Network Users Research Articles

Acceptance and use of live streaming on metaverse in Vietnam: An analysis with the UTAUT2

Virtual environments like the Metaverse have been gaining popularity in recent years. Live streaming has gained popularity as a favorite way to entertain among social network users, thanks to its real-time authenticity. This study will utilize the Extended Unified Theory of Acceptability and Use of Technology (UTAUT2) to examine the factors influencing the adoption of live streaming in the Metaverse, a new platform with greater immersion, among citizens in Vietnam. The research used a quantitative approach, collected data from a sample of participants through a structured questionnaire including Performance Expectancy (PEE), Effort Expectancy (EEF), Social Influence (SCI), Hedonic Motivation (HEM), and Experience (EXP). Additionally, technological Self-Efficacy (TSE) as an extended alternative is thought to influence that relationship as well. Results from the PLS-SEM technique was used to examine perception, acceptance, and adoption differences among demographic groups. Remarkably, the results show experience has a remarkable impact on the relationship between behavioral intention and the adoption use Metaverse for livestreaming. This study contributes theoretical value for investors and researchers on the entertainment and technology sectors due to the abilities of the live-streaming industry and the advanced features of metaverse in this digital world.

Multi-dimensional feature fusion-based expert recommendation in community question answering

PurposeCommunity question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.Design/methodology/approachTo address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.FindingsThe proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.Practical implicationsThis work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.Originality/valueThis work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

An edge computing and distributed ledger technology architecture for secure and efficient transportation

Intelligent Transportation Systems (ITS) faces significant challenges in achieving its goal of sustainable and efficient transportation. These challenges include real-time data processing bottlenecks caused by high communication latency and security vulnerabilities related to centralized data storage. We propose a novel architecture that leverages Edge Computing and Distributed Ledger Technology (DLT) to address these concerns. Edge computing pushes cloud services, such as vehicles and roadside units, closer to the data source. This strategy reduces latency and network congestion. DLT provides a secure, decentralized platform for storing and sharing ITS data. Its tamper-proof nature ensures data integrity and prevents unauthorized access. Our architecture utilizes these technologies to create a decentralized platform for ITS data management. This platform facilitates secure processing, storage, and data exchange from various sources in the transportation network. This paper delves deeper into the architecture, explaining its essential components and functionalities. Additionally, we explore its potential applications and benefits for ITS. We describe a case study focusing on a data marketplace system for connected vehicles to assess the architecture’s effectiveness. The simulation results show an average latency reduction of 83.35% for publishing and 87.57% for purchasing datasets compared to the cloud architecture. Additionally, transaction processing speed improved by 18.73% and network usage decreased by 96.67%. The proposed architecture also achieves up to 99.61% reduction in mining centralization.

Unlocking (re)purchase potential through corporate responsiveness on social networks: The role of perceived customer orientation

Social media marketing offers companies a new opportunity to connect with consumers through customer interactions. However, resource constraints may prevent companies from responding to every consumer comment. In light of this, the “reply to random comments strategy” emerges as an innovative approach where companies choose to respond to a mix of positive and negative comments. Yet, there is a lack of research and no standard practice for this strategy. This research used a mixed-method approach and signaling theory as a theoretical framework to provide extensive insights into how consumers react to various corporate response rates to random comments on social networks. Qualitative insights were gained through focus group discussions, while an empirical investigation was conducted using least squares modeling. The findings highlight the advantages of a high corporate response rate and the importance of signaling a strong customer orientation to enhance (re)purchase intentions of commenting and observing social network users. They also reveal the crucial mediating role of perceived customer orientation as a novel variable in this context. This research provides social media managers with guidelines on enhancing social network users' (re)purchase intentions by implementing a customer-oriented response strategy. The insights help practitioners build and maintain customer relationships, contributing to a company’s long-term success.

