayed helped shape their meaning and significance. The paper sets out some suggestions for future research in this important topic. Full article

This paper analyzes 20th century colloquialization processes in Peninsular Spanish, in line with recent works addressing mass-media colloquialization. Previous studies suggest a change in sports-talk announcing towards a more informal model, which is supported by the incorporation of new linguistic features as well as by the influence of some external changes. In this context, this study delves into the role of discourse markers as a colloquialization parameter, as a growth in their employment has been detected since ca. 1990. To further explore the data, a manually compiled corpus has been transcribed and analyzed: our corpus consists of both radio and TV football-match recordings aired in Spain from 1980 to 2000 and from 2000 to 2024. These two big periods have been subdivided into five-year periods or micro-diachronies to allow for a more detailed analysis. Results reveal a consolidation of the use of discourse markers by sports announcers, contrasting with earlier broadcasts that tended to avoid them or that employed more formal discourse markers, typically related to written, planned discourses. Full article

The gig economy, driven by flexible and platform-based work, is reshaping labor markets and employment norms. Understanding public perceptions of this shift is critical for promoting social good and informing equitable policy. This study employs big data analytics and Latent Dirichlet Allocation (LDA) topic modeling to analyze 15,259 tweets collected from the X platform. Seven key themes emerged from the data, including labor precarity, flexibility, algorithmic control, platform accountability, gender disparities, and worker rights. While some users emphasized autonomy and new income opportunities, most expressed concerns about job insecurity, lack of protections, and digital exploitation. These findings offer real-time insights into how gig work is discussed and contested in public discourse. The study highlights how social media analytics can inform labor policy, guide platform regulation, and support advocacy efforts aimed at building a fairer and more resilient gig economy. Full article

The importance of real-time big data has become crucial in the digital revolution of modern society, in the context of increasing data flows from multiple sources, including social media, internet connected devices (IOT) and financial systems, real-time analysis and processing is becoming a strategic tool for fast and accurate decision making, we find applications in different domains such as healthcare, finance, and digital marketing, which is revolutionizing traditional business models. In this article, we explore the recent advances and future prospects of real-time big data. Our research is based on recent work published between 2020 and 2025, examining the technological advances, the difficulties encountered and suggesting ways of optimizing the efficiency of these technologies. Full article

The increased trend of globalization and the ever-growing world population have produced significant challenges to sustainable consumption goals, especially in the food industry. Production, transportation, and consumption of food have a major impact on sustainability. This bibliometric review aims to offer a comprehensive analysis of the influence of social media platforms on sustainable consumption in the food industry. Based on a literature search in the ISI Web of Science (WoS) database, we identified 38 documents by applying three filters: “sustainable consumption,” “food industry,” and “social media”, and a detailed screening process, a final set of 29 articles was selected for analysis. The selection criteria ensured relevance and alignment with the research objectives. We conducted a qualitative thematic analysis to identify emerging trends, aiming to highlight the potential of social media in raising awareness, cultivating sustainable consumption practices, and creating change in the food industry. The findings indicate that social media is a powerful tool not only for influencer marketing and brand communication but also for consumer empowerment and behavioral change. Our review identified key themes such as the prevalence of influencer-based food marketing, challenges related to misinformation, consumer demand for transparency, and the growing integration of big data and personalized marketing strategies. We argue that social media can significantly contribute to sustainability goals when responsibly used by marketers, educators, and policymakers. Full article

In today’s digital era, social media platforms like Instagram have become deeply embedded in daily life, generating billions of content items each day. This vast stream of publicly accessible data presents a unique opportunity for researchers to gain insights into human behaviour and personality. However, leveraging such unstructured and highly variable data for psychological analysis introduces significant challenges, including data sparsity, noise, and ethical considerations around privacy. This study addresses these challenges by exploring the potential of machine learning to infer personality traits from Instagram content. Motivated by the growing demand for scalable, non-intrusive methods of psychological assessment, we developed a personality prediction system combining convolutional neural networks (CNNs) and random forest (RF) algorithms. Our model is grounded in the Big Five Personality framework, which includes Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness. Using data collected with informed consent from 941 participants, we extracted visual features from their Instagram images using two pretrained CNNs, which were then used to train five RF models, each targeting a specific trait. The proposed system achieved an average mean absolute error of 0.1867 across all traits. Compared to the PAN-2015 benchmark, our method demonstrated competitive performance. These results highlight that using social media data for personality prediction offers potential applications in personalized content delivery, mental health monitoring, and human–computer interactions. Full article

