Showing papers on "Social network published in 2021"

A Deep Graph Neural Network-Based Mechanism for Social Recommendations

Abstract: Nowadays, the issue of information overload is gradually gaining exposure in the Internet of Things (IoT), calling for more research on recommender system in advance for industrial IoT scenarios. With the ever-increasing prevalence of various social networks, social recommendations (SoR) will certainly become an integral application that provides more feasibly personalized information service for future IoT users. However, almost all of the existing research managed to explore and quantify correlations between user preferences and social relationships, while neglecting the correlations among item features which could further influence the topologies of some social groups. To tackle with this challenge, in this article, a deep graph neural network-based social recommendation framework (GNN-SoR) is proposed for future IoTs. First, user and item feature spaces are abstracted as two graph networks and respectively encoded via the graph neural network method. Next, two encoded spaces are embedded into two latent factors of matrix factorization to complete missing rating values in a user-item rating matrix. Finally, a large amount of experiments are conducted on three real-world data sets to verify the efficiency and stability of the proposed GNN-SoR.

A survey of Twitter research: Data model, graph structure, sentiment analysis and attacks

Abstract: Twitter is the third most popular worldwide Online Social Network (OSN) after Facebook and Instagram. Compared to other OSNs, it has a simple data model and a straightforward data access API. This makes it ideal for social network studies attempting to analyze the patterns of online behavior, the structure of the social graph, the sentiment towards various entities and the nature of malicious attacks in a vivid network with hundreds of millions of users. Indeed, Twitter has been established as a major research platform, utilized in more than ten thousands research articles over the last ten years. Although there are excellent review and comparison studies for most of the research that utilizes Twitter, there are limited efforts to map this research terrain as a whole. Here we present an effort to map the current research topics in Twitter focusing on three major areas: the structure and properties of the social graph, sentiment analysis and threats such as spam, bots, fake news and hate speech. We also present Twitter’s basic data model and best practices for sampling and data access. This survey also lays the ground of computational techniques used in these areas such as Graph Sampling, Natural Language Processing and Machine Learning. Along with existing reviews and comparison studies, we also discuss the key findings and the state of the art in these methods. Overall, we hope that this survey will help researchers create a clear conceptual model of Twitter and act as a guide to expand further the topics presented.

Collaborative Neural Social Recommendation

Abstract: Collaborative filtering (CF) is one of the most popular techniques for building recommender systems. To overcome the data sparsity in CF, social recommender systems have emerged to boost recommendation performance by utilizing social correlation among users’ interests. Recently, inspired by the immense success of deep learning for embedding learning, neural network-based recommender systems have shown promising recommendation performance. Nevertheless, few researchers have attempted to tackle the social recommendation problem with neural models. To this end, in this paper, we design a neural architecture that organically combines the intrinsic relationship between social network structure and user–item interaction behavior for social recommendation. Two key challenges arise in this process: first, how to incorporate the social correlation of users’ interests in this neural model, and second, how to design a neural architecture to capture the unique characteristics of user–item interaction behavior for recommendation. To tackle these two challenges, we develop a model named collaborative neural social recommendation (CNSR) with two parts: 1) a social embedding part and 2) a collaborative neural recommendation (CNR) part. In CNSR, the user embedding leverages each user’s social embedding learned from an unsupervised deep learning technique with social correlation regularization. The user and item embeddings are then fed into a unique neural network with a newly designed collaboration layer to model both the shallow collaborative and deep complex interaction relationships between users and items. We further propose a joint learning framework to allow the social embedding part and the CNR part to mutually enhance each other. Finally, extensive experimental results on two real-world datasets clearly demonstrate the effectiveness of our proposed model.

The emergence of social media data and sentiment analysis in election prediction

Abstract: This work presents and assesses the power of various volumetric, sentiment, and social network approaches to predict crucial decisions from online social media platforms. The views of individuals play a vital role in the discovery of some critical decisions. Social media has become a well-known platform for voicing the feelings of the general population around the globe for almost decades. Sentiment analysis or opinion mining is a method that is used to mine the general population’s views or feelings. In this respect, the forecasting of election results is an application of sentiment analysis aimed at predicting the outcomes of an ongoing election by gauging the mood of the public through social media. This survey paper outlines the evaluation of sentiment analysis techniques and tries to edify the contribution of the researchers to predict election results through social media content. This paper also gives a review of studies that tried to infer the political stance of online users using social media platforms such as Facebook and Twitter. Besides, this paper highlights the research challenges associated with predicting election results and open issues related to sentiment analysis. Further, this paper also suggests some future directions in respective election prediction using social media content.

