Dynamics Of Online Social Networks Research Articles

A Multi-Agent System for Fine-Grained Opinion Dynamics Analysis in Online Social Networks

The influence and dissemination of users’ opinions are the essence of the opinion dynamics in online social networks (OSNs). Understanding the process of users’ opinion formation and propagation can provide better service for public opinion monitoring and product advertising. However, most opinion dynamics research typically do not distinguish between user opinion formation and propagation, instead focusing on the process of mass opinion polarization. This article presents a multiagent system (MAS) to analyze fine-grained opinion dynamics (FOD) by agent-based modeling (ABM). At the macro level, the MAS-FOD system we designed can produce a statistical analysis of public opinion evolution based on different parameter constellations and analyze the changes in personalized agent opinions. At the micro level, the agent-based model is presented to describe the single user in OSNs as an individualized agent in MAS-FOD. Specifically, we propose two mechanisms, agent-based opinion formation (AOF) mechanism and agent-based opinion propagation (AOP) mechanism, for agents to form and disseminate opinions, respectively. Meanwhile, multidimensional social influence features mining from self, local neighbors, and global topic community are defined and used in two mechanisms to train personalized agents to self-adapt to the MAS-FOD system. We demonstrate the rationality and effectiveness of the MAS-FOD system from two types of experiments: empirical analysis and simulation analysis. The former is driven by empirical social network data to analyze the predictive performance of AOF; the latter simulates the public opinion propagation through the AOP mechanism in the MAS-FOD system. The results demonstrate that: 1) the AOF outperforms SOTA methods in the accuracy of agent opinion prediction; 2) the dissemination effect based on the AOP is more closer to the actual evolution trend of public opinion than the baseline; and 3) the MAS-FOD system can perform fine-grained analysis of opinion dynamics by adjusting different parameters.

Covid-19, identity, and piety online: ultra-Orthodox discussions in WhatsApp and Telegram groups under social distancing regulations

ABSTRACT The Covid-19 pandemic has disrupted relationships between religious communities and the state. Churches, religious authorities, and believers have been obligated to modify key activities (prayer, ceremonies) and weigh their religious identity against state prohibitions. Accordingly, we ask how members of a reclusive religious community negotiated and performed their identity under a prolonged emergency. We analyse intra-communal discourse concerning the state’s social distancing regulations during Covid-19 outbreaks and lockdowns that occurred in closed groups in messaging apps. Specifically, this research case-studies Jewish ultra-Orthodox communities in Israel (Haredim). Extensive analysis of over 2,000 WhatsApp and Telegram posts in 35 ultra-Orthodox groups over a two-year period indicates that participants constantly sought to affirm their primary, dominant, communal identity. While efforts to integrate adherence to state prohibitions into this primary identity were evident, these efforts were well accepted only when justifying them via affirmation of the communal identity – superseding medical, political, and practical concerns. The findings advance understanding of minority groups’ attitudes towards state directives and illuminate contemporary pathways of identity dynamics in online social networks. The findings may also be applicable in a broader sense to the study of discourse dynamics in echo chambers and filter bubbles.

Modularity-based approach for tracking communities in dynamic social networks

Community detection is a crucial task to unravel the intricate dynamics of online social networks. The emergence of these networks has dramatically increased the volume and speed of interactions among users, presenting researchers with unprecedented opportunities to explore and analyze the underlying structure of social communities. Despite a growing interest in tracking the evolution of groups of users in real-world social networks, the predominant focus of community detection efforts has been on communities within static networks. In this paper, we introduce a novel framework for tracking communities over time in a dynamic network, where a series of significant events is identified for each community. Our framework adopts a modularity-based strategy and does not require a predefined threshold, leading to a more accurate and robust tracking of dynamic communities. We validated the efficacy of our framework through extensive experiments on synthetic networks featuring embedded events. The results indicate that our framework can outperform the state-of-the-art methods. Furthermore, we utilized the proposed approach on a Twitter network comprising over 60,000 users and 5 million tweets throughout 2020, showcasing its potential in identifying dynamic communities in real-world scenarios. The proposed framework can be applied to different social networks and provides a valuable tool to gain deeper insights into the evolution of communities in dynamic social networks.

