Online Social Networks Research Articles

Effects of social distancing during COVID-19 on informal learning behaviours and work performance: the role of organisational remote working cultures

ABSTRACT We explored the impact of social distancing and the subsequent shift to remote work during the COVID-19 pandemic on informal learning behaviours and work performance. To accomplish this, we conducted a large-scale international study at the peak of the pandemic in 2021, utilising self-report questionnaires distributed via online social business networks, with a total of N = 1,236 participants. In our study, we developed a research model wherein social distancing served as the independent variable and remote working cultures as the moderating variable. This model was validated using confirmatory factor analysis and regression analysis techniques. Our investigation affirms that social distancing had adverse effects on informal learning within organisations. Additionally, it suggests that social distancing alone does not necessarily cultivate a remote working culture. However, our study reveals that a remote working culture can mitigate the repercussions of social distancing. Consequently, we observed positive effects on work performance through informal learning, even amidst the pandemic. A key outcome is that business leaders should promote a remote work culture and, based on this framework, establish hybrid work arrangements that effectively combine in-person and remote work.

Privacy Preserving Using K Member Gaussian Kernel Fuzzy C Means and Self Adaptive Honey Badger for Online Social Networks

An online social network (OSN) gives users a strong platform to communicate and exchange information. Protecting publicly published data from individual identification is the primary problem in sharing social network databases. The most popular method for protecting privacy is anonymizing data, which involves deleting or altering some information while maintaining as much of the original data as feasible. This work presents a combination anonymizing algorithm, which is based on k member Gaussian kernel fuzzy c means clustering and self-adaptive honey badger optimization technique (KGKFCM-SAHBO). As part of the suggested anonymization process, the various users are divided into C clusters, each of which has at least K users, using a K-member Gaussian kernel fuzzy c means clustering technique. After that, the primary clusters are further optimized using the self-adaptive honey badger optimization approach (SAHBO) to further anonymize the data and network graph. Using the Yelp dataset, the experimental findings demonstrate the efficiency of the proposed model and evaluate a number of parameters, including execution time, degree of anonymization, and information loss. The experimental results show that the proposed strategy reduced information loss and improved the degree of anonymization when compared to existing methodologies.

A Federated Social Recommendation Approach with Enhanced Hypergraph Neural Network

In recent years, the development of online social network platforms has led to increased research efforts in social recommendation systems. Unlike traditional recommendation systems, social recommendation systems utilize both user-item interactions and user-user social relations to recommend relevant items, taking into account social homophily and social influence. Graph neural network (GNN) based social recommendation methods have been proposed to model these item interactions and social relations effectively. However, existing GNN-based methods rely on centralized training, which raises privacy concerns and faces challenges in data collection due to regulations and privacy restrictions. Federated learning has emerged as a privacy-preserving alternative. Combining federated learning with GNN-based methods for social recommendation can leverage their respective advantages, but it also introduces new challenges: 1) existing federated recommendation systems often lack the capability to process heterogeneous data, such as user-item interactions and social relations; 2) due to the sparsity of data distributed across different clients, capturing the higher-order relationship information among users becomes challenging and is often overlooked by most federated recommendation systems. To overcome these challenges, we propose a federated social recommendation approach with enhanced hypergraph neural network. We introduce hypergraph graph neural networks (HGNN) to learn user and item embeddings in federated recommendation systems, leveraging the hypergraph structure to address the heterogeneity of data. Based on carefully crafted triangular motifs, we merge user and item nodes to construct hypergraphs on local clients, capturing specific triangular relations. Multiple HGNN channels are used to encode different categories of high-order relations, and an attention mechanism is applied to aggregate the embedded information from these channels. Our experiments on real-world social recommendation datasets demonstrate the effectiveness of the proposed approach. Extensive experiment results on three publicly available datasets validate the effectiveness of the proposed method.