Exacerbating Inequalities: Social Networks, Racial/Ethnic Disparities, and the COVID-19 Pandemic in the United States.

Abstract: OBJECTIVES: The disruption and contraction of older adults' social networks are among the less-discussed consequences of the COVID-19 pandemic. Our objective is to provide an evidence-based commentary on racial/ethnic disparities in social network resources and draw attention to the ways in which disasters differentially impact social networks, with meaningful insight for the ongoing pandemic. METHODS: We draw upon prior research on social networks and past natural disasters to identify major areas of network inequality. Attention is given to how pre-pandemic racial/ethnic network disparities are exacerbated during the current crisis, with implications for physical and mental health outcomes. RESULTS: Evidence from the literature shows a robust association between strong social networks and physical and mental health outcomes. During times of crisis, access to social networks for older adults are disrupted, particularly for marginalized groups. We document pre-pandemic disparities in social networks resources and offer insight for examining the impact of COVID-19 on disrupting social networks among older adults. DISCUSSION: Importantly, racial/ethnic disparities in social networks both prior to and as a result of the pandemic intensify existing inequalities and demonstrate the necessity of better understanding social network inequalities for marginalized older adults, particularly in the context of the COVID-19 health crisis.

Social influence and unfollowing accelerate the emergence of echo chambers

Abstract: While social media make it easy to connect with and access information from anyone, they also facilitate basic influence and unfriending mechanisms that may lead to segregated and polarized clusters known as “echo chambers.” Here we study the conditions in which such echo chambers emerge by introducing a simple model of information sharing in online social networks with the two ingredients of influence and unfriending. Users can change both their opinions and social connections based on the information to which they are exposed through sharing. The model dynamics show that even with minimal amounts of influence and unfriending, the social network rapidly devolves into segregated, homogeneous communities. These predictions are consistent with empirical data from Twitter. Although our findings suggest that echo chambers are somewhat inevitable given the mechanisms at play in online social media, they also provide insights into possible mitigation strategies.

DeepFakE: improving fake news detection using tensor decomposition-based deep neural network

Abstract: Social media platforms have simplified the sharing of information, which includes news as well, as compared to traditional ways. The ease of access and sharing the data with the revolution in mobile technology has led to the proliferation of fake news. Fake news has the potential to manipulate public opinions and hence, may harm society. Thus, it is necessary to examine the credibility and authenticity of the news articles being shared on social media. Nowadays, the problem of fake news has gained massive attention from research communities and needed an optimal solution with high efficiency and low efficacy. Existing detection methods are based on either news-content or social-context using user-based features as an individual. In this paper, the content of the news article and the existence of echo chambers (community of social media-based users sharing the same opinions) in the social network are taken into account for fake news detection. A tensor representing social context (correlation between user profiles on social media and news articles) is formed by combining the news, user and community information. The news content is fused with the tensor, and coupled matrix-tensor factorization is employed to get a representation of both news content and social context. The proposed method has been tested on a real-world dataset: BuzzFeed. The factors obtained after decomposition have been used as features for news classification. An ensemble machine learning classifier (XGBoost) and a deep neural network model (DeepFakE) are employed for the task of classification. Our proposed model (DeepFakE) outperforms with the existing fake news detection methods by applying deep learning on combined news content and social context-based features as an echo-chamber.

Dynamics of Public Opinions in an Online and Offline Social Network

Abstract: With the development of the information and Internet technology, the public opinions with big data will rapidly emerge in an online-offline social network, and an inefficient management of public opinions often will lead to the security crisis for either firms or governments. To unveil the interaction mechanism among a large number of agents between the online and offline social networks, in this paper we propose the public opinion dynamics model in an online-offline social network context. Next, in the theory aspect we investigate the analytical conditions to form a consensus in the public opinion dynamics model. Furthermore, we conduct the extensive simulations to investigate how the online agents impact the dynamics of public opinion formation, and unfold that the online agents shorten the steady-state time, decrease the number of opinion clusters, and smoothen the opinion changes in the opinion dynamics. The increase in the size of the online agents often enhances these effects. The results in this paper can provide a basis for the management of the public opinions in the Internet age.

