Opinion Spreading Research Articles

Ideologically motivated biases in a multiple issues opinion model

It has been observed people tend to have opinions that are far more internally consistent than it would be reasonable to expect. Here, we study how that observation might emerge from changing how agents trust the opinions of their peers in a model for opinion dynamics with multiple issues. A previous Bayesian inspired opinion model for continuous opinions is extended to include multiple issues. In the original model, agents tended to trust less opinions that were too different from their own. We investigate the properties of the extended model in its natural form. And we also introduce the possibility the trust of the agent might depend not only on the specific issue but on the average opinions over the many issues. By adopting such a ideological point of view, we observe an important decrease in the spread of individual opinions.

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.

A Combined Deep-Learning and Transfer-Learning Approach for Supporting Social Influence Prediction

Social influence is a phenomenon describing the spread of opinions across the population. Nowadays, social influence analysis (SIA) has a great impact. For example, viral marketing and online content recommendation are applications of SIA. Hand-crafted features, as well as domain expert knowledge, are usually required in convention social influence analysis, but they incur high costs and are not scalable. Deep learning based approaches overcome these issues. For instance, a recently used approach learned latent features of users to predict social influence. In this paper, a teleport probability τ from the page rank domain is integrated into the graph convolution network model for further enhance the performance of such an approach. In addition, a combined personalized propagation of neural predictions (CPPNP) algorithm leads to an impressive prediction accuracy when comparing with existing methods. Evaluation results on three well-known datasets reveal that optimizing τ enhances the performance of CPPNP. Such a combined deep-learning and transfer-learning approach well supports the social influence prediction

Network Opinion Evolution Model Incorporating the Influence of Media Heterogeneity

Opinion dynamics is the basic research content of social network research. Traditional opinion dynamics models focus on the heterogeneity of regular users, but ignore the heterogeneity of media users in social networks. Compared to regular users, media users have the characteristics of releasing more information and having greater influence, and their opinions are not easily affected by regular users. Aiming at the characteristics of information transmission with the participation of media users, we propose an opinion dynamics model to reproduce a realistic process of opinion interaction between regular users and media users and between regular users, respectively. The time variable is also incorporated into the probability of opinion adoption to make sure the opinion interaction model has timeliness. The simulation experiments on models of four typical complex network structures have shown that the characteristic of large average path length in a regular network will lead to the media opinion spreading slowly with a small scope, and a low individual opinion interaction frequency. On the other hand, the characteristics of strong heterogeneity and many center users in a scale-free network lead to media opinion spreading fast with a big scope. Moreover, the opinion adoption probability has a great influence on the average opinion value of the final state in four networks, while the bounded trust threshold has a great influence on opinion evolution in regular networks and scale-free networks. Furthermore, the media location selection approach based on the node betweenness results in opinion influence transmission with high speed and large range compared with the approaches based on node degree and clustering coefficient.

On Inference of Network Topology and Confirmation Bias in Cyber-Social Networks

This article studies topology inference, from agent states, of a directed cyber-social network with opinion spreading dynamics model that explicitly takes confirmation bias into account. The cyber-social network comprises a set of partially connected directed network of agents at the social level, and a set of information sources at the cyber layer. The necessary and sufficient conditions for the existence of exact inference solution are characterized. A method for exact inference, when it is possible, of entire network topology as well as confirmation bias model parameters is proposed for the case where the bias mentioned earlier follows a piece-wise linear model. The particular case of no confirmation bias is analyzed in detail. For the setting where the model of confirmation bias is unknown, an algorithm that approximates the network topology, building on the exact inference method, is presented. This algorithm can exactly infer the weighted communication from the neighbors to the non-followers of information sources. Numerical simulations demonstrate the effectiveness of the proposed methods for different scenarios.

Evolutionary Game Model of Public Opinion Information Propagation in Online Social Networks

The rapid development of communication technology has greatly changed the way of information propagation. While making use of positive public opinion information in online social networks (OSN) to create value, it is necessary to manage and control the propagation of public opinion. Considering the existence of both positive and negative public opinion, we proposed a tripartite evolutionary game model through identifying the relevant stakeholders involved in the public opinion spreading process, discussed the equilibrium conditions of stakeholders’ behavior strategies emphatically and carried out simulation experiments. Then, based on the experimental results, the management strategy and the key intervention points of public opinion spreading were proposed. The result shows that the key to management and control public opinion is realizing the interest balance of all stakeholders. That is, the government should increase the benefits of netizens and media spreading (reporting) positive public opinion, and at the same time strengthen the punishment of them spreading (reporting) negative public opinion. This paper further expands the research of public opinion propagation in OSN, and provides theoretical support and decision-making basis for the management and control of public opinion.