Modeling and controlling delayed rumor propagation with general incidence in heterogeneous networks

To explore the rumor propagation dynamics in online social networks (OSNs) and propose the corresponding control strategies, a novel Ignorants-Spreaders-1-Spreaders-2-Removers (I2SR) rumor-spreading model with general incidence is first formulated in heterogeneous networks. This model covers the bilinear incidence and nonlinear incidence, and considers the time delay in the rumor-spreading process, which is universal and practical. We analytically derive the basic reproduction numbers, which determine the existence of rumor-spreading equilibria but also the global dynamics of the model. Moreover, the Lyapunov–LaSalle principle and the graph-theoretic approach prove the global asymptotic stability of the rumor-free/rumor-spreading equilibria in detail. Significantly, the effects of various strategies, including uniform control, targeted control, and acquaintance control, are implemented and compared. Finally, the developed theoretical results are tested via numerical simulations and a real case data of rumors, showing that the model can more accurately simulate rumor-spreading in OSNs.

SENPR disinformation spreading model to discuss the control of the spread of disinformation in emergencies

The spread of disinformation in online social networks (OSNs) poses a significant threat to maintaining normal social order. It is important to understand when and how intervention measures can control the spread of disinformation. We propose a novel information spreading model called susceptible, exposed, negative-infected, positive-infected, recovered population (SENPR) to describe the disinformation dynamics in OSNs under the intervention strategy. The model in this study considers that the susceptible population in the network is transformed into an exposed population through the influence of two information-dissemination methods that affect other susceptible people in the network. Subsequently, the stability of the model was analyzed to verify the validity of the obtained theoretical results. Finally, the influence of different parameters on the spread of disinformation is described, and corresponding management strategies to suppress the spread of false information in emergency situations are discussed. The results show that word-of-mouth is the main mode of communication in the “development” and “burst” stages of information spreading. The earlier the intervention is implemented, the sooner the number of people affected by word-of-mouth begins to decline and the sooner the spreading of disinformation reaches the saturation and decay stages.

A Dual Rumor Spreading Model with Consideration of Fans versus Ordinary People

The spread of rumors in online social networks (OSNs) has caused a serious threat to the normal social order. In order to describe the rumor-spreading dynamics in OSNs during emergencies, a novel model with consideration of fans versus ordinary people is proposed in this paper. In contrast to previous studies, we consider the case that two rumors exist simultaneously. It is assumed that one is an entertainment rumor that fans care about, and the other is a common rumor. First, we derive the mean-field equations that describe the dynamics of this dual rumor propagation model and obtain the threshold parameter. Secondly, after finding the necessary and sufficient conditions for the existence of equilibriums, we examine the equilibrium’s local and global stability. Finally, simulations are used to explain how various parameters affect the process of spreading rumors.

A time evolving online social network generation algorithm

The rapid growth of online social media usage in our daily lives has increased the importance of analyzing the dynamics of online social networks. However, the dynamic data of existing online social media platforms are not readily accessible. Hence, there is a necessity to synthesize networks emulating those of online social media for further study. In this work, we propose an epidemiology-inspired and community-based, time-evolving online social network generation algorithm (EpiCNet), to generate a time-evolving sequence of random networks that closely mirror the characteristics of real-world online social networks. Variants of the algorithm can produce both undirected and directed networks to accommodate different user interaction paradigms. EpiCNet utilizes compartmental models inspired by mathematical epidemiology to simulate the flow of individuals into and out of the online social network. It also employs an overlapping community structure to enable more realistic connections between individuals in the network. Furthermore, EpiCNet evolves the community structure and connections in the simulated online social network as a function of time and with an emphasis on the behavior of individuals. EpiCNet is capable of simulating a variety of online social networks by adjusting a set of tunable parameters that specify the individual behavior and the evolution of communities over time. The experimental results show that the network properties of the synthetic time-evolving online social network generated by EpiCNet, such as clustering coefficient, node degree, and diameter, match those of typical real-world online social networks such as Facebook and Twitter.

