Social Network Paradigm Research Articles

Blockchain Based Atonomys De-Centralised Online Social Network

Online social networks (OSNs) have become integral to daily life, yet the prevalent centralized model of these platforms presents considerable challenges in terms of security, privacy, and management. In response to these concerns, a decentralized architecture underpinned by blockchain technology emerges as a promising solution. This paper delves into the development of an OSN service leveraging blockchain technology to facilitate decentralized operation, thereby addressing the aforementioned issues. Through the utilization of the Interplanetary Filesystem (IPFS), large volumes of low-security data are effectively decentralized, contributing to enhanced data integrity and accessibility. Furthermore, the establishment of a decentralized autonomous organization empowers users with greater autonomy, enabling democratic self-governance of the OSN. This innovative approach not only addresses existing shortcomings but also paves the way for a more resilient, transparent, and user-centric social networking paradigm.

The manifestation of brand engagement in self-concept through customer word-of-mouth behavior

ABSTRACT This research investigates brand engagement in self-concept (BESC) and its manifestation on how customers gather, give, and exchange information about a set of brands integral to their self-concept. Despite the importance of word-of-mouth, research has yet to adequately address how customers communicate and exchange their advocacy for these brands across the target actors in their communication ecosystem and modality. We draw on the social network paradigm and social risk and self-enhancement theories and propose a conceptual model tested by data from 471 respondents. Our results suggest that customers are more likely to express the brands related to their self-schema through word-of-mouth equally to both close and distant groups. Specifically, we find that the impact of BESC on close group word-of-mouth is stronger in offline modes, while the effect of BESC on distant group word-of-mouth is stronger in online modes. Strategic marketing implications for offline and online word-of-mouth communication are discussed.

Unraveling Student Engagement: Exploring its Relational and Longitudinal Character

Though higher education scholars have long known that undergraduate student engagement is associated with student persistence, they have yet to fully unravel its character and how it evolves over time. We argue that this is in part due to the individualistic, static ways engagement is measured even though scholars recognize it as a fundamentally relational concept. In our theoretical exploration, we draw on existing thinking about engagement in higher education and fit it to a social network paradigm that is well suited to conceptualizing and measuring relational, multidimensional, and dynamic phenomena such as student engagement. We conclude with suggestions for research and practice to further explore the longitudinal character of student engagement and how network analysis may be employed to explore the roles of engagement in persistence.

Macro and meso level influences on distributed integrated COPD care delivery: a social network perspective

BackgroundCare guidelines for people with chronic obstructive pulmonary disease (COPD) recommend an integrated approach for holistic, flexible, and tailored interventions. Continuity of care is also emphasised. However, many patients with COPD experience fragmented care. Discontinuities in healthcare and related social services are likely to result in disjointed rather than integrated care which can negatively affect patient health outcomes. The purpose of this qualitative study was to improve our understanding of, and how, contextual features pertaining to structures and processes of COPD integrated care influence delivery of care within patients’ healthcare networks.MethodsWe conducted individual interviews with 28 participants (9 patients, 16 healthcare professionals, and 3 spousal caregivers). Participants were recruited through the lung clinic at a city hospital in western Canada. We employed a social network paradigm to analyse and interpret the data.ResultsThe analysis revealed an overarching theme of fragmented COPD care with two sub-themes: (1) Funding shortfalls and availability of resources, and (2) Dis(mis)connected communication pathways. The overarching theme depicts variations, delays, and discontinuities in patient care. The sub-themes describe how macro level influences and meso level shortfalls were perceived to influence the availability of respiratory care resources that contributed to fragmented COPD care.ConclusionsEmploying a social network lens drew particular attention to family physicians’ pivotal role in delivering community-based COPD care. While an integrated approach to care is recommended by care guidelines, institutional and organizational structures and processes, such as financial and communication structures, may inhibit delivery of integrated care. Thus, macro and meso level structures and processes have the potential to shape patient care by constraining family physicians’ purposive and communication actions necessary for facilitating an integrated distributed approach to care. We propose a context of care which fosters a context for family physicians’ delivery of patient-centered care. Integrated care delivery may improve patients’ wellbeing and alleviate financial constraints on the healthcare system.