User Acceptance of Blockchain Technology in Financial Applications: Information Security, Technology Awareness and Privacy Aspects

Blockchain technology is now an advanced and innovative database technology and the attributes of blockchains are apparent in a variety of industries, especially in the financial industry. One of the most famous blockchain use cases, cryptocurrencies, has provoked much interest in social network users and customers. According to CoinMarketCap’s information, the global crypto market capitalization has reached around USD 2.37 T and there are around 9975 different cryptocurrencies available in the market. Despite the fact that academia and industry have paid much attention towards the blockchain direction, there is not much research on the factors that influence customer acceptability. This paper studies blockchains from a different angle, probing the factors prompting customers to use financial applications that utilize blockchain technology. We established the model and sorted the individual factors of perceived information security, technology awareness and privacy and found that users’ acceptance is significantly affected by information security and technology awareness, while privacy does not significantly influence users. According to the findings, we provide useful insights for application developers, conclude by presenting the limitations of the research and provide guidelines for future research.

Sustainable food packaging: engagement through the public discourse on social networks

Purpose This study aims to examine the public discourse on sustainable food packaging, evaluating the main characteristics of that discourse and how risk and analytical message content influence consumer engagement. Design/methodology/approach The authors use two studies with databases built from messages posted on Twitter (now X). Study 1 explores the discourse of sustainable food packaging by conducting a content/sentiment analysis. Study 2 examines more than four million messages posted by or mentioning the world’s most relevant food companies. Hypotheses are tested through censored regression analyses. Findings The results show that plastic is the term that can better classify how we talk about sustainable food packaging. The authors also show that food packaging messages related to sustainability, compared to those not related to sustainability, are composed using more analytical language, contributing to generating greater engagement. Moreover, social network users remain skeptical about food companies, as the latter messages on packaging sustainability generate less engagement (likes + retweets) than when other users post these messages. Originality/value This study addresses important points in the public discourse on social networks about the sustainability of food packaging and its language features. First, the data are representative of the food market with posts from leading worldwide food companies. Second, identifying the hot topics of the discussion on sustainability packaging on social media provides a new perspective on how companies and society view sustainable food packaging. Third, the authors show how the source of the message moderates the impact of sustainability on engagement.

Game-theoretic implications for uncovering the effects of virtual tipping in complex user networks

Consumer-generated media (CGM), including social live streaming service (SLSS), often incorporate gamification elements to engage users. Online virtual tips and gifts, such as “bits” in Twitch, exemplify this approach by fostering interactive dynamics between live streamers and their audience. However, issues remain in understanding the impact of (virtual) tips on user behavior and how platform commissions, which are partly collected from the tips provided by other users, influence the platform’s overall revenue and user behavioral strategies. This study considers a CGM/SLSS platform, such as Twitch, as a variation of the public goods game and proposes a game-theoretical model integrated with a platform-mediated tipping system with a certain commission rate. In experiments, the proposed model was applied to artificial and actual SLSS networks, demonstrating that the influence of a tipping system on user behavior is significant, impacting the motivation for live streaming and the effort to improve its quality. Depending on individual preferences for psychological or monetary reward, the commission rate is determined by the platform depending on the users’ network positions, such as the degree of connectivity, which corresponds to the number of followers/followees in SLSS networks. Our findings offer valuable insights into the design and management of SLSS platforms and further suggest that there may be an appropriate commission rate to maximize platform revenue.

Anomalous behavior detection based on optimized graph embedding representation in social networks

Anomalous behaviors in social networks can lead to privacy leaks and the spread of false information. In this paper, we propose an anomalous behavior detection method based on optimized graph embedding representation. Specifically, the user behavior logs are first extracted into a social network user behavior temporal knowledge graph, based on which the graph embedding representation method is used to transform the network topology and temporal information in the user behavior knowledge graph into structural embedding vectors and temporal information embedding vectors, and the hybrid attention mechanism is used to merge the two types of vectors to obtain the final entity embedding to complete the prediction and complementation of the temporal knowledge graph of user behavior. We use graph neural networks, which use the temporal information of user behaviors as a time constraint and capture both user behavioral and semantic information. It converts the two parts of information into vectors for concatenation and linear transformation to obtain a comprehensive representation vector of the whole subgraph, as well as joint deep learning models to evaluate abnormal behavior. Finally, we perform experiments on the Yelp dataset to validate that our method achieves a 9.56% improvement in the F1-score.