Increase of low-frequency modes of user dynamics in online social networks during overheating of discussions

User dynamics in online social networks have a significant impact on not only the online community but also real-world activities. As examples, we can mention explosive user dynamics triggered by social polarization, echo chamber phenomena, fake news, etc. Explosive user dynamics are frequently called online flaming. The wave equation-based model for online social networks (called the oscillation model) is a theoretical model proposed to describe user dynamics in online social networks. This model can be used to understand the relationship between explosive user dynamics and the structure of social networks. However, since the oscillation model was introduced as a purely theoretical model of social networks, it is necessary to confirm whether the model describes real phenomena correctly or not. In this paper, we first show a prediction from the oscillation model; the low-frequency oscillation mode of user dynamics will be dominant when the structure of online social networks changes so that user activity is activated. To verify the predictions with actual data, we show spectral analyses of both the log data of posts on an electronic bulletin board site and the frequency data of word search from Google Trends. The results support the predictions from the theoretical model.

Derivation and characteristics of closed‐form solutions of the fundamental equations for online user dynamics

SummaryThe oscillation model, based on the wave equation on networks, can describe user dynamics in online social networks. The fundamental equation of user dynamics can be introduced into the oscillation model to explicitly describe the causal relation of user dynamics yielded by certain specific network structures. Moreover, by considering the sparseness of the link structure of online social networks, a novel fundamental equation of different forms has been devised. In this article, we derive a closed‐form solution of the new fundamental equation. Also, we show that the closed‐form solution of the new fundamental equation can generate the general solution of the original wave equation and investigate the characteristics of the derived general solution.

Perturbative Expansion of the Fundamental Equation of Online User Dynamics for Describing Changes in Eigenfrequencies

The oscillation model has been proposed as a theoretical framework for describing user dynamics in online social networks. This model can represent the user dynamics generated by a particular network structure and allow its causal relationships to be explicitly described. In this paper, by applying perturbation theory to the fundamental equation of the oscillation model, we confirm that we can explicitly trace, at least in principle, the changes in user dynamics associated with changes in the network structure. Specifically, we formulate perturbative expansions up to infinite order, by drawing on inferences from regularities found in perturbative expansions; the accuracy of perturbative expansions of finite order is evaluated by numerical experiments.

Effect of the media on the opinion dynamics in online social networks

The media plays an important role in the process of opinion evolution. It can promote the spread of opinions and make netizens reach consensus towards the specific topic. To study the critical role of the media in the evolution of opinions, we constructed a mathematical model of opinion dynamics under the influence of the media. Through mathematical analysis, we can get analytical solutions which are individuals’ opinion values in the equilibrium and interval of the convergence time. We also employed an online network topology from a real social platform for numerical simulation to verify the theoretical solutions. It found that the participation of the media greatly accelerated the dissemination of opinions. The supporting proportion for the media’s opinion is ramped up quickly when intervention time of the media reaches a certain value which we thought this time as best intervention time. When network size is larger, agents will be more negative in equilibrium. Even intervention time is longer, agents still cannot support the media. With extreme initial opinions and self-confidence of all agents, we found that the media can better influence fully stubborn agents than flexible agents. At the same time, the media can better change flexible agents positions than stubborn agents.

Decay Characteristics of User Dynamics in Online Social Networks

Users' interests in trends and news that are attracting attention on social media are generally not retained for a long time but decrease with time. The activity dynamics, such as the gradual fading of interests of users in the social network, have been described as the damped oscillation model in previous studies. Previous studies assumed a damping coefficient (which expresses the strength of damping of the oscillation) as a constant independent of the frequency, but it is known that the damping coefficient in the general oscillation phenomena depends on the frequency. In this study, we define two matrices (the Laplacian matrix and its square root matrix) which give the social network structure and consider possible characteristics of the decay rate that can be derived from natural assumptions concerning the network structure.

