Rich Media Content Research Articles

Immersive time in the metaverse and visits to the physical world: why not both? A holistic customer engagement framework

PurposeThrough the theoretical lenses of media richness, perceived realism and customer engagement, this study aims to investigate the mechanisms that promote customer engagement in metaverse-mediated environments in the meetings, incentives, conferences and exhibitions (MICE) context, as well as the impact of customer engagement on customers’ metaverse usage intensity and future visit intention.Design/methodology/approachA survey of customers who have experience with metaverse-mediated MICE activities was conducted. Data from 267 respondents were analysed using partial least squares-structural equation modelling and fuzzy-set qualitative comparative analysis (fsQCA) to test our research framework.FindingsMedia richness dimensions, including multiple cues, immediate feedback and personal focus, were found to enhance perceived metaverse realism, which in turn affects the dimensions of customer engagement, leading to customers’ metaverse usage intensity and future visit intention. The fsQCA analysis identifies three configurations that lead to high event visit intention.Practical implicationsThis research helps developers and marketers better understand how rich media contents create realistic experiences in the metaverse, aiding them to devise strategies for customer engagement and improve resource allocation.Originality/valueDespite its potentially revolutionary impacts, empirical studies on the mechanisms driving customer engagement in the metaverse and its effects are scarce. This study contributes by revealing the multiple-phase mechanism of the customer engagement journey in the metaverse-mediated MICE context. By expanding the media richness theory into this area, our study provides new insights by illustrating how media richness dimensions create multisensory experiences and real-time interactions, enhancing perceived metaverse realism and customer engagement. It also addresses the debate on whether metaverse-mediated events substitute or complement real-life events.

A Survey on Artificial Intelligence Techniques for Improved Rich Media Content Delivery in a 5G and Beyond Network Slicing Context

A Survey on Artificial Intelligence Techniques for Improved Rich Media Content Delivery in a 5G and Beyond Network Slicing Context

A Novel Multi-Dimensional Analysis Approach to Teaching and Learning Analytics in Higher Education

E-learning platforms are being used in higher education to liberate teaching and learning from the constraints of time, pace, and space. These platforms provide rich media content, collaborative assessment, and analytics tools and support learning through a variety of online possibilities. However, the focus of most of this research is on learning analytics inside e-learning platforms and does not cover other related institutional surveys and educator professional development activities. This paper outlines a novel conceptual approach for the creation of a teaching and learning data warehouse analytics system that utilizes a Multi-Dimensional Analysis approach to teaching and learning analytics across these e-learning platforms in combination with other institutional data sources, such as various institutional surveys, tracking of professional development activities, and analysis of the use of the learning management system. The novel teaching and learning data warehouse analytics system (TLDWAS) is being developed at Lingnan University, a leading liberal arts university in Hong Kong. The genesis of this project is analyzing large volumes of teaching and learning data and presenting a big picture for senior management to gain insight from massive amounts of student courses and teacher evaluation data, fill the research gaps mentioned above, and help educators identify problems and solutions more effectively. The TLDWAS provides a visual interface for identifying some indicators and storing, classifying, and analyzing teaching and learning data in various forms to generate statistics, analyze models, and identify meaningful patterns in the data in order to improve teaching and learning. The TLDWAS not only provides more evidence-based decision-making for senior management but is also capable of being applied to identify patterns and relations pertaining to students’ academic performance, usage of e-learning systems, and associated staff development activities.

Resource-efficient seamless transitions for high-performance multi-hop UAV multicasting

Many UAV-related applications require group communications between UAVs to reliably and efficiently deliver rich media content as well as to extend line-of-sight coverage between sky and ground. This paper is among the first to study fast yet resource-efficient UAV transitions for aerial group communications while maintaining high group communication performance. We develop a set of novel analytic and algorithmic results to form the efficient transition formation (ETF) algorithm, in order to deal with different UAV transition scenarios in a group communication environment. The ETF algorithm first evaluates the seamlessness of a straight-line trajectory (SLT) by processing low-complexity computations (e.g., Euclidean distances) or fast checks with controlled traffic overheads. For a non-seamless SLT, the ETF algorithmm establishes a new trajectory consisting of a minimal number of seamless straight lines that join at specially selected locations to control mobile UAVs’ seamless travel distances. Our simulation studies quantify the performance gains that the ETF algorithm may achieve, outperforming compared studies by admitting 66% more traffic with reduced energy consumption and guaranteed communication performance for both transitional and non-transitional UAVs.

