An adaptive group decision making framework: Individual and local world opinion based opinion dynamics

Opinion dynamics (OD) models, which simulate individuals’ opinion evolution process on social network to analyze the final state of opinion distribution in a group, usually differ from each other due to the differences in social network evolution rules and opinion evolution rules. However, most existing social network evolution rules and opinion evolution rules usually cannot characterize the comprehensive influence of key factors such as neighbors and opinion differences in social relationships. To fully consider the properties of social network evolution and improve the efficiency of consensus reaching process in group decision making, this paper introduces the concept of local world opinion derived from individuals’ common friends, and then proposes an individual and local world opinion-based OD model. In the proposed model, social network evolution is jointly determined by the distance between individual opinions and network structure similarity. The pair of individuals with the largest consensus improvement space are then suggested to adjust their opinions by using an adaptive individual opinion adjustment mechanism. Finally, detailed simulation results are provided to demonstrate the convergence of the proposed model and analyze different parameters’ effects on the stabilized time steps and the number of stable state opinion clusters.     

Influence identification of opinion leaders in social networks: an agent-based simulation on competing advertisements

In social networks, factors that influence the spread of information are essential for companies to comprehend. This study uses the opinion dynamic theory to investigate the influence of multiple advertisement opinion leaders in social networks. We construct an integrated bounded confidence model to simulate the evolution of followers’ opinions under two advertisement opinion leaders. Through experimental simulation, we found that the weight of influence on advertisements has a dual effect on the evolution of followers’ opinions, and the probability that information is transmitted by opinion leaders has a significant impact on the evolution of collective opinions. The results show that, for competitive products, companies should properly understand the propaganda power of product advertisements and improve the probability of information being successfully transmitted by opinion leaders.     

A public and large-scale expert information fusion method and its application: Mining public opinion via sentiment analysis and measuring public dynamic reliability

With the rapid development of social media, reliable information released by the public on social media can provide important decision-making support. Therefore, the consideration of the public as another decision-making body participating in large-scale group decision-making (LSGDM) problems has become an extensively researched topic. However, the participation of the public as a decision-making body with decision-making experts faces several issues, such as the acquisition of public opinion, the reliability of public opinion, the integration of public and expert opinions, etc. Given this, this paper proposes a public and large-scale expert information fusion method that considers public dynamic reliability via sentiment analysis and intuitionistic fuzzy number (IFN) expressions. First, sentiment analysis technology is used to process public social media data and obtain IFNs as the opinions of the public decision-making body. Second, the concept of public dynamic reliability is defined to measure the degree of integration of public opinion. Third, a novel information entropy measure of IFNs is proposed, and a new method is introduced to determine the criteria weights under the two different decision-making bodies. Finally, an optimization model that considers the consensus levels of expert subgroups is proposed to determine the weights of different decision-making bodies. The public and expert opinions are then aggregated to obtain collective decision-making information. A case study is proposed to illustrate the application of the proposed method, and the comparative analysis reveals the features and advantages of this model.     

基于正负反馈的SEIR微博舆情传播模型

社交网络舆情已经成为社会舆情的主要阵地。针对传统模型难以描述社交网络舆情话题的真实传播过程,分析社交网络舆情话题的真实特点,补充加入社交网络中显著的水军和僵尸粉这2大显著特征,作为舆情话题传播中的正负反馈,分别对舆情话题的传播起到推动及抑制作用,构建带有正负反馈的社交网络舆情传播话题模型,提高舆情预测模型的准确率,得出正负反馈对舆情传播的影响力。     

Optimizing consensus reaching in the hybrid opinion dynamics in a social network•

Hybrid opinion dynamics which involves two types of individuals (i.e., leaders and followers) communicate in real time and share opinions and knowledge have been widely used in diverse applications. In real applications of hybrid opinion dynamics, one of the main demands is how to manage a consensus among individuals. This paper aims at proposing a novel consensus reaching strategy for the hybrid opinion dynamics in a social network. Firstly, we give the network partition algorithm to divide the social network into sub-network, and introduce Floyd algorithm to calculate the shortest path between any two individuals, which can provide the assistance for determining the weights among individuals. On this basis, we present the hybrid opinion dynamics model. Next, we develop the consensus reaching model with minimum adjustments (i.e. CRMD model) in hybrid opinion dynamics, and discuss some the properties of the CRMD model. Furthermore, the detailed numerical and simulation analysis are conducted to illustrate the effectiveness of this CRMD model. The simulation results show the CRMD model has the distinct advantages over other consensus strategies. Thus, the CRMD model is helpful to manage and control the public opinions for the government and enterprise.     