Modeling the dissemination of privacy information in online social networks

AbstractDriven by the rapid development of information technology, online social networks (OSNs) have experienced a fast development in recent years, allowing increasingly more people to share and spread information over OSNs. The rapid rise of OSN platforms such as Facebook and Twitter is sufficient evidence of such development. As one type of information, privacy information can also be created and disseminated over an OSN, posing a severe threat to individual privacy. This article attempts to construct a model for disseminating privacy information in OSNs and to analyze the model by simulating the dissemination process of privacy information in OSNs. First, we establish network models that exhibit the main characteristics of OSNs. Second, by considering the factors related to social relationships, especially intimacy between users and the attention of users to the privacy subject, we derive the parameters for privacy information dissemination models in OSNs. Third, based on the theory of information dissemination dynamics, we construct a model for information dissemination that conforms to the properties of privacy information. We also present some experimental results based on the constructed model and analyze the characteristics of privacy information dissemination. Fourth, we study and verify the various properties of the model through a set of experiments. The proposed model provides the opportunity to better understand the dynamics of privacy information dissemination in OSNs and the effect of user behavior on dissemination.

Enriching the Fake News Process of Classifying Model using Deep Learning and Machine Learning Algorithm

Fake news detection has become a pressing issue due to the rapid propagation of misinformation through various online platforms. In recent years, due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large numbers and widespread in the online world. An important goal in improving the trustworthiness of information in online social networks is to identify the fake news timely. This paper aims at investigating the principles, methodologies and algorithms for detecting fake news articles, creators and subjects from online social networks and evaluating the corresponding performance. Information carefulness on Internet, especially on social media, is an increasingly important concern, but web-scale data hampers, ability to identify, evaluate and correct such data, or so called "fake news," present in these platforms. In this paper, we propose a method for "fake news" detection and ways to apply it on Facebook, one of the most popular online social media platforms. This method uses Naive Bayes classification model to predict whether a post on Facebook will be labelled as real or fake.

Illuminating the Relationship Between Social Networking Usage and Job Performance Through the Work–Life Balance of Gen-Y Employees in Corporate Sector

The use of social media, such as social networking sites continues to grow in everyday life, along with their use in the workplace. So, a concern raised about how the personal use of social networking sites like Facebook, WhatsApp, Instagram, etc., at work affects employee job performance. Moreover, the purpose of this study is to explore the relationship between social networking usage and employee job performance through the mediating role of work–life balance. The quantitative methodology was employed in this study. Data was collected using a self-administered questionnaire adapted from a previously validated survey. The target population consisted of Gen-Y employees of the corporate sector in Chandigarh, Delhi, and Haryana (UTs/states) of India. Judgemental sampling was applied to collect data from 224 employees of the corporate sector. The study results indicated that time spent, frequency of using social networking sites has significant relationship with job performance and work–life balance of employees while work–life balance and employee job performance are significantly positively related. However, social networking use has a significant negative relationship with job performance and work–life balance. Additionally, work–life balance significantly mediates the relationship between social networking usage and job performance. Therefore, the study concluded that employees struggle to set boundaries since they cannot distinguish between work and family life due to the rise in technology and social media use. Therefore, managers and HR professionals need to consider the negative aspect of online social networking at work. Further, the implications for managers and organizations are discussed.

Exploring Trust Dynamics in Online Social Networks: A Social Network Analysis Perspective

This study explores trust dynamics within online social networks, blending social science theories with advanced machine-learning (ML) techniques. We examine trust’s multifaceted nature—definitions, types, and mechanisms for its establishment and maintenance—and analyze social network structures through graph theory. Employing a diverse array of ML models (e.g., KNN, SVM, Naive Bayes, Gradient Boosting, and Neural Networks), we predict connection strengths on Facebook, focusing on model performance metrics such as accuracy, precision, recall, and F1-score. Our methodology, executed in Python using the Anaconda distribution, unveils insights into trust formation and sustainability on social media, highlighting the potent application of ML in understanding these dynamics. Challenges, including the complexity of modeling social behaviors and ethical data use concerns, are discussed, emphasizing the need for continued innovation. Our findings contribute to the discourse on trust in social networks and suggest future research directions, including the application of our methodologies to other platforms and the study of online trust over time. This work not only advances the academic understanding of digital social interactions but also offers practical implications for developers, policymakers, and online communities.