Gamification in Tourism: A Design Framework for the TRIPMENTOR Project

In this paper, we discuss the gamification strategies and methodologies used by TRIPMENTOR—a game-oriented cultural tourism application in the region of Attica. Its primary purpose is to provide visitors with rich media content via the web and mobile environments by redirecting travellers, highlighting points of interest, and providing information for tour operators. Gamification is a critical component of the project; it relates users to specific sites and activities, improves their visiting experiences, and encourages a constant interaction with the application through a playful experience. In TRIPMENTOR, gamification serves both as a tourism marketing strategy and as a tool for encouraging users to share their experiences while exploring Attica in a way designed to meet their personal needs, interests, and habits. This paper aims to describe and analyse the gamification mechanisms applied, following the Octalysis framework, and discuss the opportunities and challenges of gamification as a tourist marketing strategy.

Design and Implementation of Interactive Platform for Operation and Maintenance of Multimedia Information System Based on Artificial Intelligence and Big Data.

In order to cope with the challenges that operators face after the impact of diversified social channels in the field of interactive services, we actively build a new generation of intelligent interactive systems based on artificial intelligence technology, a semantic understanding, and intention recognition, covering rich media content, omnichannel coverage, high-frequency knowledge updates, consistent service response, high-quality, and low-cost intelligent interactive solutions is proposed. The solution provides overall business modeling for scenario design and a scenario-based knowledge expression system, with the function of fragmented knowledge processing. With complete text and voice information, combined with pictures, text, audio, video, and other multimedia, we intelligently interact with users, allowing users to obtain required information and solve problems in a pleasant and relaxing interaction. Therefore, the research and exploration of the intelligent interactive system architecture based on artificial intelligence is a useful practice and strong support for operators to redefine the connotation and elements of “smart service” in the process of building “smart operation.” Through repeated tests, it can be seen that the language similarity has reached 0.75549, which is very close to 1.0000. It can be seen that the design of this platform has been successful.

Teaching large-enrollment online language courses: Faculty perspectives and an emerging curricular model

Large enrollment online courses (over 40 students) are becoming increasingly common due to the profits they can offer institutions. Little research, however, has been conducted into faculty experiences in these contexts. This qualitative study fills this need and explores the experiences, challenges, and strategies of faculty, specifically in courses that have traditionally required interaction between students and teachers. Participants were professors and administrators (N = 13) of Chinese, English, Japanese, and Spanish teaching in a South Korean cyber university. Data included an online survey, interviews, and document analysis. Participants reported feeling slightly more-favorable than less-favorable about their experiences teaching and their students’ experiences learning in these courses. ACTFL’s (2010) guidelines for language courses were re-examined considering the current technology and budgetary environment, with findings ultimately leading to the Framework for Developing and Teaching Large Online Language Courses. We offer an expansion of the ACTFL criteria of meaningful interaction, documenting the role that interaction between student and rich media content has to offer. This study also highlights the degree to which these large online courses rely on students arriving with their own self-directed learning strategies, underlining concerns related to equity in education.

Providing a model to promote citizens' water literacy for sustainable development

The aim of this study is to provide a model for promoting citizens' water literacy for sustainable media-based development. According to the purpose of this research, it is an applied and exploratory type that has been obtained by the method of Strauss and Corbin's systematic method of data foundation theory. Due to the qualitative approach of the research, in-depth and semi-structured interviews were conducted with 34 experts in the fields of environment, water, social sciences, and communications using theoretical sampling. Then the data were analyzed and 1120 primary codes of open coding, 71 categories of axial coding, and 23 main factors of selective coding were identified. The main contribution of this research is the promotion of water literacy, which have been formed according to the causal conditions (targeting and policy-making in water literacy, water valuation, water attitudes, water skills, water knowledge, literacy promotion), the underlying factors (media diversity) as well as the intervening factors (media politicization, journalists' dependence on sources of power), which was obtained through the strategies such as a change in educational system, pathology of educational master plans, interaction of industry, educational institutions and universities, promotion of social capital, interaction with media, audience, media greening, complete and rich media content, institutionalization of water discourse, agile media interaction. In the second stage, in order to weigh the causal conditions and strategies, hierarchical analysis was used. The results revealed that among the causal conditions, targeting and policy-making were the most important priorities in water literacy.

