Online Social Networks Research Articles

A Hybrid Probabilistic Graph Based Community Clustering Model for Large Social Networking Link Prediction Data

Dynamic community clustering is essential for online social networking sites due to the high dimensionality and large data size. It aims to uncover social relationships among nodes and links within the network. However, traditional models often struggle with community structure detection because of the extensive computational time and memory required. Additionally, these models need contextual weighted node information to establish social networking feature relationships. To address these challenges, an advanced probabilistic weighted community detection framework has been developed for large-scale social network data. This framework uses a filter-based probabilistic model to eliminate sparse values and identify weighted community detection nodes for dynamic clustering analysis. Experimental results demonstrate that this filter-based probabilistic community detection framework outperforms others in terms of normalized mutual information, entropy, density, and runtime efficiency (measured in milliseconds).

What drives Chinese Generation Z’s purchase intentions for new Chinese style apparel? A study from a stimulus-organism-response perspective

PurposeThis study aims to understand how product attributes (object stimuli), social influences (social-psychological stimuli) and internal factors (internal stimuli) contribute to Chinese Generation Z’s purchase intentions (responses) for new Chinese style apparel (NCSA) through NCSA attitudes (cognitive state) and cultural pride (affective state) based on the stimulus-organism-response (S-O-R) model.Design/methodology/approachData were collected from 989 respondents aged between 18 and 29 years through self-administrated questionnaires via a professional survey panel, Credamo, in China. Data were analyzed using structural equation modeling.FindingsResults showed that among the seven stimuli, NCSA’s design, online social networking communities, cultural identity and personal norms significantly influenced Generation Z’s purchase intentions through both NCSA attitudes and cultural pride. Further, NCSA’s cultural connotations and celebrity influences elicited purchase intentions merely through the affective state – cultural pride. In contrast, NCSA’s functionality did not have a significant influence on NCSA attitudes and adversely affected cultural pride.Originality/valueThis study fills the research gap and extends the application of the S-O-R model within the NCSA context. The findings of this study shed light on the practical implications for marketers, brands and policymakers with regard to a better understanding of Chinese Generation Z’s NCSA consumption.

Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas

The emergence and acceptance of digital technology have caused information pollution and an infodemic on Online Social Networks (OSNs), blogs, and online websites. The malicious broadcast of illegal, objectionable and misleading content causes behavioural changes and social unrest, impacts economic growth and national security, and threatens users’ safety. The proliferation of AI-generated misleading content has further intensified the current situation. In the previous literature, state-of-the-art (SOTA) methods have been implemented for Fake News Detection (FND). However, the existing research lacks multidisciplinary considerations for FND based on theories on FN and OSN users. Theories’ analysis provides insights into effective and automated detection mechanisms for FN, and the intentions and causes behind wide-scale FN propagation. This review evaluates the available datasets, FND techniques, and approaches and their limitations. The novel contribution of this review is the analysis of the FND in linguistics, healthcare, communication, and other related fields. It also summarises the explicable methods for FN dissemination, identification and mitigation. The research identifies that the prediction performance of pre-trained transformer models provides fresh impetus for multilingual (even for resource-constrained languages), multidomain, and multimodal FND. Their limits and prediction capabilities must be harnessed further to combat FN. It is possible by large-sized, multidomain, multimodal, cross-lingual, multilingual, labelled and unlabelled dataset curation and implementation. SOTA Large Language Models (LLMs) are the innovation, and their strengths should be focused on and researched to combat FN, deepfakes, and AI-generated content on OSNs and online sources. The study highlights the significance of human cognitive abilities and the potential of AI in the domain of FND. Finally, we suggest promising future research directions for FND and mitigation.