Dispelling the Fake: Social Bot Detection Based on Edge Confidence Evaluation.

Social bot detection is essential for maintaining the safety and integrity of online social networks (OSNs). Graph neural networks (GNNs) have emerged as a promising solution. Mainstream GNN-based social bot detection methods learn rich user representations by recursively performing message passing along user-user interaction edges, where users are treated as nodes and their relationships as edges. However, these methods face challenges when detecting advanced bots interacting with genuine accounts. Interaction with real accounts results in the graph structure containing camouflaged and unreliable edges. These unreliable edges interfere with the differentiation between bot and human representations, and the iterative graph encoding process amplifies this unreliability. In this article, we propose a social Bot detection method based on Edge Confidence Evaluation (BECE). Our model incorporates an edge confidence evaluation module that assesses the reliability of the edges and identifies the unreliable edges. Specifically, we design features for edges based on the representation of user nodes and introduce parameterized Gaussian distributions to map the edge embeddings into a latent semantic space. We optimize these embeddings by minimizing Kullback-Leibler (KL) divergence from the standard distribution and evaluate their confidence based on edge representation. Experimental results on three real-world datasets demonstrate that BECE is effective and superior in social bot detection. Additionally, experimental results on six widely used GNN architectures demonstrate that our proposed edge confidence evaluation module can be used as a plug-in to improve detection performance.

The role of big data and IoT in logistics supply chain management of e-commerce

With the rapid development of social mode virtualization and electronic component technology, the application of data science and Internet of Things (IoT) technology in the field of e-commerce is gradually increasing. This study aims to explore how these emerging technologies can enhance the advantage of Chinese e-commerce companies in international competition. By comprehensively analyzing the massive data generated by online social networking and the application of IoT sensor technology in logistics and enterprise management, this paper proposes a decision support model based on data analysis. Research methods include data collection, data analysis and case studies. The results of the study show that data analytics and IoT technologies can effectively improve the efficiency of e-commerce operations and customer experience. The conclusion is that these technologies not only contribute to the domestic development of e-commerce enterprises, but also play a non-negligible role in international competition. This research has important implications for understanding the practical applications and potential of new technologies in the field of e-commerce.

Corporate learning in a disruptive digital era: a new global organizational paradigma (A case-study of IBM’s Your Learning Platform)

In a digital world when the technologies as well as the market places are evolving at an alarming pace, facilitating the employees to acquire newer knowledge and helping them advance their learning is most challenging than ever before. Staying agile and to become and remain ahead of competition in this swiftly changing business landscape is a daunting task indeed for companies. Corporate universities and Classrooms in B-schools have totally been overshadowed and got undermined by newer versions of learning and we are witnessing that the buffet of teaching employees is expanding and changing more than ever before. In addition to tracking down a plethora of in-person opportunities in the form of mentorships and training, employees are able to use their computers and smart phones to sift through facilely rich content online at any time and place. It may be in the form of online social media networks, published papers, as well as videos and courses. Nowadays, mobile and cloud technologies, and social networks are taken for granted by the tech-savvy modern employees. And not surprisingly, the millennials believe that technology makes them more effective and productive and hence always prefer to communicate through the electronic mode at workplace than face to face or for that matter even the telephone. They are found to be using technology only routinely. This Paper addresses the important challenge of shifting learning to the 21st Century platform economy faced by corporates and discusses how IBM has successfully tackled the same through its Your Learning Platform.