Augmenting Speech Quality Estimation in Software-Defined Networking Using Machine Learning Algorithms.

With the increased number of Software-Defined Networking (SDN) installations, the data centers of large service providers are becoming more and more agile in terms of network performance efficiency and flexibility. While SDN is an active and obvious trend in a modern data center design, the implications and possibilities it carries for effective and efficient network management are not yet fully explored and utilized. With most of the modern Internet traffic consisting of multimedia services and media-rich content sharing, the quality of multimedia communications is at the center of attention of many companies and research groups. Since SDN-enabled switches have an inherent feature of monitoring the flow statistics in terms of packets and bytes transmitted/lost, these devices can be utilized to monitor the essential statistics of the multimedia communications, allowing the provider to act in case of network failing to deliver the required service quality. The internal packet processing in the SDN switch enables the SDN controller to fetch the statistical information of the particular packet flow using the PacketIn and Multipart messages. This information, if preprocessed properly, can be used to estimate higher layer interpretation of the link quality and thus allowing to relate the provided quality of service (QoS) to the quality of user experience (QoE). This article discusses the experimental setup that can be used to estimate the quality of speech communication based on the information provided by the SDN controller. To achieve higher accuracy of the result, latency characteristics are added based on the exploiting of the dummy packet injection into the packet stream and/or RTCP packet analysis. The results of the experiment show that this innovative approach calculates the statistics of each individual RTP stream, and thus, we obtain a method for dynamic measurement of speech quality, where when quality decreases, it is possible to respond quickly by changing routing at the network level for each individual call. To improve the quality of call measurements, a Convolutional Neural Network (CNN) was also implemented. This model is based on two standard approaches to measuring the speech quality: PESQ and E-model. However, unlike PESQ/POLQA, the CNN-based model can take delay into account, and unlike the E-model, the resulting accuracy is much higher.

IoT-Enabled Social Relationships Meet Artificial Social Intelligence

With the recent advances of the Internet of Things (IoT), and the increasing accessibility to ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as social relationships explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as artificial social intelligence (ASI) that has the potential to tackle the social relationships explosion problem. This article discusses the role of IoT in social relationships management, the problem of social relationships explosion in IoT, and reviews the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques.

Harnessing the power of dialogue: examining the impact of facebook content on citizens’ engagement

ABSTRACT The present study sheds light on the under-researched relationship between social media content and citizens’ engagement, as well as the impact of dialogic communication on citizens’ engagement. To this end, Facebook content of the five largest Greek municipalities was examined. A six-dimensional typology of social media content posted by local governments on Facebook was proposed. The results suggest that dialogic posts and posts that trigger offline participatory activities exerted significant influence on all forms of citizens’ online engagement. It is recommended that social media managers of local governments in Greece, as well as other countries with similar public administration styles and social media usage patterns, create dialogic loops with their citizenry by addressing users’ questions and concerns through Facebook posts. Moreover, they could publish rich media content on Facebook in the form of videos and posts that mobilise citizens to participate in various offline activities (e.g., events, council meetings).