In the digital age, the mechanisms of power and control have evolved beyond Foucault’s disciplinary societies, giving rise to a new paradigm of algorithmic governance. This study critically reinterprets Foucault’s concept of the human subject in light of contemporary digital surveillance, big data analytics, and algorithmic decision-making. The paper looks at how technology, biopolitics, and subject formation interact. It says that algorithmic control changes people’s choices in ways that have never been seen before through predictive modeling and real-time behavioral modulation. The study starts with a comparison of early Foucauldian frameworks and more recent theories of digital governance. It uses a method that combines philosophy, media studies, and political theory. The results show that while disciplinary societies relied on institutionalized norms and body regulation, algorithmic control works through data-driven anticipatory mechanisms, which make subjectivity less clear and more broken up. This shift raises ethical and ontological questions about autonomy, resistance, and the very notion of the self in a hyper-connected society. The study concludes that rethinking Foucault’s insights in the digital era is essential for understanding and contesting the pervasive influence of algorithmic power on human subjectivity. Full article

In today’s hyperconnected world, disaster announcements—regardless of actual impact—can significantly shape consumer behaviour and brand perception in the hospitality sector. This study investigates how customers respond online to disaster-related signals, focusing on digital marketing activities by luxury hotels in Santorini, Greece. Drawing on a case study of the Santorini Earthquake in February 2025—during which the Greek government declared a state of emergency—we use big data analytics, including web traffic metrics, social media interaction and fuzzy cognitive mapping, to analyse behavioural shifts across platforms. The findings indicate that disaster signals trigger increased engagement, altered sentiment and changes in advertising efficiency. This study provides actionable recommendations for tourism destinations and hospitality brands on how to adapt digital strategies during crisis periods. Full article

The increasing digitalisation of housing markets has expanded the types and sources of data available for research. However, there is limited understanding of how these diverse data types are used across different themes in urban housing studies and which analytical approaches are applied. This study addresses these questions through a systematic review of 71 peer-reviewed studies published between 2010 and 2021, following PRISMA guidelines. The review identifies five dominant research themes: housing market analysis, rental market analysis, housing policy evaluation, housing affordability, and housing inequality. It also classifies five main data sources: official statistics, non-official statistics, surveys and qualitative data, big data, and social media. A cross-examination of themes and data types shows that official statistics remain the most frequently used across the themes, while emerging data sources such as big data and social media are underutilised—especially in research on informal housing and demand-side dynamics. Regression analysis and hedonic modelling are the most commonly applied analytical methods, with the choice of method largely shaped by research objectives and data types. By developing a cross-typology framework linking research themes, data sources, and methods, this study provides an evidence base for inclusive, responsive, and data-informed strategies that support socially and economically sustainable urban housing systems. Full article

Urban green spaces (UGSs) provide recreational and cultural services to urban residents and play an important role in mental health. This study uses big data mining techniques to analyze 62 urban parks in the Changsha–Zhuzhou–Xiangtan urban agglomeration (CZXUA) based on data such as points of interest (POIs), areas of interest (AOIs), and user comments from the popular social media platform Dianping. In addition, the authors apply sentiment analysis using perceptual dictionaries combined with geographic information data to identify text emotions. A structural equation model (SEM) was constructed in IBM SPSS AMOS 24.0 software to investigate the relationship between five external features, five types of cultural services, nine landscape elements, four environmental factors, and tourist emotions. The results show that UGS external features, cultural services, landscape elements, and environmental factors all have positive effects on residents’ emotions, with landscape elements having the greatest impact. The other factors show similar effects on residents’ moods. In various UGSs, natural elements such as vegetation and water tend to evoke positive emotions in residents, while artificial elements such as roads, squares, and buildings elicit more varied emotional responses. This research provides science-based support for the design and management of urban parks. Full article

Multimodal interaction technology has become a key aspect of remote education by enriching student engagement and learning results as it utilizes the speech, gesture, and visual feedback as various sensory channels. This publication reflects on the latest breakthroughs in multimodal interaction and its usage in remote learning environments, including a multi-layered discussion that addresses various levels of learning and understanding. It showcases the main technologies, such as speech recognition, computer vision, and haptic feedback, that enable the visitors and learning portals to exchange data fluidly. In addition, we investigate the function of multimodal learning analytics in order to measure the cognitive and emotional states of students, targeting personalized feedback and refining instructional strategies. Though multimodal communication may bring a historical improvement to the mode of online education, the platform still faces many issues, such as media synchronization, higher computational demand, physical adaptability, and privacy concerns. These problems demand further research in the fields of algorithm optimization, access to technology guidance, and the ethical use of big data. This paper presents a systematic review of the application of multimodal interaction in remote education. Through the analysis of 25 selected research papers, this review explores key technologies, applications, and challenges in the field. By synthesizing existing findings, this study highlights the role of multimodal learning analytics, speech recognition, gesture-based interaction, and haptic feedback in enhancing remote learning. Full article