Prenatal Care in Urban China: Qualitative Study on Challenges and Coping Mechanisms

This study aims to identify challenges that women face in accessing prenatal care services in urban China and their coping mechanisms to deal with the challenges. We conducted semi-structured interviews in June and July in 2019 with 38 women who had experience of childbirth within the last five years. Through interviews, this study pays particular attention to a quality of prenatal care services by focusing on women’s experiences in seeking for such services. The findings suggest that most participants had access to standardized prenatal care services but faced two challenges: long wait time and short doctor-patient interaction time. These challenges stem from overcrowded hospitals. The findings also illuminate power and information asymmetry between doctors and patients. Women leverage social networks with friends, colleagues, and former classmates to fill in the gap of short doctor-patient interaction by obtaining relevant information about pregnancy and prenatal care services. The analyses of interviews and a social networking site also suggest that online social networks play a similar role to fill in the informational gap. Still, social networks remain a coping mechanism rather than a fundamental solution to the systemic issues within the public health system.

Real-time fake news detection in online social networks: FANDC Cloud-based system

Social networks have become a common way for people to communicate with each other and share ideas, thanks to their fast information-sharing features. But fake news spread on social networks can cause many negative consequences by affecting people’s daily lives. However, the literature lacks online and real-time fake news detection systems. This study aims to fill this gap in the literature and to handle the fake news detection problem with a system called FANDC, based on cloud computing, to cope with fake news in seven different categories, and to solve the real-time fake news detection problems. The system was developed using the CRISP-DM methodology with a hybrid approach. BERT algorithm was used in the system running on the cloud to avoid possible cyber threats with the dataset created with approximately 99 million big data from COVID-19-TweetIDs GitHub repository. It was trained in two periods with 100% accuracy during the modeling phase in terms of training accuracy. Experimental results of the FANDC system performed the real-time detection of fake news at 99% accuracy. However, previous studies experimental level success rate in the literature, were around 90%. We hope that the developed system will greatly assist social network users in detecting fake news in real-time.

Neural Synchrony of Minds and Machines: Hippocampal Mechanisms to Advance AI and Virtual Networks

Abstract Artificial intelligence (AI) predicts behavior by learning from user data collected through digitally mediated interfaces. Virtual spaces---such as online social networks---extend the physical world into virtual worlds, leveraging AI technologies to transform user behavior into digital assets by mimicking the brain’s reinforcement networks. Social media platforms utilize AI to manipulate user behavior through rewards that can diverge from the user’s intended goals. This misalignment fragments user behavior and cognition, resulting in virtual disembodiment. Presented is a conceptual mechanism based on the hippocampus to synchronize AI and the brain, synthesizing the user’s physical and mental presence in virtual spaces.

Ingenuidade e Inércia Cognitiva como Atributos para a Viralização de Fake News nas Redes Sociais Online

The dissemination of fake news on online social networks has created a scenario of disinformation in which manipulation and control are used to influence behavior and gain advantages. This phenomenon has motivated joint efforts to inhibit its spread. However, despite these initiatives, the problem persists. The assumption is that users of these channels, who are key agents in confronting this phenomenon, become contributors to disinformation when affected by naivety or cognitive inertia. The aim is to analyze the role of these users in the spread of fake news and identify factors that influence their uncritical stance. Conducted through a narrative review of the literature for theoretical purposes, this analysis adopts a reflective, exploratory and essayistic approach to the condition of co-participants of online social network users in the face of the problem of fake news, based on key themes such as post-truth and critical information competence, and illustrated by an empirical analysis of three specific cases of viral fake news. It is concluded that users of online social networks, when naive and inert, are part of the problem and, therefore, need to be more reactive, instead of, consciously or unconsciously, becoming agents of misinformation.