NDI-PPO: Node degree increment-based proximal policy optimization method for employment community discovery in online social networks

Purpose: aiming at the limitations of pre-input parameters in the complex network overlapping community discovery algorithm based on tag propagation in real networks and the problems of tag redundancy, method: a node degree increment-based proximal policy optimization method for community discovery in online social networks is proposed (named NDI-PPO). Process: by applying the cohesion idea and introducing the concept of modularity increment, a social network great community is constructed from the bottom up according to the criteria of community division. For the problem that the number of iterative steps is sensitive to the strategy gradient algorithm, we adopt an improved PPO to improve the efficiency of feature extraction. In label updating, the maximum clique is used as the core unit to update the labels and weights of the maximum maximum clique adjacent nodes from the center to the periphery using intimacy, and the weights of the non-maximum maximum clique adjacent nodes are updated by means of the maximum weight. In the post-processing stage, the adaptive threshold method is used to remove the noise in the node label, which effectively overcomes the limitation of the number of pre-input overlapping communities in the real network. Result: The simulation results show that the proposed community discovery algorithm NDI-PPO is superior to other advanced algorithms, the time complexity is greatly reduced, and it is suitable for community discovery in large social networks.

A Review of Machine Learning Approaches for Rumour Detection: Techniques, Challenges, and Future Directions (Machine Learning for Rumour Detection)

Rumours and misinformation propagate rapidly across online social networks, posing significant challenges to maintaining the integrity of information dissemination. In recent years, machine learning (ML) techniques have emerged as promising tools for automating the detection and mitigation of rumours. This review paper provides a comprehensive examination of the advancements in rumour detection using ML approaches. The paper begins by outlining the landscape of rumour dissemination in online social networks, highlighting the characteristics and challenges associated with rumour detection. Subsequently, it systematically categorizes and analyzes various ML methods employed for rumour detection, including supervised, unsupervised, and semi-supervised learning approaches. Furthermore, the review delves into the diverse features and representations utilized in ML models for rumour detection, such as textual content, user engagement patterns, network structures, and temporal dynamics. It discusses the strengths and limitations of different feature sets and their impact on the effectiveness of rumour detection systems. Moreover, the paper explores the intricacies of dataset construction and evaluation methodologies for training and testing rumour detection models. It examines commonly used benchmark datasets and evaluation metrics, emphasizing the importance of robust evaluation frameworks for assessing the performance of ML-based rumour detection systems accurately. Additionally, the review identifies key challenges and open research questions in the field of rumour detection using ML, including handling evolving rumour patterns, addressing adversarial attacks, and enhancing the interpretability and explain ability of ML models. It also discusses potential directions for future research aimed at advancing the state-of-the-art in rumour detection and mitigation.

Creative webs: decoding the creativity potential of Twitter followers.

Previous studies have associated creativity to one's social networks. This study builds on this framework and examines the premise that one's social fabric is a catalyst for creativity, specifically probing the dynamics between online social connections and creative expressions in the realm of Esports. Therefore, this research paper examined a correlation between people's creativity and their Effective Network Size (non-redundant ties) on Twitter, to see if potentially non-redundant information is related to creativity. Creativity score was defined as the propensity of a participant to utilize specific terms relating to Esports in its emerging stages, prior to its peak popularity as evidenced by Google Trends. Effective Network Size was analyzed based on the social ties of participants on Twitter (N = 50,000). The findings indicate that the higher the Effective Network Size score, the higher the creativity score. Furthermore, geographically dispersed social networks moderated the relationship between Effective Network Size and creativity. For people with more dispersed social networks, ENS was more constructive for creativity. These findings are discussed in the broader context of the relevancy of online social networking sites for creativity.