Containing COVID-19 Pandemic using Community Detection

Diseases such as COVID-19 that quickly spread through social contact where Infections remain undetected for long pose new kinds of challenges. Traditional epidemic models when applied to COVID-19 not only give highly alarmist predictions but also fail to indicate any way to target interventions. The models also do not factor social structure/context/dynamics.. To address this challenge we propose a novel Social Infection Analysis Model explain the intuition behind it and a methodology that factors social structure while performing the analysis. SIAM is particularly beneficial as standard epidemic models are dependent on wide-spread testing. Our methodology includes (i) modelling society/localities using social network paradigm and analysis at macro level (ii) detecting communities and (iii) analysis of infection spread within a community over time. To arrive at an outlook for infection spread within communities we make use of the NK Model for solving complex, long term problems coupled with agent based modeling for member behavior. We make use of NetLogo to run simulations. We intend to validate our research in Indian context.

DTCMS: Dynamic traffic congestion management in Social Internet of Vehicles (SIoV)

With the augmentation of traffic exponentially, we observe that traffic congestion does not guarantee road safety or enhance the driving experience. In the recent past, Social Internet of Vehicles (SIoV), a social network paradigm permits social relationships among every vehicle in the network or with any road infrastructure to render a radically useful environment. SIoV is beneficial for the drivers, in improving road safety, avoiding mishaps, and providing a friendly-driving experience. In this paper, we propose a traffic scheduling algorithm to gain the maximum throughput for the flow of vehicles at a road intersection with the formation of social relationships among the vehicles and with the Road Side Units (RSUs). The algorithm estimates the flow rate of vehicles for lanes at the intersections exploiting the volume of traffic moving through the given road. A condition matrix is designed for the consistent movement of traffic considering different routes on the road segments. Social relationships are devised on various aspects of travel needs for a safe, agile, and better driving experience. Simulation results illustrate the efficacy of the proposed scheme with high traffic throughput, service rate and reduce the total travelling time, delay time, and average waiting time in comparison with Dynamic Throughput Maximization Framework and Adaptive Traffic Control Algorithm.

Hybrid convolutional neural network (CNN) and long-short term memory (LSTM) based deep learning model for detecting shilling attack in the social-aware network

In social aware network (SAN) paradigm, the fundamental activities concentrate on exploring the behavior and attributes of the users. This investigation of user characteristic aids in the design of highly efficient and suitable protocols. In particular, the shilling attack introduces a high degree of vulnerability into the recommender systems. The shilling attackers use the reviews, user ratings and forged user generated content data for the computation of recommendation rankings. The detection of shilling attack in recommender systems is considered to be essential for sustaining their fairness and reliability. In specific, the collaborative filtering strategies utilized for detecting shilling attackers through efficient user behavior mining are considered as the predominant methodologies in the literature. In this paper, a hybrid convolutional neural network (CNN) and long-short term memory (LSTM)-based deep learning model (CNN–LSTM) is proposed for detecting shilling attack in recommender systems. This deep learning model utilizes the transformed network architecture for exploiting the deep-level attributes derived from user rated profiles. It overcomes the limitations of the existing shilling attack detection methods which mostly focuses on identifying spam users by designing features artificially in order to enhance their efficiency and robustness. It is also potent in elucidating deep-level features for efficiently detecting shilling attacks by accurately elaborating the user ratings. The experimental results confirmed the significance of the proposed CNN–LSTM approach by accurately detecting most of the obfuscated attacks compared to the state-of-art algorithms used for investigation.

Dynamic Management of Traffic Signals through Social IoT

Abstract Traffic congestion is a major threat to transportation sector in every urban city around the world. This causes many adverse effects like, heavy fuel consumption, increased waiting time, pollution, etc. and pose an eminent challenge to the movement of emergency vehicles. To achieve better driving we proceed towards a trending research field called Social Internet of Vehicles (SIoV). A social network paradigm that permits the establishment of social relationships among every vehicle in the network or with any road infrastructure can be radically helpful. This holds as the aim of SIoV, to be beneficial for the drivers, in improving the road safety, avoiding mishaps, and have a friendly-driving environment. In this paper, we propose a Dynamic congestion control with Throughput Maximization scheme based on Social Aspect (D-TMSA) utilizing the social, behavioral and preference-based relationships. Our proposed scheme along with the various social relationship types allocates green signal to maximize the traffic flow passing through an intersection. Simulation results show that the D-TMSA outperforms the existing work by achieving high throughput, lowering the total traveling time and reducing the average waiting time to better the flow of traffic based on their social attributes with each other.