Analyzing Australian SME Instagram Engagement via Web Scraping

Abstract Background: Social media has grown into a prominent marketing and communication tool, and Instagram is a leader in this regard due to its distinctive supports for medial rich contents. Due to the challenges faced by Small-and-medium-enterprises (SMEs), they are suggested to embrace social media technology. Previous studies in Interactive value creation theory (IVF) indicated that SMEs-followers co-creation processes enable Instagram engagement. However, little is known of how to promote the value co-creation process via practical Instagram micro-practices that SMEs can employ regarding their constraints. This study is among the first to examine SMEs' practices for Instagram engagement via the process of value co-creation. Method: We collected data regarding Instagram engagements of 10 Australian SMEs in eight months period using Instagram API. Qualitative analysis is conducted based on 2110 contents in a Poisson regression model. Content analysis and time-series visualizations are employed to investigate the uniqueness of several outliners in the dataset. Results: Findings imply that SMEs should bank on @Tagging instead of #Hashtags to get more engagement as contents with @Tagging show higher levels of inclusiveness and trustworthiness. Also, customized postings for the loci-specific audience effectively encourage followers to participate in conversations, while commercial intensive posting style shows adverse effects due to its low level of credibility. Rich media contents would not necessarily reel in more engagement; the commitment and consistent postings amassed a considerable number of followers over time, leading to a higher engagement rate. Conclusions: This study confirmed the benefits of data scraping in building business intelligence and validate the IVF framework as the theoretical background to investigate the effectiveness of Instagram micro-practices. IVF should be considered in designing social media practices to enable SMEs and followers' collaborations for business value generation. This study provides extra interpretations of the interrelationships between IVF, visual-rich contents, and social media engagement.

Soul and machine (learning)

Machine learning is bringing us self-driving cars, medical diagnoses, and language translation, but how can machine learning help marketers improve marketing decisions? Machine learning models predict extremely well, are scalable to “big data,” and are a natural fit to analyze rich media content, such as text, images, audio, and video. Examples of current marketing applications include identification of customer needs from online data, accurate prediction of consumer response to advertising, personalized pricing, and product recommendations. But without the human input and insight—the soul—the applications of machine learning are limited. To create competitive or cooperative strategies, to generate creative product designs, to be accurate for “what-if” and “but-for” applications, to devise dynamic policies, to advance knowledge, to protect consumer privacy, and avoid algorithm bias, machine learning needs a soul. The brightest future is based on the synergy of what the machine can do well and what humans do well. We provide examples and predictions for the future.

Intelligent Collaborative Authoring of Place-Based, Cross-Cultural and Media-Rich Experiences

In this paper, we present a framework that aims to support the active participation and collaboration of knowledge workers and engineers in the co-authoring of place-based, cross-cultural and media-rich memories, experiences, stories and narration. To achieve this, the framework proposes a novel approach for facilitating such a participation and collaboration through the semantic integration of data/information and integrated tools that will be both accessible via an open, user-friendly, mobile and knowledge-based platform, emphasizing a low-effort participative and guided co-authoring approach. The presented collaborative and participative approach is expected to foster social cohesion in heterogeneous communities of interest and practice. For the realization of the framework, we propose the implementation of a proof-of-concept system and its evaluation in the socio-cultural group of immigrants and refugees within the context of creating and sharing knowledge related to the physical and digital artifacts of a modern art museum. Our vision for the proposed framework is to introduce new technology for the collaborative authoring of cultural experiences with low effort using an intelligent assistant. Additionally, we envision a Shared Experiences Ecosystem (SEE) that aims to provide media-rich content and tools that will eventually foster the inclusive access of heterogeneous socio-cultural groups to shared experiences, increasing social cohesion in resilient local environments.

Design and implementation of intelligent creation platform based on artificial intelligence technology

In this article, we have designed a fusion media platform that integrates intelligent creation and editing based on Artificial Intelligence Technology. The system includes a content acquisition and intelligent creation module, a multimedia editing module, and a multi-channel integrated publishing and operation module. The content collection and intelligent creation module utilizes the audio and video capture technology of the mobile computer and natural language processing technology to achieve rich media content acquisition and smart embellishment. The multimedia editing module can realize text matching and modification trace preservation, associated content intelligent extraction, and audio and video processing. The multi-channel integrated publishing module utilizes crawling technology and auth2.0 authentication technology to realize seamless interfacing of new media channels such as WeChat, Weibo, headline, Sohu, TV production system, radio production system, newspaper typesetting system, etc. At the same time, the platform can also capture operational data through crawler technology to meet the needs of intelligent operations. The platform has been piloted in a number of press and publication media organizations. Through the results of the operation and the user feedback, the platform can provide a good service for news media workers from content creation, editorial review to channel release. This research will provide a comprehensive working platform for the news publishing media industry, which is of great value for promoting the comprehensive fusion of the media.