Intimacy as a competency: information-seeking practices in (marriage) migration online support groups

This article explores the use of online social networks for seeking and sharing information about marriage migration. In Europe, since the 1990s, this migration has faced heightened scrutiny. Laws and administrative practices have added complexity to immigration procedures. Manifold screening methods gauge the authenticity of relationships aligning with the host nation’s concept of a suitable family for integration. In this context, informal self-help groups emerged to offer support to those facing burdensome formalities and local administrative intricacies. Based on extensive qualitative fieldwork, this article examines the significance of these support groups, drawing on the concept of intimacy as a shared competency. Here, intimacy is conceived as an active relational skill that counterbalances the limitations of migration policies. The analysis transcends the division between online and offline modes of living, shedding new light on intimacy and extimité – the sharing of one’s intimate self with others for validation – in doing family.

익명표현의 자유에 대한 비판적 고찰

With the introduction of the Internet real-name system and the Constitutional Court ruling in the early 2010s, multiple research has been executed on the Constitutional Court’s ruling. And as a result, research on freedom of expression and freedom of anonymous expression was intensively conducted until the mid-2010s. Since then, however, almost no research has been conducted on freedom of anonymous expression. Considering the development and changes in online media and Social Networking Services since 2010, the issue needs to be revisited. The content and scope of freedom of expression in ancient Greece and even in the 17th century were very different from today. Even John Milton, the author of Areopagitica, thought that the freedom of the Catholics did not need to be guaranteed. Even after Areopagitica, freedom of expression was emphasized as a very important element during the French Revolution in the process of America’s separation from Britain. The issue of anonymity in freedom of expression has been a continuous issue since the 14th century. Regarding freedom of anonymous expression, the Constitutional Court ruled that the Internet real-name system was unconstitutional in 2012, and about the real-name Internet bulletin board system during the election campaign, it ruled that it was constitutional in 2010 and 2015, but changed its position and ruled it unconstitutional in 2021. However, the Constitutional Court ruled that the identity verification system on public institution bulletin boards was constitutional in 2022, making it clear that freedom of anonymous expression cannot be guaranteed indefinitely. When considering the fact that public bulletin boards are a channel through which the general public can convey their opinions in the realm of ‘nation vs. individual,’ such a decision does not seem to be desirable. Today's media environment is very different from the past. The biggest change is that, unlike in the past, individuals produce more information than any other information-producers. However, in the process, anonymity serves as one of the main causes of fake news and hate speech. Considering all the circumstances, the anonymity in freedom of expression should be guaranteed to the maximum extent possible in the ‘individual-to-state’ dimension, but it would be desirable to guarantee it to a relatively weak level in the ‘individual-to-individual’ domain.

The Influence Mechanism of Online Social Network Relationships on Sustainable Entrepreneurial Success

Affected by economic uncertainty, the success rate of sustainable entrepreneurship is not optimistic at present. Effectively improving the success rate of sustainable entrepreneurship is of great significance for ensuring and maintaining sustainable development for long-term socioeconomic and environmental sustainability. Based on social network theory, entrepreneurial capital theory, psychological capital theory, and the literature on sustainable entrepreneurship, this study explores the influence mechanism of online social network relationships on sustainable entrepreneurial success from the perspective of those relationships, dividing entrepreneurs’ abilities into an online relationship-building ability and online relationship coordination ability. We used an empirical study with 346 valid questionnaires. The results of a PLS-SEM analysis show that online social network relationships have a significant impact on sustainable entrepreneurial capital, in which online relationship-building capacity has a similar impact on both sustainable entrepreneurial resource integration and acquisition; online relationship-building capacity can positively affect both transactional and interpersonal psychological capital; sustainable entrepreneurial resource integration can be more effective than resource acquisition in promoting sustainable entrepreneurial success through the use of social media; and transactional psychological capital has a more significant impact on sustainable entrepreneurial success than does interpersonal psychological capital. This study provides new insights into how entrepreneurs can fully utilize their online social network relationships to achieve entrepreneurial success.