An empirical analysis on the behavioral differentia of the “Elite-Civilian” users in Sina microblog

The human dynamics of online social networks have been studied broadly. However, few works have been done on the behavioral differentia of different categories of users. In this paper, we analyze the user characteristics and post behaviors from the aspect of “Elite”(verified users) and “Civilian”(unverified users) in Sina microblog. We find the follower number distribution of elite users does not fit power law while civilian users’ fits. We also find the civilian users need to be more diligent than elite users to maintain the same level of influence. Besides, there is a linear correlation between users’ influence and post behavior when influence is relatively low. And the inter-tweeting time distributions of the two categories of users have significant difference on day scale while they are similar on minute scale. Furthermore, we propose a modified interest model that could explain why the long-range post behavior of civilian users fits power low with cut-off. Findings provide notable insights for understanding the behaviors of different “social strata” in virtual social space.

Opinion Formation in Online Social Networks: Exploiting Predisposition, Interaction, and Credibility

The challenging but intriguing problem of modeling opinion formation dynamics in online social networks (OSNs) has attracted many researchers in recent years because the inherent complexities present in human opinion update process are yet to be clearly understood. Although the existing works adopt the distance-based homophily principle to model the neighbors’ influences on the formation of an agent’s opinion, they ignore several other key factors that govern the update process. Explicitly, we consider two essential aspects of the real-world opinion formation process that were not explored previously. First, we consider the predisposition of agents that leads to selective exposure to information when presented with different opinion sources. Second, we explicitly consider an agent’s past interaction experience with others and how opinions encountered in the past interactions influence future opinion update process of that agent. Although the confidence level of an agent on the expressed opinion was previously used to distinguish an expert, we propose the concept of the relative credibility of the opinion sources for such distinction. For this, we take into account an agent’s perceived credibility about others and the relative nature of human judgment when exposed to many opinion sources with different credibility. In addition, for the first time, the credibility of sources external to an OSN is considered in the opinion formation model proposed in this paper. We validate our model by analyzing its performance in capturing the real-world opinion formation dynamics using traces collected from an OSN, specifically Twitter. On the other hand, through simulation, various scenarios are created to observe the steady-state outcomes of the dynamics under various influences of our model parameters and network characteristics. Finally, different compelling and practical applications with social and economic values can be built based on our model.

Modeling rumor propagation and refutation with time effect in online social networks

Rumor propagation and refutation form an important issue for spreading dynamics in online social networks (OSNs). In this paper, we introduce a novel two-stage rumor propagation and refutation model with time effect for OSNs. The dynamical mechanism of rumor propagation and refutation with time effect is investigated deeply. Then a two-stage model and the corresponding mean-field equations in both homogeneous and heterogeneous networks are obtained. Monte Carlo simulations are conducted to characterize the dynamics of rumor propagation and refutation in both Watts–Strogatz network and Barabási–Albert network. The results show that heterogeneous networks yield the most effective rumor and anti-rumor spreading. Besides, the sooner authority releases anti-rumor and the more attractive anti-rumor is, the less rumor influence is. What’s more, these findings suggest that individuals’ ability to control themselves and identify rumor accurately should be improved to reduce negative impact of rumor effectively. The results are helpful to understand better the mechanism of rumor propagation and refutation in OSNs.

Collaborative Filtering With Social Exposure: A Modular Approach to Social Recommendation

This paper is concerned with how to make efficient use of social information to improve recommendations. Most existing social recommender systems assume people share similar preferences with their social friends. Which, however, may not hold true due to various motivations of making online friends and dynamics of online social networks. Inspired by recent causal process based recommendations that first model user exposures towards items and then use these exposures to guide rating prediction, we utilize social information to capture user exposures rather than user preferences. We assume that people get information of products from their online friends and they do not have to share similar preferences, which is less restrictive and seems closer to reality. Under this new assumption, in this paper, we present a novel recommendation approach (named SERec) to integrate social exposure into collaborative filtering. We propose two methods to implement SERec, namely social regularization and social boosting, each with different ways to construct social exposures. Experiments on four real-world datasets demonstrate that our methods outperform the state-of-the-art methods on top-N recommendations. Further study compares the robustness and scalability of the two proposed methods.