分层演化趋向行为的网络舆情传播模型

通过构建带有分层行为演化趋向的舆情传播模型,研究了媒体作用下分层行为舆情演变的内在规律。在参考疾病传播模型SIR（susceptible infected recovered）和带媒体干预的SIaIbR（susceptible infected-a infected-b recovered）模型基础上,提出了带有媒体干预的具有分层演化趋向行为的舆情演变模型（SI）³R,与SIR模型不同的是（SI）³R模型引入了群体分层这一概念,并且在演化过程中处于群体不同分层中的个体带有不同的演化趋势。通过对不同层次中个体的影响,媒体能够发挥更有效的作用。给出了分层演化群体模型及其动力学方程,通过数值求解,模拟了分层媒体作用对传播过程的影响以及初始分层密度对传播过程的影响。     

社交网络下考虑决策者角色差异的舆情演化模型

互联网加速了舆情的发展,舆情讨论时刻都在发生.为了探索决策者角色差异对舆情演化的影响,改进传统DeGroot模型,在社交网络环境下考虑多种决策者角色共存的舆情演化模型.将决策者分为权威者和非权威者,从社会心理学的角度将非权威者随机分为服从者、反抗者和独立者.针对传统模型的权重确定方式较为机械的不足,引入节点在社会网络中的特征向量中心性,提出新的权重确定方法.每一步舆情演化均分为两个阶段:第1阶段所有决策者都进行舆情演化,随后服从者和反抗者在第2阶段对其第1阶段的观点进行调整.在连通的Erdddotos-Rényi网络中进行仿真实验,结果显示,服从者总是会与权威者达成共识,且该共识由权威者的意见决定;反抗者聚集在与服从者和权威者的共识较远的意见簇;独立者的稳定意见则散落分布在二者之间,且更加靠近服从者和权威者的共识.     

复杂网络演化的舆论动力学模型及仿真分析

针对舆论传播过程中复杂动力学演化问题,提出一种基于传播动力学的舆论动态演化模型。首先,构建舆论及舆论演化模型,通过方程变换求出静态解;其次,引入Fokker-Planck方程对舆论演化渐近行为进行分析,得到稳态解决方案并求解,构建复杂网络与模型的关联并提出仿真研究实验目的;最后,通过对舆论演化模型及引入Fokker-Planck方程的舆论意见模型进行仿真分析,并以真实微博舆论数据为例进行实证分析,研究舆论在复杂网络中传播和演化的实质。实验结果表明舆论网络演化渐近行为与度分布相一致,网络舆论传播中的连接方式会受到节点意见影响,模型能有效描述微博舆论传播网络形成和演化过程的动力学行为。     

Reflecting on Social Influence in Networks

In many social contexts, social influence seems to be inescapable: the behavior of others influences us to modify ours, and vice-versa. However, social psychology is full of examples of phenomena where individuals experience a discrepancy between their public behavior and their private opinion. This raises two central questions. First, how does an individual reason about the behavior of others and their private opinions in situations of social influence? And second, what are the laws of the resulting information dynamics? In this paper, we address these questions by introducing a formal framework for representing reasoning about an individual’s private opinions and public behavior under the dynamics of social influence in social networks. Moreover, we dig deeper into the involved information dynamics by modeling how individuals can learn about each other based on this reasoning. This compels us to introduce a new formal notion of reflective social influence. Finally, we initialize the work on proof theory and automated reasoning for our framework by introducing a sound and complete tableaux system for a fragment of our logic. Furthermore, this constitutes the first tableau system for the “Facebook logic” of J. Seligman, F. Liu, and P. Girard.     

Consensus in a social network with two principals

This paper considers a model of opinion dynamics in a social network with two principals, in which the members may affect the opinions of each other and their opinions evolve according to a time-homogeneous Markov chain. We study the existence of a consensus in this network for two types of influence models, namely, when the principals may or may not affect the opinions of each other directly. In addition, we find the values of social network parameters under which a consensus is reached. For the cases without a consensus in its standard definition, we introduce the notion of a consensus of the majority and find the parameter values under which it is reached. Two numerical examples illustrate the obtained theoretical results.     

不良舆情话题在传播节点网络模型的演化规律研究

不良网络舆情是网络时代的一种重要舆论形态,反映了公众对突发事件的情绪表达,具有很强的社会影响力,需要及时处置和正确引导。本文分析了突发事件网络舆情的演化动力和影响因素,对不同阶段的舆情传播演化规律进行研究,并同步构建了网络舆情的传播模型,从而为做好舆情防控工作提供参考。     

基于非理性博弈的舆情传播仿真建模研究

随着社交网络的不断发展,借助社交网络进行传播的舆情信息的威胁越来越大. 本文利用博弈论的方法研究了舆情信息的传播机制,从理性博弈和非理性博弈的角度出发对个体与邻居的交互行为进行建模. 随后以目前较为成熟的传染病模型为基础,通过引入传播学中的社会威慑因素来构建基于非理性博弈的舆情传播模型,以实现适应舆情传播研究的仿真系统,继而将基于非理性博弈的舆情传播模型在无标度网络和小世界网络上的动力学特征进行理论分析. 在仿真环境中对非理性博弈的舆情传播模型进行实验,分析模型参数、网络密度和舆情传播源对舆情传播的影响,其结果符合理论分析结果. 最后对无标度网络上舆情传播的控制策略进行了研究,包括:社会威慑方法、目标免疫方法和正面消息方法,为社交网络上的政策制定提供了理论基础.     