The revenue model of mainstream online social networks and potential alternatives: A scenario-based evaluation by German adolescents and adults

Mainstream Online Social Networks (OSNs) face extensive criticism for their revenue model and its negative consequences for users and societies. While experts often discuss alternative OSN models, little is known about potential users’ evaluations of different OSN models. To close this research gap, the present study investigates how non-expert individuals, i.e., potential users, evaluate mainstream OSNs and expert-proposed alternative models. Next to the general evaluations of different models, individual differences among users open to various OSN models were explored.Three OSN scenarios were created based on interviews with experts from academia and industry and presented to samples of German adolescents (N = 1166) and adults (N = 878): one describing the “status quo”, one “option” model similar to the model currently considered by Meta, and one describing a “public-service” broadcasting OSN. Participants rated each characteristic of the “status quo” scenario, indicated their willingness to pay for each of the three OSN models, and specified their preferred OSN model next to providing sociodemographic information.Replicated across both samples was that the “status quo” scenario received predominantly negative evaluations. Further replicated across both samples was that most participants were willing to pay for a “public-service” OSN and chose this model as the preferred one. Only a few significant relations of sociodemographic variables were observed. Consistently replicated across both samples, men rated various characteristics of the “status quo” scenario more positively, and women were more likely to prefer the “public-service” OSN. Some differences between the two samples also emerged.In summary, both adolescents and adults demonstrated receptivity to alternative OSN models, especially a public-service broadcasting OSN. Most sociodemographic factors had limited influence indicating potential widespread adoption if such an alternative was implemented – at least in Germany. Consequences arising from such a model for platform design, policy, regulation, and governance are discussed.

Dynamic modeling and simulation of double-rumor spreaders in online social networks with IS2TR model

Dynamic modeling and simulation of double-rumor spreaders in online social networks with IS2TR model

The effects of social capital deprivation for wellbeing: Evidence from the Covid-19 pandemic

The paper explores the relationship between the Covid-19 pandemic, subjective wellbeing and social capital in the UK. I exploit the pandemic as a quasi-natural experiment of an exogenously induced reduction of social capital as an explanation of the frequently documented reduction in wellbeing during the pandemic period. Differently from the literature, results show that after the onset of the pandemic - when the imposition of restrictions to social interactions occurred - both mental health and life satisfaction substantially decreased, and this decrease was larger for the people who reported having higher pre-pandemic social capital. Results also show however that their wellbeing decrease was not high enough to fully overturn the wellbeing gap usually in their favour. This suggests that the positive effects of social capital for wellbeing are via at least two pathways: in-person social interactions and via the value of having social networks. These results contribute to the literature on the relationship between social capital and wellbeing, as well as the one on the effects of the pandemic for mental health and life satisfaction. Policy implications include investing in social capital to increase overall populations’ wellbeing; and, during epidemiological crises, facilitating online psychological support to hamper the negative effects of social isolation, and campaigning to encourage people to keep as much as possible in touch with loved ones and their communities via online social networks and online events to prevent a further degradation of the quantity and quality of social interactions.

Blockchain Enabled Social Network for Detecting Fake Accounts

Front end technology with permanent accounts is employed in online social networks to help people get to know one another. In an effort to maintain connection with everyone, Facebook and Twitter are changing along with the users. OSNs are used by them for planning events, news exchange, interpersonal communication, and even running their own online enterprises. Because of OSNs' quick growth and the abundance of personal data that its users have shared, attackers and imposters have been lured to them with the intention of stealing personal information, spreading disruptive activities, and publishing false information. A further rationale for creating fictitious profiles for hateful accounts. In an attempt to harm the reputation of the person they detest, these users make profiles using their usernames and publish photographs and articles that aren't related to them. to use blockchain technology, Aadhar number verification, and SVM profile classification to create a reliable solution for detecting phoney social network profiles. By analysing several aspects of social media profiles, the SVM classification uses machine learning to discern between real and fraudulent accounts. By verifying Aadhar numbers through legitimate channels, Aadhar number verification is integrated into the registration process, guaranteeing the authenticity of user identities. A decentralised and secure identity management system is made possible by the use of blockchain technology, with smart contracts providing immutable storage for verified user data.