Maintenance Service Platform (MSP) for maintenance information collection and sharing

The MSP is a novel software tool for collecting and sharing on factory and company levels maintenance relevant data and information related to maintenance operations and machine conditions. This tool, developed following the social network paradigm, aims at facilitating the communication with and between the workers, where the users can freely connect, communicate, and share information in a personal way. At the same time, the tool facilitates the exchange of information and sends notifications about the conditions of the production equipment, current and planned maintenance activities, comments related to current production situations, problems related to production activities and much more. The development of the MSP tool was carried out in the PROGRAMS project (GA #767287).

The MIoT paradigm: Main features and an “ad-hoc” crawler

The Internet of Things (IoT) is currently considered the new frontier of the Internet, and a lot of research results about this topic can be found in the literature. One of the most effective ways to investigate and implement IoT is based on the use of the social network paradigm: Social Internet of Things (SIoT) is an excellent attempt in this direction. In the last years, social network researchers have introduced new paradigms capable of capturing the growing complexity of this scenario. One of the most known of them is Social Internetworking System, which models a scenario comprising several related social networks. In this paper, we investigate the possibility of applying the ideas underlying Social Internetworking System to IoT, and we propose a new paradigm, called MIoT (Multiple Internets of Things), capable of modeling and handling the increasing complexity of this last context. Furthermore, in order to facilitate knowledge extraction and exploitation in presence of a huge number of things, we also propose a crawler specifically designed for an MIoT. Finally, through an experimental campaign, we show that classical crawlers are not adequate for MIoTs, whereas our own is well suited and outperforms all of them in this context.

Text-Based Intelligent Learning Emotion System

Nowadays, millions of users use many social media systems every day. These services produce massive messages, which play a vital role in the social networking paradigm. As we see, an intelligent learning emotion system is desperately needed for detecting emotion among these messages. This system could be suitable in understanding users’ feelings towards particular discussion. This paper proposes a text-based emotion recognition approach that uses personal text data to recognize user’s current emotion. The proposed approach applies Dominant Meaning Technique to recognize user’s emotion. The paper reports promising experiential results on the tested dataset based on the proposed algorithm.

Social Media and Dance/Movement Therapy: Reciprocity, Collaboration, and Relationship

Dawning at the onset of the twenty first century, social networking has emerged as a catalyst of global significance that, if embraced strategically, may fuel the growth of dance/movement therapy (DMT) in exponential ways. In the few years that the American Dance Therapy Association (ADTA) has utilized social media for public relations and advocacy purposes, both ADTA membership and the general public’s awareness of DMT have already increased markedly. Inherent within the social networking paradigm is the essential role of relationship and reciprocity. These tenets encourage a cross-promotion, not only of dance/movement therapists by dance/movement therapists but of all creative arts therapists by all creative arts therapists. Fully maximizing the growth potential for the field of DMT demands a shift from a twentieth century mindset of isolationism to the social media inspired, twenty first century paradigm of collaboration and mutual support among all creative arts therapists. Fully committing to this paradigm will produce greater awareness of DMT in the general public, a deeper understanding of DMT by allied professionals, an increased consumer demand for our services, a more diverse pool of dance/movement therapists, and a visibility worthy of a profession beginning its second half century.

The Impact of Electronic Word-of-Mouth on Online Impulse Buying Behavior: The Moderating role of Big 5 Personality Traits