Enhancing local energy sharing reliability within peer-to-peer prosumer communities: A cellular automata and deep learning approach

This study introduces a significant advancement in peer-to-peer (P2P) energy trading systems within smart grids, addressing a crucial gap in existing research by incorporating optimal energy storage capacities to accommodate varying energy demands resulting from lifestyle changes. Through a two-level optimization approach, aimed at maximizing self consumption and optimizing energy flow within the grid, we propose a novel energy management strategy. Our contribution lies in the introduction of a new layer of deep learning and rules control, forming a self-energy sharing system for each prosumer. This architecture, termed the smart node, integrates deep learning techniques, to predict and customize energy services through dynamic adjustment of lower and upper bounds of battery capacities. Additionally, we leverage cellular automaton (CA) approaches to establish sustainable consensus among P2P network users, enhancing the adaptability and efficiency of the energy management system. The results show that the proposed algorithm could reduce the energy consumed by the P2P community from the utility by around 20% and maximize the collective self-consumption by around 8% compared to conventional energy trading in microgrids.

Digital health literacy and diet quality

The objective of this study was to evaluate the correlation between the level of digital health literacy (DHL) and diet quality (DQ). Information was collected from users of digital social networks through online questionnaires containing sociodemographic data, questions from the Digital Health Literacy Instrument and a 24-hour recall. To verify the correlation between the level of DHL and QD, the Spearman correlation test and the chi-square test were applied for the association between DHL and QD with sociodemographic factors. The diet of 71% of respondents was classified as “needs modification”. There was an association between higher levels of DHL and younger age, higher income and higher education. There was no significant correlation between DHL level and QD. It was observed that sociodemographic factors are significant for the development of digital health literacy. Assessing the reliability of information was the greatest difficulty reported for health searches.

Impacts of Social Media Advertising on Purchase Intention and Customer Loyalty in E-Commerce Systems

The emergence of new technologies has had a noteworthy impact on communication systems, leading to the importance of conducting research in this area due to the significant influence of social media. Marketing operators must implement the necessary infrastructure to identify and fulfill customers' expectations, considering the advantages and the increasing number of users in social networks. This paper presents a comprehensive framework for evaluating the purchase intention of electronic commerce systems, taking into account the impact of social media advertising and customer loyalty. The paper aims to enhance purchase intention in e-commerce through social media advertising and examine influential factors that improve online shopping performance, including social media advertising's effectiveness on customer purchase behavior and brand loyalty. The paper develops a theoretical framework of nine hypotheses to evaluate purchase intention in e-commerce systems, with a focus on the effect of social media advertising on brand loyalty and purchase intention. To validate the proposed model and test the research hypotheses, we make use of information obtained from an international company located in China. The results demonstrate the positive effect of pleasurable motivation on purchase intention, with a significance level of 3.776 and a path coefficient of 0.279. Moreover, an investigation of the positive effect of customer loyalty on the recommended advertisement confirms the hypotheses, with a significance of 32.815 and a path coefficient of 0.788.

Energy-efficient maximization algorithm for energy harvesting under imperfect channel information

In this paper, we propose a novel energy-efficient resource optimization scheme in non-orthogonal multiple access (NOMA) networks with simultaneous wireless information and power transfer (SWIPT). In this system, we consider practical imperfect channel information that accounts for random channel delays and channel estimation errors. Additionally, the small cell users (SCUs) in a heterogeneous network (HetNet) can harvest energy from the small base station (SBS) signal by energy harvesting. Based on the non-linear energy harvesting (NEH) model, the problem of maximizing energy efficiency with imperfect channel state information (CSI) is formulated. Since the formulated problem is a probabilistic mixed non-convex optimization problem, a two-stage algorithm is developed to jointly optimize sub-channel matching and power allocation. In particular, the problem is first transformed into a non-probabilistic problem through the relaxation method. For the sub-channels matching problem, a low-complexity many-to-many matching algorithm is designed to achieve dynamic matching based on the preference lists. For the power allocation problem, the closed-form transmission power of SCUs can be derived by the Dinkelbach method and Lagrangian dual approach. Simulation results show that the proposed scheme can achieve higher energy efficiency than existing linear energy harvesting (LEH)-NOMA and NEH-OFDMA schemes, with improvements of 37.99% and 84.69%, respectively.