The analysis in this study covers how power imbalance, alliance cohesion, diplomatic and media framing, and big data analytics affect scaling up in the conflict in a multipolar world. This research applies the Constructivist International Relations Theory to examine survey data of 250 international relations experts, policymakers, and analysts using Survey Structured Equation Modeling (SEM) via SMART-PLS. Power imbalance and the way the media frames the situation are found to lead to an escalation of conflicts, but strong alliance cohesion, diplomatic effort, and big data analytics can mitigate the risk of the escalation. Strategic diplomacy, media regulation, and real-time data monitoring have thus shown their capacity to prevent conflict. These contribute to conflict studies by incorporating political IR models, data science knowledge, and policy advice on global security governance. This means they can support the prediction and prevention of conflicts by means of diplomatic transparency, ethical media practice, and AI early warning systems. This study is limited by the use of self-reported data; however, the results of this study indicate that this topic is under-explored in cultural and geopolitical terms. The results help inform policymakers and security entities on ways to address conflict resolution as a matter of discretion and from a multidimensional perspective. Survey Structured Equation Modeling (SEM) via SMART-PLS is a technique used for analyzing structural relationships between measured variables and latent constructs, providing valuable insights into complex models. Survey Structured Equation Modeling (SEM) via SMART-PLS is a technique used for analyzing structural relationships between measured variables and latent constructs, providing valuable insights into complex models. Full article

As the automotive industry advances rapidly, user needs are in a constant state of evolution. Driven by advancements in big data, artificial intelligence, and natural language processing, mining user requirements from user-generated content (UGC) on social media has become an effective way to understand these dynamic needs. While existing technologies have progressed in topic identification and sentiment analysis, single-method approaches often face limitations. This study proposes a novel method for user requirement mining based on BERTopic and RoBERTa, combining the strengths of topic modeling and sentiment analysis to provide a more comprehensive analysis of user needs. To validate this approach, UGC data from four major Chinese media platforms were collected. BERTopic was applied for topic extraction and RoBERTa for sentiment analysis, facilitating a linked analysis of user emotions and identified topics. The findings categorize user requirements into four main areas—performance, comfort and experience, price sensitivity, and safety—while also reflecting the increasing relevance of advanced features, such as sensors, powertrain performance, and other technologies. This method enhances user requirement identification by integrating sentiment analysis with topic modeling, offering actionable insights for automotive manufacturers in product optimization and marketing strategies and presenting a scalable approach adaptable across various industries. Full article

In the face of rapid progress in heritage preservation and cultural tourism integration, landscape planning in historic cities is pivotal to showcasing regional identities and disseminating cultural value. However, the complexity of cultural characteristic identification and the imbalance in planning often restrict the progress of urban development. Additionally, existing studies predominantly rely on subjective methods and focus on a single cultural attribute, highlighting the urgent need for research on diversified cultural perception. Using Jingdezhen, a renowned historic cultural city, as an example, this study introduces a multi-label deep learning approach to examine cultural perceptions in tourism heritage landscapes. Leveraging social media big data and an optimized ResNet-50 model, a framework encompassing artifacts, production, folk, and living culture was constructed and integrated with ArcGIS spatial analysis and diversity indices. The results show: (1) The multi-label classification model achieves 92.35% accuracy, validating its potential; (2) Heritage landscapes exhibit a “material-dominated, intangible-weak” structure, with artifacts culture as the main component; (3) Cultural perception intensity is unevenly distributed, with core areas demonstrating higher recognition and diversity; (4) Diversity indices suggest that comprehensive venues display stronger cultural balance, whereas specialized ones reveal marked cultural singularity, indicating a need for improved integration across sites. This research expands the use of multi-label deep learning in tourism heritage studies and offers practical guidance for global heritage sites tackling mass tourism. Full article

Urban functional zones (UFZs) are related to people’s daily activities. Accurate recognition of UFZs is of great significance for an in-depth understanding of the complex urban system and optimizing the urban spatial structure. Emerging geospatial big data provide new ideas for humans to recognize urban functional zones. Point-of-interest (POI) data have achieved good results in the recognition of UFZs. However, since humans are the actual users of urban functions, and POI data only reflect static socioeconomic characteristics without considering the semantic and temporal features of dynamic human activities, it leads to an incomplete and insufficient representation of complex UFZs. To solve these problems, we proposed a data-synthesis-driven approach to quantify and analyze the distribution and mixing of urban functional zones. Firstly, representation learning is used to mine the spatial semantic features, activity temporal features, and activity semantic features that are embedded in POI data and social media check-in data from spatial, temporal, and semantic aspects. Secondly, a weighted Stacking ensemble model is used to fully integrate the advantages between different features and classifiers to infer the proportions of urban functions and dominant functions of each urban functional zone. A case study within the 5th Ring Road of Beijing, China, is used to evaluate the proposed method. The results show that the approach combining dynamic and static features of POI data and social media data effectively represents the semantic information of UFZs, thereby further improving the accuracy of UFZ recognition. This work can provide a reference for uncovering the hidden linkages between human activity characteristics and urban functions. Full article