Social-Enhanced Attentive Group Recommendation

Abstract: With the proliferation of social networks, group activities have become an essential ingredient of our daily life. A growing number of users share their group activities online and invite their friends to join in. This imposes the need of an in-depth study on the group recommendation task, i.e., recommending items to a group of users. Despite its value and significance, group recommendation remains an unsolved problem due to 1) the weights of group members are crucial to the recommendation performance but are rarely learnt from data; 2) social followee information is beneficial to understand users’ preferences but is rarely considered; and 3) user-item interactions are helpful to reinforce the performance of group recommendation but are seldom investigated. Toward this end, we devise neural network-based solutions by utilizing the recent developments of attention network and neural collaborative filtering (NCF). First of all, we adopt an attention network to form the representation of a group by aggregating the group members’ embeddings, which allows the attention weights of group members to be dynamically learnt from data. Second, the social followee information is incorporated via another attention network to enhance the representation of individual user, which is helpful to capture users’ personal preferences. Third, considering that many online group systems also have abundant interactions of individual users on items, we further integrate the modeling of user-item interactions into our method. Through this way, the recommendation for groups and users can be mutually reinforced. Extensive experiments on the scope of both macro-level performance comparison and micro-level analyses justify the effectiveness and rationality of our proposed approaches.

Central Server Free Federated Learning over Single-sided Trust Social Networks

Abstract: Federated learning has become increasingly important for modern machine learning, especially for data privacy-sensitive scenarios. Existing federated learning mostly adopts the central server-based architecture or centralized architecture. However, in many social network scenarios, centralized federated learning is not applicable (e.g., a central agent or server connecting all users may not exist, or the communication cost to the central server is not affordable). In this paper, we consider a generic setting: 1) the central server may not exist, and 2) the social network is unidirectional or of single-sided trust (i.e., user A trusts user B but user B may not trust user A). We propose a central server free federated learning algorithm, named Online Push-Sum (OPS) method, to handle this challenging but generic scenario. A rigorous regret analysis is also provided, which shows interesting results on how users can benefit from communication with trusted users in the federated learning scenario. This work builds upon the fundamental algorithm framework and theoretical guarantees for federated learning in the generic social network scenario.

Digital nudging and privacy: improving decisions about self-disclosure in social networks*

Abstract: Self-disclosure on social network sites (SNSs) constitutes a feedback necessity as well as a potential privacy risk. We integrate both perspectives by studying privacy-related factors that influenc...

Changes in Social Network Structure in Response to Exposure to Formal Credit Markets

Abstract: Formal financial institutions can have far-reaching and long-lasting impacts on informal lending and information networks. We first study 75 villages in Karnataka, 43 of which were exposed to microfinance after we first collected detailed network data. Networks shrink more in exposed villages. Links between households that were unlikely to ever borrow from microfinance are at least as likely to disappear as links involving likely borrowers. We replicate these surprising findings in the context of a randomized controlled trial in Hyderabad, where a microfinance institution randomly selected neighborhoods to enter first. Four years after all neighborhoods were treated, households in early-entry neighborhoods had credit access longer and had larger loans. We again find fewer social relationships between households in early-entry neighborhoods, even among those ex-ante unlikely to borrow. Because the results suggest global spillovers, which are inconsistent with standard models of network formation, we develop a new dynamic model of network formation that emphasizes chance meetings, where efforts to socialize generate a global network-level externality. Finally, we analyze informal borrowing and the sensitivity of consumption to income fluctuations. Households unlikely to take up microcredit suffer the greatest loss of informal borrowing and risk sharing, underscoring the global nature of the externality.

Social Networks and Loneliness During the COVID-19 Pandemic

Abstract: We utilize longitudinal social network data collected pre–COVID-19 in June 2019 and compare them with data collected in the midst of COVID in June 2020. We find significant decreases in network den...

Don't put all social network sites in one basket: Facebook, Instagram, Twitter, TikTok, and their relations with well-being during the COVID-19 pandemic.

Abstract: Prior studies indicated that actively using social network sites (SNSs) is positively associated with well-being by enhancing social support and feelings of connectedness. Conversely, passively using SNSs is negatively associated with well-being by fostering upward social comparison and envy. However, the majority of these studies has focused on Facebook. The present research examined the relationships between well-being-satisfaction with life, negative affect, positive affect-and using actively or passively various SNSs-Facebook, Instagram, Twitter, TikTok-during the COVID-19 pandemic. In addition, two mediators were tested: social support and upward social comparison. One thousand four persons completed an online survey during the quarantine measures; the analyses employed structural equation modeling. Results showed that passive usage of Facebook is negatively related to well-being through upward social comparison, whereas active usage of Instagram is positively related to satisfaction with life and negative affect through social support. Furthermore, active usage of Twitter was positively related to satisfaction with life through social support; while passive usage was negatively related to upward social comparison, which, in turn, was associated with more negative affect. Finally, TikTok use was not associated with well-being. Results are discussed in line with SNSs' architectures and users' motivations. Future research is required to go beyond methodological and statistical limitations and allow generalization. This study concludes that SNSs must be differentiated to truly understand how they shape human interactions.