An Efficient Competitive Control Mechanism for Negative Information Spread in Online Social Networks

An Efficient Competitive Control Mechanism for Negative Information Spread in Online Social Networks

Research on Online Games and Social Interaction

This paper delves into the central position of online games in contemporary society and their close relationship with social interaction. With the popularity of the Internet, online games have become one of the most important ways of leisure and social communication. The study outlines the development of MMOs, from their origins to their diverse classifications, and in particular how their unique social characteristics facilitate interactions between players. Through theoretical analysis combined with social exchange theory and symbolic interaction theory, this paper reveals the mechanisms of social interactions in MMOs, including inter-player exchanges, cooperation and competition, and explores how these interactions profoundly affect players' gaming experience and behavioural decisions. Further, this paper analyses the trend of convergence between online games and social networks, and how this convergence enhances the social attributes and player engagement of games. At the same time, the paper also focuses on the social problems that online games may bring, such as Internet addiction and social disorders, cyberbullying, etc., and looks forward to the impact of technological advances on future social interaction patterns. Finally, the paper summarises the potential and challenges faced by online games in promoting healthy social interactions, and puts forward suggestions for future research with a view to promoting the healthy development of the field of online games and social interactions.

Examining the Influence of Online Social Networks and MOOCs on Collaborative Practices among TVET Teachers: The Mediating Role of Interpersonal Trust

This study examines the impact of online social networks (OSN) and Massive Open Online Courses (MOOCs) on collaborative practices among vocational training educators, focusing on the mediating role of interpersonal trust. Using a quantitative research design, Structural Equation Modeling (SEM) analyzed data from 343 Technical and Vocational Education and Training (TVET) teachers via a survey assessing MOOC usage, OSN, teacher collaboration, and interpersonal trust. Findings indicate a significant positive relationship between OSN and MOOCs, suggesting their potential to enhance collaborative learning environments. However, a negative direct effect of OSN on teacher collaboration reveals challenges that hinder effective engagement. Importantly, interpersonal trust is identified as a crucial mediator, highlighting the necessity of fostering trust among educators to improve collaboration and knowledge sharing. The research addresses a gap in understanding how MOOCs can enhance collaboration among TVET teachers and improve student outcomes, emphasizing the importance of integrating OSNs and MOOCs into vocational training frameworks while cultivating a culture of trust. Ultimately, the study suggests that leveraging OSNs and MOOCs, alongside interpersonal trust, can transform collaborative practices, benefiting both educators and students. Future research should explore the complexities of these relationships, especially barriers to collaboration and effective strategies for utilizing online networks in vocational training.

Multi-agent Attacks for Black-box Social Recommendations

The rise of online social networks has facilitated the evolution of social recommender systems, which incorporate social relations to enhance users’ decision-making process. With the great success of Graph Neural Networks (GNNs) in learning node representations, GNN-based social recommendations have been widely studied to model user-item interactions and user-user social relations simultaneously. Despite their great successes, recent studies have shown that these advanced recommender systems are highly vulnerable to adversarial attacks, in which attackers can inject well-designed fake user profiles to disrupt recommendation performances. While most existing studies mainly focus on targeted attacks to promote target items on vanilla recommender systems, untargeted attacks to degrade the overall prediction performance are less explored on social recommendations under a black-box scenario. To perform untargeted attacks on social recommender systems, attackers can construct malicious social relationships for fake users to enhance the attack performance. However, the coordination of social relations and item profiles is challenging for attacking black-box social recommendations. To address this limitation, we first conduct several preliminary studies to demonstrate the effectiveness of cross-community connections and cold-start items in degrading recommendations performance. Specifically, we propose a novel framework MultiAttack based on multi-agent reinforcement learning to coordinate the generation of cold-start item profiles and cross-community social relations for conducting untargeted attacks on black-box social recommendations. Comprehensive experiments on various real-world datasets demonstrate the effectiveness of our proposed attacking framework under the black-box setting.