MMedia: An Efficient Transmission Policy for Multimedia Applications using Mobile Cloud Computing

Background & Objective: Multimedia aggregates various types of media such as audio, video, images, animations, etc., to form a rich media content which produces an everlasting effect in the minds of the people. Methods: In order to process multimedia applications using mobile devices, we encounter a big challenge as these devices have limited resources and power. To address these limitations, in this work, we have proposed an efficient approach named as mMedia, wherein multimedia applications will utilize the multi cloud environment using Mobile Cloud Computing (MCC), for faster processing. The proposed approach selects the best available network. The authors have also considered using the Lyapunov optimization technique for efficient transmission between the mobile device and the cloud. Results: The simulation results indicate that mMedia can be useful for various multimedia applications by considering the energy delay tradeoff decision. Conclusion: The results have been compared alongside the base algorithm SALSA on the basis of different parameters like time average queue backlog, delay and time average utility and indicate that the mMedia outperforms in all the aspects.

Soul and Machine (Learning)

Machine learning is bringing us self-driving cars, medical diagnoses, and language translation, but how can machine learning help marketers improve marketing decisions? Machine learning models predict extremely well, are scalable to “big data,” and are a natural fit to analyze rich media content, such as text, images, audio, and video. Examples of current marketing applications include identification of customer needs from online data, accurate prediction of consumer response to advertising, personalized pricing, and product recommendations. But without the human input and insight—the soul—the applications of machine learning are limited. To create competitive or cooperative strategies, to generate creative product designs, to be accurate for “what-if” and “but-for” applications, to devise dynamic policies, to advance knowledge, to protect consumer privacy, and avoid algorithm bias, machine learning needs a soul. The brightest future is based on the synergy of what the machine can do well and what humans do well. We provide examples and predictions for the future.

Pictures tell a story: antecedents of rich-media curation in social network sites

ABSTRACTThis study explores a new aspect of online social networking, in which textual information has been largely replaced by the curation of rich-media content. We examine these ‘curation-based’ networks (CBN) and develop a theoretical model of the antecedents of intentions to curate rich-media content. Results from the empirical analysis of 449 users of Pinterest, a popular CBN, show that users undertake curation tasks to achieve objectives around the management, discovery, and presentation of rich-media content. The findings have implications for researchers and practitioners interested in the changing nature of online social networks and the role of rich-media in online communication.

ComProSe: Shaping Future Public Safety Communities with ProSe-Based UAVs

3GPP ProSe supports direct discovery and communications between UE localized in proximity to each other. However, the existing 3GPP ProSe has limited support for user-friendly rich media content exchange between different public safety groups. This article proposes ComProSe, an innovative ProSe-enhanced multimedia communication framework for public safety services. By employing UAV as relays, ComProSe provides direct discovery and QoS-aware communications between public safety UEs from different organizations. Additionally, user-friendly interaction between public safety UEs is offered. ComProSe is designed in alignment with the emerging 3GPP 5G technologies and is compatible with both operator networks and mobile consumer electronics. The article also presents real-life testing illustrating the cooperation between UAV and public safety users availing from a public safety service in the context of ComProSe.

Identifying community structure in a multi‐relational network employing non‐negative tensor factorization and GA k‐means clustering

The ubiquity of social networking sites leads to the generation of rich social media content. Community discovery is one of the significant tools in the analysis of social media data that is often multi‐relational due to diverse forms of user interactions. Although there has been extensive research devoted to community discovery, most of it is restricted to single‐relational networks. However, focus has been shifted to multi‐relational networks in the recent years. In this study, we aim to discover communities in the multi‐relational networks through relational learning. Our main focus is the utilization of non‐negative tensor factorization and GA k‐means clustering for community discovery. In order to incorporate the relational characteristics of the data in the learning methodology, tensors are used to model the multi‐relational network. Tensor factorization reveals the latent features of the data and shows state‐of‐the‐art results for multi‐relational learning. Once the implicit information is obtained by factorization, we apply a GA k‐means clustering algorithm for community discovery. Experiments are performed on synthetic as well as real datasets. The results obtained are quite promising and clearly demonstrate the effectiveness of our proposed scheme. WIREs Data Mining Knowl Discov 2017, 7:e1196. doi: 10.1002/widm.1196This article is categorized under: Algorithmic Development > Structure Discovery Technologies > Machine Learning