Online social networks security and privacy: comprehensive review and analysis

Abstract: With fast-growing technology, online social networks (OSNs) have exploded in popularity over the past few years. The pivotal reason behind this phenomenon happens to be the ability of OSNs to provide a platform for users to connect with their family, friends, and colleagues. The information shared in social network and media spreads very fast, almost instantaneously which makes it attractive for attackers to gain information. Secrecy and surety of OSNs need to be inquired from various positions. There are numerous security and privacy issues related to the user’s shared information especially when a user uploads personal content such as photos, videos, and audios. The attacker can maliciously use shared information for illegitimate purposes. The risks are even higher if children are targeted. To address these issues, this paper presents a thorough review of different security and privacy threats and existing solutions that can provide security to social network users. We have also discussed OSN attacks on various OSN web applications by citing some statistics reports. In addition to this, we have discussed numerous defensive approaches to OSN security. Finally, this survey discusses open issues, challenges, and relevant security guidelines to achieve trustworthiness in online social networks.

Social networks predict the life and death of honey bees.

Abstract: In complex societies, individuals’ roles are reflected by interactions with other conspecifics. Honey bees (Apis mellifera) generally change tasks as they age, but developmental trajectories of individuals can vary drastically due to physiological and environmental factors. We introduce a succinct descriptor of an individual’s social network that can be obtained without interfering with the colony. This ‘network age’ accurately predicts task allocation, survival, activity patterns, and future behavior. We analyze developmental trajectories of multiple cohorts of individuals in a natural setting and identify distinct developmental pathways and critical life changes. Our findings suggest a high stability in task allocation on an individual level. We show that our method is versatile and can extract different properties from social networks, opening up a broad range of future studies. Our approach highlights the relationship of social interactions and individual traits, and provides a scalable technique for understanding how complex social systems function. Honey bee workers take on different tasks for the colony as they age. Here, the authors develop a method to extract a descriptor of the individuals’ social networks and show that interaction patterns predict task allocation and distinguish different developmental trajectories.

Unifying spatial and social network analysis in disease ecology.

Abstract: Social network analysis has achieved remarkable popularity in disease ecology, and is sometimes carried out without investigating spatial heterogeneity. Many investigations into sociality and disease may nevertheless be subject to cryptic spatial variation, so ignoring spatial processes can limit inference regarding disease dynamics. Disease analyses can gain breadth, power and reliability from incorporating both spatial and social behavioural data. However, the tools for collecting and analysing these data simultaneously can be complex and unintuitive, and it is often unclear when spatial variation must be accounted for. These difficulties contribute to the scarcity of simultaneous spatial-social network analyses in disease ecology thus far. Here, we detail scenarios in disease ecology that benefit from spatial-social analysis. We describe procedures for simultaneous collection of both spatial and social data, and we outline statistical approaches that can control for and estimate spatial-social covariance in disease ecology analyses. We hope disease researchers will expand social network analyses to more often include spatial components and questions. These measures will increase the scope of such analyses, allowing more accurate model estimates, better inference of transmission modes, susceptibility effects and contact scaling patterns, and ultimately more effective disease interventions.

Social Media Communication and Loneliness Among Older Adults: The Mediating Roles of Social Support and Social Contact.

Abstract: Background and objectives Social media communication offers a medium for helping older people stay socially and emotionally connected with others. This study investigated the association between social media communication with close social ties and loneliness among community-dwelling older adults. The study also examined the mediating roles of social support and social contact. Research design and methods Four waves of data from the Health and Retirement Study (2010/2012 and 2014/2016) were used to address the research questions (N = 7,524). A path model was estimated to examine the association between social media communication and older adults' loneliness. We also examined whether the association between social media communication and loneliness was mediated by perceived social support from close social ties (children, other family members, and friends) and frequency of contact with social network members (phone, in-person contact, and writing letters/email). Results The results showed that frequent social media communication was associated with lower levels of loneliness, adjusting for previous levels of loneliness. The relationship between social media communication and loneliness was mediated by perceived social support and social contact. Thus, social media communication was associated with higher levels of perceived social support and social contact, which were related to lower levels of loneliness among older adults. Discussion and implications These findings suggested that social media communication may be considered an intervention to reduce loneliness among older people by increasing levels of social support and social contact.