Purpose of the paper: The purpose of this study is to investigate the moderating role of Big 5 personality traits in the relationship between electronic word-of- mouth and impulse buying behavior. Design/methodology/approach: Data was gathered through self-administered questionnaire from a sample of 266 Chinese and Pakistani students who were online users and studying at various institutes located in the capital city Islamabad, Pakistan. Findings: As hypothesized, impulsive buying behavior is significantly associated with electronic word-ofmouth and moderating effect of Big 5 personality traits was also substantiated. The data was analysed statistically using IBM SPSS Statistics 22 to find out correlation and regression analysis between study variables, reliability of research instrument, strength of relationship between independent and dependent variables, moderating effect of Big 5 personality traits in the relationship between electronic word-of- mouth and impulse buying behavior was also substantiated. Research limitations/implications: Future researches may replicate our model with a larger sample size in order to enhance generalizability. It will be also fruitful to extend this research to various other online shopping environments. Practical implications: Implication suggests that online retailers and designers of shopping websites should understand the importance of online social interactions in order to encourage consumers’ online buying patterns. They should integrate elements of online social interaction, together with reviews that customer placed online on websites, buying pattern, history and sales volume on their websites for the transformation of eWOM. What is original/value of paper: The social network paradigm and UGT are confirmed and validated by current study in online word-of-mouth context. This study stands as a pioneer that empirically scrutinize the moderating effect of these traits and reveals that online social interactions can strongly enhances consumers’ purchase on impulse when individuals display the traits of i.e. extroversion, openness

PIF: A Personalized Fine-Grained Spam Filtering Scheme With Privacy Preservation in Mobile Social Networks

Mobile social network (MSN) emerges as a promising social network paradigm that enables mobile users' information sharing in the proximity and facilitates their cyber-physical-social interactions. As the advertisements, rumors, and spams spread in MSNs, it is necessary to filter spams before they arrive at the recipients to make the MSN energy efficient. To this end, we propose a personalized fine-grained filtering scheme (PIF) with privacy preservation in MSNs. Specifically, we first develop a social-assisted filter distribution scheme, where the filter creators send filters to their social friends (i.e., filter holders). These fil- ter holders store filters and decide to block spams or relay the desired packets through coarse-grained and fine-grained keyword filtering schemes. Meanwhile, the developed cryptographic filter- ing schemes protect creator's private information (i.e., keyword) embedded in the filters from directly disclosing to other users. In addition, we establish a Merkle Hash tree to store filters as leaf nodes where filter creators can check if the distributed filters need to be updated by retrieving the value of root node. It is demon- strated that the PIF can protect users' private keywords included in the filter from disclosure to others and detect forged filters. We also conduct the trace-driven simulations to show that the PIF can not only filter spams efficiently but also achieve high delivery ratio and low latency with acceptable resource consumption.

Mobile social networking application viability: a research framework

Purpose – The purpose of this article is to outline a conceptual framework on mobile applications that support social interactions among users to warrant commercial viability of such applications. Design/methodology/approach – We build on the social network paradigm to propose an activity-based view on mobile application usage, and theoretically link the pertinent features of mobile social networking applications (MSNAs) to a set of measurement metrics concerning their commercial viability. Findings – The conceptual framework formulated highlights how MSNAs can be systematically designed and deployed to ensure their commercial viability. Research limitations/implications – A pertinent set of features that support social networking among the users, and their plausible mechanisms in facilitating the commercial viability of MSNAs, are explicated. This facilitates future research endeavours in systematically investigating the emerging form of mobile applications. The limitation lies in a lack of empirical data to validate the formulated propositions, which is beyond the scope of this paper and is directed for future research. Practical implications – A coherent set of measurement metrics are put forward for practitioners to measure the commercial viability of an MSNA. Also theoretically based insights are derived for how they can better derive commercial values from the emerging form of mobile applications. Originality/value – Most relevant previous research has focused either on the MSNAs’ technical design aspects or user behavioural issues. This research ties up the themes on technical design, user behaviour and business consideration in formulating a research framework for assessing the commercial viability of MSNAs.

BASA: building mobile Ad-Hoc social networks on top of android

Mobile ad-hoc social networks (MASNs) are emerging as a self-configuring and self-organizing social networking paradigm, which enhance local interactions among mobile and handheld device users. However, the MASNs cannot be directly derived on demand for various Android systems from existing social networks (SNs) without having access to end-to-end IT network infrastructure. In this article, we propose a detailed solution called BASA which would help in rapidly building local mobile ad-hoc social networks on top of the current Android platform. BASA establishes a four-layer system architecture according to the underlying challenges and requirements in MASNs. BASA is implemented in an EU project named SOCITIES. Our prototype shows that BASA is flexible to expedite various services.