Stochastic bilevel interdiction for fake news control in online social networks

Social media platforms attempt to mitigate and control fake news, using interventions such as flagging posts or adjusting newsfeed algorithms, to protect vulnerable individuals. In this paper, we consider performing intervention actions on specific source nodes or user–user edges in social networks, under uncertain effectiveness of different intervention strategies. We model misinformation from malicious users to vulnerable communities using stochastic network interdiction formulations. Specifically, we minimize the expected number of reachable vulnerable users via stochastic maximum flow, and develop an alternative formulation for handling large-scale social networks based on their topological structures. We derive theoretical results for path-based networks and develop an approximate algorithm for single-edge removal on paths. We test instances of a social network with 23,505 nodes, based on the IMDb actors dataset, to demonstrate the scalability of the approach and its effectiveness. Via numerical studies, we find that characteristics of removed edges change when intervention effectiveness is stochastic. Our results suggest that intervention should target on (i) a smaller set of centrally located edges with nodes that represent communities where regulatory actions are more effective, and (ii) dispersed edges with nodes where intervention has a high chance of failure.

How much is it about the money? Using facebook to promote a web survey during the COVID-19 pandemic and estimating average response-returns on ad-investments

The use of ads posted on online social networks has become a popular strategy to recruit survey participants. This paper quantifies the returns from ad-investments in terms of the number of responses. We use as a case study a survey about living and working conditions that was promoted in Spain via Facebook from March 2020 to October 2021. Results show that the effect of each additional euro invested in Facebooks ads daily generated between 2 and 2.8 additional completed surveys per day. These results are stable and consistent throughout the period we study. Nonetheless, we find that the number of survey responses tended to be significantly higher when the COVID-19 restrictions imposed were tighter. Our findings are relevant and have practical implications for future survey research.

Cuckoo Search Optimization-Based Influence Maximization in Dynamic Social Networks

Online social networks are crucial in propagating information and exerting influence through word-of-mouth transmission. Influence maximization (IM) is the fundamental task in social network analysis to find the group of nodes that maximizes the influence in the social network. IM has different applications like viral marketing, campaigning, advertising, and so on. Literature has presented various algorithms based on different approaches to address the IM problem, including nature-inspired algorithms. Most of the work focuses on the static social network. The proposed work first employs nature-inspired Cuckoo Search Optimization to solve the IM problem in dynamic networks. The proposed algorithm applies the fuzzy-logic-based technique to optimize the nests. We also perform statistical tests to show the effectiveness of the proposed algorithm with the benchmark algorithms. The experimental results are performed on five datasets and compare the results with the state-of-the-art algorithms. The results show that the proposed algorithm gives better results than the nature-inspired state-of-the-art algorithms.

Stance Detection in the Context of Fake News—A New Approach

Online social networks (OSNs) are inundated with an enormous daily influx of news shared by users worldwide. Information can originate from any OSN user and quickly spread, making the task of fact-checking news both time-consuming and resource-intensive. To address this challenge, researchers are exploring machine learning techniques to automate fake news detection. This paper specifically focuses on detecting the stance of content producers—whether they support or oppose the subject of the content. Our study aims to develop and evaluate advanced text-mining models that leverage pre-trained language models enhanced with meta features derived from headlines and article bodies. We sought to determine whether incorporating the cosine distance feature could improve model prediction accuracy. After analyzing and assessing several previous competition entries, we identified three key tasks for achieving high accuracy: (1) a multi-stage approach that integrates classical and neural network classifiers, (2) the extraction of additional text-based meta features from headline and article body columns, and (3) the utilization of recent pre-trained embeddings and transformer models.

Higher-order network information propagation model based on social impact theory

User relationships are becoming increasingly diverse in current online social networks. This paper utilizes simplicial complexes in higher-order networks to depict multiple user relationships and characterize the topology of social networks. By employing social impact theory and factors such as user interest and information aging to define state transition rules among users, we establish a UAPR information propagation model based on simplicial complexes to simulate the information propagation process in higher-order social networks. The proposed model is simulated on three types of synthetic networks and four real-world simplicial complexes, demonstrating its ability to accurately describe the dynamics and influences of information propagation. Furthermore, our results indicate that different network structures, user interest values, information aging speeds, and intimacy levels significantly influence the information transmission process.

