Online Social Networks Research Articles

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.

Exploring equity perception of electric vehicles from a social media perspective

Electric vehicles (EVs) are considered a promising solution for climate change. However, their introduction has raised uncertainties in transportation equity, reflecting the diverse public perceptions and discussions on this issue. Understanding these perceptions is crucial for integrating equity into EV adoption effectively. To bridge this gap, our study used a social media approach to conduct a cross-platform equity analysis on two popular online social networks (OSNs): Twitter and Reddit. We identified 22,533 tweets and 12,318 Reddit comments relevant to equity from all EV-related discussions from 2011 to 2022. Our analysis utilized different features of the two OSNs, including verified account identity and Reddit communities, to present a comprehensive picture of equity perception in social media. Additionally, we used machine learning methods to infer Twitter users’ demographic attributes and integrate user demographics into equity analysis. Our findings show that political accounts are the center of attention on Twitter, but they are accompanied by negative attitudes. Many fringe communities are highly active in equity discussions on Reddit. Our study also found that younger people pay less attention and hold more negative sentiments towards equity discussions, while male and female users have similar interests in equity and similar sentiment. Notably, the proportion of equity discussions remains low in the overall EV corpus, despite a significant increase in recent years.

Bifurcation Analysis for an OSN Model with Two Delays

In this research, we introduce and analyze a mathematical model for online social networks, incorporating two distinct delays. These delays represent the time it takes for active users within the network to begin disengaging, either with or without contacting non-users of online social platforms. We focus particularly on the user prevailing equilibrium (UPE), denoted as P*, and explore the role of delays as parameters in triggering Hopf bifurcations. In doing so, we find the conditions under which Hopf bifurcations occur, then establish stable regions based on the two delays. Furthermore, we delineate the boundaries of stability regions wherein bifurcations transpire as the delays cross these thresholds. We present numerical simulations to illustrate and validate our theoretical findings. Through this interdisciplinary approach, we aim to deepen our understanding of the dynamics inherent in online social networks.

Diffusion prediction of competitive information with time-varying attractiveness in social networks

The ubiquity of social media has facilitated the simultaneous dissemination of large-scale information within online social networks. By assuming that information attractiveness is static, numerous studies have been devoted to the analysis of multiple information dissemination. However, real-world information attractiveness often exhibits variations throughout the dissemination process. This paper delves into the study of how time-varying information attractiveness influences the diffusion process, particularly in the context of competitive information dissemination regarding multiple products. First, we propose a Markov multi-information diffusion model, incorporating three critical parameters: the attractiveness degree, the information-boom time, and the information prosperity index, to address the dynamic nature of attractiveness. The basic reproduction number is derived, and the accuracy of the proposed model is verified. Furthermore, our numerical simulation results illustrate that both the maximal attractiveness value and the information prosperity duration significantly enhance information competitiveness, while delayed information boom time may undermine this competitiveness. In addition, it is indicated that improving the peak attractiveness is the key for time-varying information to achieve a better spreading effect. Moreover, we find that the growth of information attractiveness can even mitigate the impact of blocking the spread caused by information discarding behavior, highlighting intrinsic quality as the paramount determinant of information promotion outcomes.

Self-presentation and social networking online: The professional identity of PhD students in HCI

The number of research doctorate degrees awarded by US institutions per year has increased steadily over the decades. However, the academic job market is also becoming more competitive, and doctoral candidates often face difficulties in developing a professional identity and making career-related decisions. In this study, we investigated PhD students' professional identity formation with regard to their usage of social networking sites (SNSs). Through semi-structured interviews with 16 students in Human-Computer Interaction (HCI), we found that self-presentation and online presence in the online community were considered necessary. Students' perception of using SNSs for professional activities was impacted by their peers and faculty. SNSs helped students gain information and support from online communities and also reflected their professional identities. The results present insights for transforming doctoral education and preparing students for diverse career options in today's economy.

Pacemaker effects on online social rhythms on a social network

The dynamics of coupled oscillators in a network are a significant topic in complex systems science. People with daily social rhythms interact through social networks in everyday life. This can be considered as a coupled oscillator in social networks, which is also true in online society (online social rhythms). Controlling online social rhythms can contribute to healthy daily rhythms and mental health. We consider controlling online social rhythms by introducing periodic forcing (pacemakers). However, theoretical studies predict that pacemaker effects do not spread widely across mutually connected networks such as social networks. We aimed to investigate the characteristics of the online social rhythms with pacemakers on an empirical online social network. Therefore, we conducted an intervention experiment on the online social rhythms of hundreds of players (participants who were pacemakers) using an avatar communication application (N = 416). We found that the intervention had little effect on neighbors’ online social rhythms. This may be because mutual entrainment stabilizes the neighbors’ and their friends’ rhythms. That is, their online social rhythms were stable despite the disturbances. However, the intervention affected on neighbors’ rhythms when a participant and their neighbor shared many friends. This suggests that interventions to densely connected player groups may make their and their friends’ rhythms better. We discuss the utilization of these properties to improve healthy online social rhythms.

Anti-Semitic Speech Detection and Classification in Online Social Network using Deep Learning

Every individual possesses the entitlement to freedom of speech. However, in the guise of free expression, this privilege is being abused to discriminate against and harm other people. This prejudice is referred to as hate speech. A clear definition of hate speech is language that expresses hatred for an individual or a group of individuals based on traits including race, religion, ethnicity, gender, nationality, handicap, and sexual orientation. Hate speech has become increasingly widespread, both in physical spaces and on the internet, in recent years. Thus, recent studies used a range of machine learning and deep learning techniques with text mining method to automatically recognise the hate speech messages on real-time datasets in order to address this developing issue in social media sites. This project's goal is to examine comments on social networks using Natural Language Processing (NLP) and a Deep Learning method called VADER method. In order to identify the text as positive or negative, VADER neural networks are used to extract the keywords from user generated content. If it's negative, immediately block the comments in accordance with the user's preferences and block the friends in accordance with pre-established threshold values. The proposed framework was deployed in a real-time social networking site with an improved notification system, according to experimental findings