Opinion evolution of online consumer reviews in the e-commerce environment

Online consumer reviews play an important role in shaping potential customers’ purchase decisions in e-commerce. Previous studies have analyzed the influence of online consumer reviews on sales, mainly considering factors such as reviewers’ and viewers’ profiles, information provided, and product features. However, there are relatively few studies that discuss how online consumer reviews interact with each other and how consumers’ opinions evolve over time. This paper proposes an opinion evolution dynamics model that is applicable to online consumer reviews in the e-commerce environment by taking into account influencing factors such as viewer reading limits, review sorting and releasing strategies, convergence parameters, review posting possibilities, and confidence thresholds. Using multi-agent simulation based on the proposed opinion evolution dynamics model, the paper discusses how these factors affect viewers’ opinions, and the opinion evolution process itself. Finally, conclusions and managerial implications of the simulation results are discussed.     

Social Media Analysis and Public Opinion: The 2010 UK General Election

Social media monitoring in politics can be understood by situating it in theories of public opinion. The multimethod study we present here indicates how social media monitoring allow for analysis of social dynamics through which opinions form and shift. Analysis of media coverage from the 2010 UK General Election demonstrates that social media are now being equated with public opinion by political journalists. We use interviews with pollsters, social media researchers and journalists to examine the perceived link between social media and public opinion. In light of competing understandings these interviews reveal, we argue for a broadening of the definition of public opinion to include its social dimension.     

基于两级传播理论的舆论超网络传播分析

社交网络挖掘可以使人们更好地认识信息在网络中的传播规律,分析信息在事件中的传播特点。现有的文献研究主要集中于舆论事件社交网络的静态建模,以及针对一些共性特点的仿真验证,而对舆论事件模型结构变化的讨论较少。本文尝试从两级传播理论出发,采用三层超网络结构对舆论事件不同时段构建传播分析模型,给出舆论演化分析度量指标,挖掘超网络结构变化的特点,探索舆论酝酿期积蓄力量的潜在因素。以长生疫苗事件进行分析,发现需要在酝酿期有多样化的意见领袖不断在各个话题中进行牵引,在积累了潜在的舆论人群之后才能促成舆论爆发。     

Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies

Consensus reaching is an iterative and dynamic process that supports group decision-making models by guiding decision-makers towards modifying their opinions through a feedback mechanism. Many attempts have been recently devoted to the design of efficient consensus reaching processes, especially when the dynamism is dependent on time, which aims to deal with opinion dynamics models. The emergence of novel methodologies in this field has been accelerated over recent years. In this regard, the present work is concerned with a systematic review of classical dynamic consensus and opinion dynamics models. The most recent trends of both models are identified and the developed methodologies are described in detail. Challenges of each model and open problems are discussed and worthwhile directions for future research are given. Our findings denote that due to technological advancements, a majority of recent literature works are concerned with the large-scale group decision-making models, where the interactions of decision-makers are enabled via social networks. Managing the behavior of decision-makers and consensus reaching with the minimum adjustment cost under social network analysis have been the top priorities for researchers in the design of classical consensus and opinion dynamics models.     

融合社交网络用户自身属性的信息传播数学建模与舆情演化分析

针对传统的社交网络信息传播模型极少将用户属性和信息特征这两个因素纳入到信息传播模型研究中的不足,该文提出了一种基于用户自身属性的信息传播模型。首先该文抽取用户影响力、用户态度、用户年龄、信息能量、信息价值等特征并构建交互规则;其次,根据这些特征建立信息传播的数学模型,模拟社交网络舆情演化过程;最后,为验证模型的有效性,开展了与真实事件的实证分析对比实验。实验结果表明: 仿真结构与真实数据的相似度大于0.97,因而该模型符合社交网络舆情信息传播的特性,能够较为准确地描述社交网络中的舆情传播过程。     

An active opinion dynamics model: the gap between the voting result and group opinion

Originally developed to simulate the evolution of public opinion, opinion dynamics models have also been successfully applied to market pricing and advertising. However, passive interactions initiated by locational or social relationships in these models are insufficient to characterize purposeful behaviours such as canvassing or trading, where people are driven by their specific intrinsic motivations. Here, we propose an active model in which people tend to communicate with someone who is more likely to be an ally and game theoretically decide whether to interact. Model simulations highlight the macroscopic development of opinion evolution, showing the ubiquitous gap between people’s voting result and their collective opinion, and how it narrows with the stabilization of opinion evolution. Our results help explain why group opinion rarely reverses its initial stance and the significance of a level of inclusiveness that is neither too high nor too low. Additionally, we find and attest to the probability distribution of group opinion change, which contributes to predicting how much the collective opinion of a group will change after full discussion.     

基于区块链技术的在线社会网络舆情传播模型与干预治理

区块链的快速发展和普及,为网络社会网络舆情的传播提供了便捷的平台,这使得网络舆情逐渐成为人民群众最活跃的反映方式.当网络舆情涉及社会热点等问题时,会引起网民的快速关注,甚至引发突发事件和连锁反应,考察政府的治理干预能力,甚至对国家社会安全和稳定构成紧张.本文根据网络舆情传播的特点,从个体行为和群体社会强化两个方面揭示网...