Investigating the online social network development through the Five Cs Model of Similarity

PurposeThe purpose of this paper is to explore the influence of five dimensions of similarity (i.e. condition similarity, context similarity, catalyst similarity, consequence similarity and connection similarity) on Facebook social networks development.Design/methodology/approachA questionnaire-based survey was conducted with 245 Romanian college students. SmartPLS 3 statistical software for partial least squares structural equation modeling was chosen as the most adequate technique for the assessment of models with both composites and reflective constructs.FindingsMore than 52 percent of the variance in social network development was explained by the advanced similarity model. Each dimension had a positive effect on Facebook social networks development, the highest influences being exerted by condition similarity, context similarity and consequence similarity.Research limitations/implicationsThe current approach is substantively based on the homophily paradigm in explaining social network development. Future research would benefit from comparing and contrasting complementary theories (e.g. the rational self-interest paradigm, the social exchange or dependency theories) with the current findings. Also, the research is tributary to a convenience-based sample of Romanian college students which limits the generalization of the results to other cultural contexts and, thus, invites further research initiatives to test the model in different settings.Social implicationsSimilarity attributes and mechanisms consistently determine the dynamics of online social networks, a fact which should be investigated in depth in terms of the impact of new technologies among young people.Originality/valueThis study is among the first research initiatives to approach similarity structures and processes within an integrative framework and to conduct the empirical analysis beyond US-centric samples.

Users' participation and social influence during information spreading on Twitter.

Online Social Networks generate a prodigious wealth of real-time information at an incessant rate. In this paper we study the empirical data that crawled from Twitter to describe the topology and information spreading dynamics of Online Social Networks. We propose a measurement with three measures to state the efforts of users on Twitter to get their information spreading, based on the unique mechanisms for information retransmission on Twitter. It is noticed that small fraction of users with special performance on participation can gain great influence, while most other users play a role as middleware during the information propagation. Thus a community analysis is performed and four categories of users are found with different kinds of participation that cause the information dissemination dynamics. These suggest that exiting topological measures alone may reflect little about the influence of individuals and provide new insights for information spreading.

A hybrid epidemic model for deindividuation and antinormative behavior in online social networks

With the increasing popularity of user-contributed sites, the phenomenon of “social pollution”, the presence of abusive posts has become increasingly prevalent. In this paper, we describe a novel approach to investigate negative behavior dynamics in online social networks as epidemic phenomena. We show that using hybrid automata, it is possible to explain the contagion of antinormative behavior in certain online commentaries. We present two variations of a finite-state machine model for time-varying epidemic dynamics, namely triggered state transition and iterative local regression, which differ with respect to accuracy and complexity.We validate the model with experiments over a dataset of 400,000 comments on 800 YouTube videos, classified by genre, and indicate how different epidemic patterns of behavior can be tied to specific interaction patterns among users.

Rumor spreading in online social networks by considering the bipolar social reinforcement

Considering the bipolar social reinforcement which includes positive and negative effects, in this paper we explore the rumor spreading dynamics in online social networks. By means of the generation function and cavity method developed from statistical physics of disordered system, the rumor spreading threshold can be theoretically drawn. Simulation results indicate that decreasing the positive reinforcement factor or increasing the negative reinforcement factor can suppress the rumor spreading effectively. By analyzing the topological properties of the real world social network, we find that the nodes with lower degree usually have smaller weight. However, the nodes with lower degree may have larger k-shell. In order to curb rumor spreading, some control strategies that are based on the nodes’ degree, k-shell and weight are presented. By comparison, we show that controlling those nodes that have larger degree or weight are two effective strategies to prevent the rumor spreading.