Enhancing Security in Social Networks through Machine Learning: Detecting and Mitigating Sybil Attacks with SybilSocNet

This study contributes to the Sybil node-detecting algorithm in online social networks (OSNs). As major communication platforms, online social networks are significantly guarded from malicious activity. A thorough literature review identified various detection and prevention Sybil attack algorithms. An additional exploration of distinct reputation systems and their practical applications led to this study’s discovery of machine learning algorithms, i.e., the KNN, support vector machine, and random forest algorithms, as part of our SybilSocNet. This study details the data-cleansing process for the employed dataset for optimizing the computational demands required to train machine learning algorithms, achieved through dataset partitioning. Such a process led to an explanation and analysis of our conducted experiments and comparing their results. The experiments demonstrated the algorithm’s ability to detect Sybil nodes in OSNs (99.9% accuracy in SVM, 99.6% in random forest, and 97% in KNN algorithms), and we propose future research opportunities.

Robin Dunbar: The neurobiology of human sociality

Robin Dunbar is an eminent evolutionary psychologist and anthropologist whose pioneering work has permanently redefined how we think about human social relationships. Dunbar, who is an emeritus Professor of Evolutionary Psychology at the University of Oxford, became world-renowned for, among other contributions, his formulation of “Dunbar's number,” a theoretical limit to the number of stable social relationships an individual can maintain, typically cited as about 150. That idea, which he developed as he studied the relationship between brain size and social group size in various primate species, has since had broad influences across areas from social media design to organizational management. Over time, Dunbar's number has become more widely known and discussed, particularly with the advent of social media and online social networks. This has led to renewed interest in its implications for digital social interactions. Dunbar's illustrious career spans multiple prestigious institutions, including, prior to joining Oxford in 2007, the University of Cambridge, the University of Stockholm (Sweden), University College London and the University of Liverpool. He is a fellow of the British Academy and the Royal Anthropological Institute, a Foreign Member of the Finnish Academy of Science and Letters, and an elected Honorary Member of the Hungarian Academy of Sciences. His interdisciplinary expertise is reflected in his professorships in psychology, evolutionary biology, and anthropology at institutions such as Liverpool and Oxford Universities. He has held a visiting chair in statistical physics and computer science at Aalto University, Finland, which has also awarded him an honorary doctorate. Dunbar studies the evolution of social processes in primates and humans, with work that blends neurobiology, cognitive science, and the social dynamics of how we communicate via friendship networks. His insights into friendships and community cohesion continue to shape our understanding of human social behaviour in the digital age. It is an absolute pleasure that Professor Dunbar answered the Genomic Press Interview as we celebrate the extraordinary story of his scientific odyssey with our readers.

A deep decentralized privacy-preservation framework for online social networks

This paper addresses the critical challenge of privacy in Online Social Networks (OSNs), where centralized designs compromise user privacy. We propose a novel privacy -preservation framework that integrates blockchain technology with deep learning to overcome these vulnerabilities. Our methodology employs a two-tier architecture: the first tier uses an elitism-enhanced Particle Swarm Optimization and Gravitational Search Algorithm (ePSOGSA) for optimizing feature selection, while the second tier employs an enhanced Non-symmetric Deep Autoencoder (e-NDAE) for anomaly detection. Additionally, a blockchain network secures users’ data via smart contracts, ensuring robust data protection. When tested on the NSL-KDD dataset, our framework achieves 98.79% accuracy, a 10% false alarm rate, and a 98.99% detection rate, surpassing existing methods. The integration of blockchain and deep learning not only enhances privacy protection in OSNs but also offers a scalable model for other applications requiring robust security measures.

Dynamic Quarantine Model for Rumor Propagation Control in Online Social Networks

Dynamic Quarantine Model for Rumor Propagation Control in Online Social Networks