Exploring different technology addiction types in young adults: a latent profile analysis

This study aimed to examine the different latent profiles of technology addiction in young adults, and the criteria for belonging to these profiles, in relation to sociodemographic factors (gender and SES) and technology use status (daily social network usage time, daily instant messaging usage time, daily online gaming usage time, daily website usage time, and connection location). Data were collected from a sample of 1,431 young adults [988 (69.1%) female, 443 (30.9%) male]. Latent Profile Analysis (LPA) was used to identify the latent profiles, and Multinomial Regression Analysis was employed to examine the relationships between the latent profiles and sociodemographic and technology use. The LPA revealed four latent profiles of technology addiction in young adults: “Non-Addicts (NA)”, “Moderate Technology Addicts-Gamers (MTAG)”, “High Technology Addicts-Non-Gamers (HTANG)”, and “High Technology Addicts-Gamers (HTAG)”. The criteria for belonging to these four profiles were statistically significantly related to sociodemographic and technology use factors, except for connection location, and age. These findings provided further support for our knowledge about planning programs or multi-layered intervention models to prevent technology addiction in young adults.

Anomaly and intrusion detection using deep learning for software-defined networks: A survey

Software-Defined Networks (SDN) represent an adaptable paradigm for dealing with network users’ dynamic demands. Confidentiality, integrity, and availability are fundamental pillars for the security of the networks, which are often targeted by cyberattacks. The scientific community has been recently exploring deep learning to implement Network Intrusion Detection Systems (NIDS) against network attacks. In this survey, we aim to present an empirical literature review on state-of-the-art NIDS based on deep learning for defending SDNs. The essential steps to develop such systems are carefully examined: benchmark datasets, data preprocessing, deep learning modeling, hyperparameter tuning, and performance evaluation. There has been a growing trend in published works since 2021, underpinning the importance of the research field, which is still active and under investigation. We support the development of the area by discussing the identified open issues and future research directions.

Rethinking the mobile edge for vehicular services

The growing connected car market requires mobile network operators (MNOs) to rethink their network architecture to deliver ultra-reliable low-latency communications. In response, Multi-Access Edge Computing (MEC) has emerged as a solution, enabling the deployment of computing resources at the network edge. For MNOs to tap into the potential benefits of MEC, they need to transform their networks accordingly. Consequently, the primary objective of this study is to design a realistic MEC architecture and corresponding optimal deployment strategy – deciding on the placement and configuration of computing resources – as opposed to prior studies focusing on MEC run-time management and orchestration (e.g., service placement, computation offloading, and user allocation). To cater to the heterogeneous demands of vehicular services, we propose a multi-tier MEC architecture aligned with 5G and Beyond-5G radio access network deployments. Therefore, we frame MEC deployment as an optimization problem within this architecture, assuming 3 MEC tiers. Our data-driven evaluation, grounded in realistic assumptions about network architecture, usage, latency, and cost models, relies on datasets from a major MNO in the UK. We show the benefits of adopting a 3-tier MEC architecture over single-tier (centralized or distributed) architectures for heterogeneous vehicular services, in terms of deployment cost, energy consumption, and robustness.

Entities recommendations using contextual information

Generating entities recommendations has attracted considerable interest in recent years. Most recently published works mainly focus on providing a user with the most relevant and/or personalized entity recommendations that score highly against the query and/or the user’s preference. Some works consider user side information, such as the user network, user relations, and user’s demographic information, and propose to integrate them into the framework of recommender systems. These approaches have been shown to increase the users’ satisfaction and engagement with the system. In this paper, we investigate entities recommender systems and summarize the recent efforts in this domain by categorizing approaches. The first category presents different approaches that utilize knowledge graph as side information. The second category gathers work that consider both the current query, and the users’ previous interactions with the system. These latter works have considered the full user history to personalize the ranking of recommended entities related to the query. In this review paper, we emphasize contextual information-based approaches that utilize user’s context and feedback to improve the recommendations. We accomplished a summary of the literature and synthesized the papers according to different perceptions. Finally, a comparison between approaches is provided and some drawbacks are identified.