Periodic-Aware Intelligent Prediction Model for Information Diffusion in Social Networks

Abstract: Due to the rapid development of information and communication technologies with several emerging computing paradigms, such as ubiquitous computing, social computing, and mobile computing, modeling of information diffusion becomes an increasingly significant issue in the big data era. In this study, we focus on a periodic-aware intelligent prediction method based on a comprehensive modeling of user and contagion features, which can be applied to support information diffusion across social networks in accordance with users’ adoption behaviors. In particular, the Dynamically Socialized User Networking (DSUN) model and sentiment-Latent Dirichlet Allocation (LDA) topic model, which consider a series of social factors, including user interests and social roles, semantic topics and sentiment polarities, are constructed and integrated together to facilitate the information diffusion process. A periodic-aware preception mechanism usingreinforcement learning with a newly designed reward rule based on topic distribution is then designed to detect and classify different periods into the so-called routine period and emergency period. Finally, a deep learning scheme based on multi-factor analysis is developed for adoption behavior prediction within the identified different periods. Experiments using the real-world data demonstrate the effectiveness and usefulness of our proposed model and method in heterogenous social network environments.

Social Capital and Health

Abstract: “The real nature of man is the totality of social relations” (Marx 1963: 83). All individuals dwell in a network of social relationships. Their health conditions can be contingent on structural attributes of their network contexts. Since Durkheim’s classic study on suicide ([1897] 1951), there has been a long research tradition on diverse aspects of social relationships and health in sociology and other social sciences (for reviews see Berkman et al. 2000; House et al. 1988; Pescosolido and Levy 2002; Smith and Christakis 2008; Song et al. 2011; Umberson and Montez 2010). In the last two decades social capital has grown into one of the most popular but controversial relationship-based theoretical tools in the multidisciplinary health literature.

Secure Social Internet of Things Based on Post-Quantum Blockchain

Abstract: With the advancement of the application of Internet of Things (IoTs), the IoT technology is combining with the social network, forming a new network with private object information as the media and social entertainment as the purpose. Social Internet of things (SIoTs) is a new application of IoT technology in social network. The current SIoT systems are centralized and the user's security and privacy is not properly protected. In order to address the challenges in SIoTs, we propose a privacy protection system for the users. First, we propose a post-quantum ring signature. Second, we propose a blockchain system based on the ring signature. Compared with the traditional SIoTs, our system is based on post-quantum techniques, which is secure against both traditional computers and quantum computers. The results of the blockchain system show that it is very suitable for SIoTs.

A novel framework for detecting social bots with deep neural networks and active learning

Abstract: Microblogging is a popular online social network (OSN), which facilitates users to obtain and share news and information. Nevertheless, it is filled with a huge number of social bots that significantly disrupt the normal order of OSNs. Sina Weibo, one of the most popular Chinese OSNs in the world, is also seriously affected by social bots. With the growing development of social bots in Sina Weibo, they are increasingly indistinguishable from normal users, which presents more huge challenges in detecting social bots. Firstly, it is difficult to extract the features of social bots completely. Secondly, large-scale data collection and labeling of user data are extremely hard. Thirdly, the performance of classical classification approaches applied to social bot detection is not good enough. Therefore, this paper proposes a novel framework for detecting social bots in Sina Weibo based on deep neural networks and active learning (DABot). Specifically, 30 features from four categories, namely metadata-based, interaction-based, content-based, and timing-based are extracted to distinguish between social bots and normal users. Nine of these features are completely new features proposed in this paper. Moreover, active learning is employed to efficiently expand the labeled data. Then, a new deep neural network model called RGA is built to implement the detection of social bots, which makes use of a residual network (ResNet), a bidirectional gated recurrent unit (BiGRU), and an attention mechanism. After performance evaluation, the results show that DABot is more effective than the state-of-the-art baselines with the accuracy of 0.9887.

Managing Consensus With Minimum Adjustments in Group Decision Making With Opinions Evolution

Abstract: Nowadays, online social networks, such as “Facebook” and “WeChat,” facilitate the expression, diffusion, and interactions of individuals’ opinions regarding various issues. In this environment, individuals’ opinions are liable to be influenced by others and then evolve over the time. In this paper, we propose an approach based on minimum adjustments to manage the consensus in the group decision making (GDM) with opinions evolution. First, inspired by the idea proposed in DeGroot model, we establish a novel GDM model with opinions evolution, and then discuss its consensus conditions. Based on this, we propose an algorithm to achieve the network partition, and then provide a consensus model with minimum adjustments to obtain the optimal adjusted initial opinions and collective consensus opinion. Finally, we provide a numerical example to demonstrate the feasibility and effectiveness of the proposed theoretical results, and design comparative simulations to explore the effects of the opinions evolution on the final consensus solution.