Secure Encounter-Based Mobile Social Networks: Requirements, Designs, and Tradeoffs

Encounter-based social networks and encounter-based systems link users who share a location at the same time, as opposed to the traditional social network paradigm of linking users who have an offline friendship. This new approach presents challenges that are fundamentally different from those tackled by previous social network designs. In this paper, we explore the functional and security requirements for these new systems, such as availability, security, and privacy, and present several design options for building secure encounter-based social networks. To highlight these challenges, we examine one recently proposed encounter-based social network design and compare it to a set of idealized security and functionality requirements. We show that it is vulnerable to several attacks, including impersonation, collusion, and privacy breaching, even though it was designed specifically for security. Mindful of the possible pitfalls, we construct a flexible framework for secure encounter-based social networks, which can be used to construct networks that offer different security, privacy, and availability guarantees. We describe two example constructions derived from this framework, and consider each in terms of the ideal requirements. Some of our new designs fulfill more requirements in terms of system security, reliability, and privacy than previous work. We also evaluate real-world performance of one of our designs by implementing a proof-of-concept iPhone application called MeetUp. Experiments highlight the potential of our system and hint at the deployability of our designs on a large scale.

Social-Network-Sourced Big Data Analytics

Very large datasets, also known as big data, originate from many domains. Deriving knowledge is more difficult than ever when we must do it by intricately processing this big data. Leveraging the social network paradigm could enable a level of collaboration to help solve big data processing challenges. Here, the authors explore using personal ad hoc clouds comprising individuals in social networks to address such challenges.

Drivers of in‐group and out‐of‐group electronic word‐of‐mouth (eWOM)

PurposeThe purpose of this study is to address a recent call for additional research on electronic word‐of‐mouth (eWOM). In response to this call, this study draws on the social network paradigm and the uses and gratification theory (UGT) to propose and empirically test a conceptual framework of key drivers of two types of eWOM, namely in‐group and out‐of‐group.Design/methodology/approachThe proposed model, which examines the impact of usage motivations on eWOM in‐group and eWOM out‐of‐group, is tested in a sample of 302 internet users in Portugal.FindingsResults from the survey show that the different drivers (i.e. mood‐enhancement, escapism, experiential learning and social interaction) vary in terms of their impact on the two different types of eWOM. Surprisingly, while results show a positive relationship between experiential learning and eWOM out‐of‐group, no relationship is found between experiential learning and eWOM in‐group.Research limitations/implicationsThis is the first study investigating the drivers of both eWOM in‐group and eWOM out‐of‐group. Additional research in this area will contribute to the development of a general theory of eWOM.Practical implicationsBy understanding the drivers of different eWOM types, this study provides guidance to marketing managers on how to allocate resources more efficiently in order to achieve the company's strategic objectives.Originality/valueNo published study has investigated the determinants of these two types of eWOM. This is the first study offering empirical considerations of how the various drivers differentially impact eWOM in‐group and eWOM out‐of‐group.

The social network paradigm and applications in pharmacy

There have been calls for research to include contextual and macrolevel factors within explanatory models of human health. A network approach focuses on the influence of relationships to predict behavior. The first objective was to describe the social network paradigm based in sociology that uses network principles and methods to visualize, quantify, and predict outcomes using the structure and function of relationships. The second objective was to discuss applications of this approach in the context of health to guide future research in pharmacy. This was a descriptive overview of conceptual models, methods, measures, and analyses that are used in the social network paradigm. The social network paradigm contains conceptual models that rely on relational and structural assumptions to make predictions related to human behavior. Although there is not 1 dominate theory of social networks, several models hold across applications and are commonly used by scholars. The methodology emphasized considerations of network boundaries, sampling strategies, and the type of data collected. Specific variables used in social network analysis were defined and dichotomized into constructs of centrality and cohesion. Network analysis was described in terms of available computational programs, data management, and statistical testing. Related research in health care was applied and ideas for pharmacy were proposed using a social network-based theoretical model. There is growing momentum for health behaviorists to gain familiarity for studying the effect of social networks on behavior. Applications in pharmacy using social network models, methods, and analysis can be used as a stand-alone perspective or in conjunction with other theoretical perspectives in an effort to explain human health or organizational behavior in health care settings.