LeaDCD: Leadership concept-based method for community detection in social networks

Community discovery plays an essential role in analyzing and understanding the behavior and relationships of users in social networks. For this reason, various algorithms have been developed in the last decade for discovering the optimal community structure. In social networks, some individuals have special characteristics that make them well-known by others. These groups of users are called leaders and often have a significant impact on others, with an exceptional ability to build communities. In this paper, we propose an efficient method to detect communities in social networks using the concept of leadership (LeaDCD). The proposed algorithm mainly involves three phases. First, based on nodes' degree centrality and maximal cliques, some small groups of nodes (leaders) considered as seeds for communities are discovered. Next, unassigned nodes are added to the seeds through an expansion process to generate the initial community structure. Finally, small communities are merged to form the final community structure. To demonstrate the effectiveness of our proposal, we carried out comprehensive experiments on real-world and artificial graphs. The findings indicate that our algorithm outperforms other commonly used methods, demonstrating its high efficiency and reliability in discovering communities within social graphs.

Short-term load forecasting of mixed industrial users in distribution networks using DARTS-LSTM method

Abstract Load forecasting has perennially stood as a fundamental pillar within the power industry, with precision in prognostication constituting a paramount necessity for critical functions including grid management, optimization of energy storage, and response to demand fluctuations. In the present study, we introduce a comprehensive global modeling prediction framework aimed at forecasting large-scale power demand within time series data, alongside the development of a novel short-term power load forecasting model. By integrating all time series data pertaining to the same regression task and employing a unified univariate prediction function across the entire time series dataset, we efficaciously forecast large-scale heterogeneous loads aggregated at various levels within distribution networks. The proposed framework leverages the cutting-edge DARTS (Differentiable Architecture Search) deep learning framework for implementation. Through comprehensive experimentation on real-world data, our model framework demonstrates superior performance in terms of overall prediction accuracy, scalability, and robustness in handling missing and non-stationary time series data.

Demand-side energy sharing mechanism with residential energy storage in active distribution networks

Abstract With the widespread adoption of distributed new energy and residential energy storage (RES), demand-side users in active distribution networks (ADNs) are transitioning from traditional consumers to prosumers. The flexible resources of demand-side prosumers require an appropriate trading mechanism to achieve optimal resource allocation. A demand-side energy sharing mechanism based on residential energy storage is proposed. Firstly, a trading decision model for prosumer units is established by integrating the charge-discharge utility of RES devices with production-consumption characteristics. Then, a decentralized social welfare maximization energy sharing problem for ADNs is constructed using the Alternating Minimization Algorithm (AMA). Furthermore, a 10-node active distribution network is analyzed as an example. The proposed mechanism can maximize social welfare and increase the adaptation level of new energy from 64.58% to 66.83%.

Robust identification of users by convolutional neural network in MATLAB and Raspberry Pi

The following article presents the development of an algorithm embedded in a Raspberry Pi 3B board, where a user identification was made, using the convolutional neural network (CNN) for 5 predefined users, with the option of loading remotely a new network for a new user. Comparatively, the same application was programmed in MATLAB programming software to evaluate the results and identify the advantages between them. Networks were trained for 5 different users, using the Caffe library on the Raspberry Pi, and the MATLAB neural network package on the computer. Where it was found that the training made by Caffe on an embedded system is much slower and less efficient than the ones performed in MATLAB, obtaining less than 55% accuracy with Caffe networks and more than 90% with MATLAB networks, training with the same number of samples, the same architecture, and the same database. Finally, the accuracy obtained through confusion matrix is over 88% in each case of users identification.