THE EFFECT OF NETWORK TYPES ON HERD BEHAVIOR

THE EFFECT OF NETWORK TYPES ON HERD BEHAVIOR

Efficient spread-size approximation of opinion spreading in general social networks.

In contemporary society, understanding how information, such as trends and viruses, spreads in various social networks is an important topic in many areas. However, it is difficult to mathematically measure how widespread the information is, especially for a general network structure. There have been studies on opinion spreading, but many studies are limited to specific spreading models such as the susceptible-infected-recovered model and the independent cascade model, and it is difficult to apply these studies to various situations. In this paper, we first suggest a general opinion spreading model (GOSM) that generalizes a large class of popular spreading models. In this model, each node has one of several states, and the state changes through interaction with neighboring nodes at discrete time intervals. Next, we show that many GOSMs have a stable property that is a GOSM version of a uniform equicontinuity. Then, we provide an approximation method to approximate the expected spread size for stable GOSMs. For the approximation method, we propose a concentration theorem that guarantees that a generalized mean-field theorem calculates the expected spreading size within small error bounds for finite time steps for a slightly dense network structure. Furthermore, we prove that a “single simulation” of running the Monte Carlo simulation is sufficient to approximate the expected spreading size. We conduct experiments on both synthetic and real-world networks and show that our generalized approximation method well predicts the state density of the various models, especially in graphs with a large number of nodes. Experimental results show that the generalized mean-field approximation and a single Monte Carlo simulation converge as shown in the concentration theorem.

CAOM: A community-based approach to tackle opinion maximization for social networks

Opinion Maximization Problem (OMP) targets at selecting a subset of influential initial nodes and eventually generating the maximum opinion spread. The current OMP methods mainly pay attention to the improvement of efficient algorithms, which hardly obtain high efficiency and stable accuracy in large-scale social networks. In this paper, we study the OMP with the community-based approach. To be specific, we first formulate the OMP and construct the weight-based opinion model to estimate the dynamic change of opinion value. In particular, to generate the influential individuals, we propose a Community-based Approach for the OMP (CAOM), including: community detection, selection of candidate nodes and generation of seed nodes. Then, to reduce the computational complexity effectively and distribute seed nodes into the reasonable communities, the significant communities are devised. Based on the one-hop measure and the potential nodes of each community, the candidate nodes are selected. For each community, we acquire the influence score based on its neighbors within community and beyond community. Finally, we develop the two-hop measure and Elimination of Overlapping Influence (EOI) to determine seed nodes from candidate nodes. Experimental results in ten social networks demonstrate that CAOM can accelerate the opinion spread with smaller running time compared with the baselines.

Internet Public Opinion Risk Grading under Emergency Event Based on AHPSort II-DEMATEL

The internet often serves as a communication platform for users to freely express opinions. However, as it supports the quick and wide spread of public opinions, it often results in great uncertainty and is very difficult to control. As a result, evaluating the risk levels of internet public opinion becomes very challenging. The safety of internet public opinion affects the sustainability and stability of society. In this paper, a novel conceptual model of internet public opinion governance is proposed. The model can be used to grade risk levels by combining the Analytic Hierarchy Process Sort II (AHPSort II) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) under a fuzzy environment with triangular fuzzy sets. A numerical example is provided to verify the efficiency of the proposed method, and some managerial implications are also discussed.

Zombie politics: evolutionary algorithms to counteract the spread of negative opinions

This paper is about simulating the spread of opinions in a society and about finding ways to counteract that spread. To abstract away from potentially emotionally laden opinions, we instead simulate the spread of a zombie outbreak in a society. The virus causing this outbreak is different from traditional approaches: It not only causes a binary outcome (healthy vs. infected) but rather a continuous outcome. To counteract the outbreak, a discrete number of infection-level-specific treatments are available. This corresponds to acts of mild persuasion or the threats of legal action in the opinion spreading use case. This paper offers a genetic and a cultural algorithm that find the optimal mixture of treatments during the run of the simulation. They are assessed in a number of different scenarios. It is shown that albeit far from being perfect, the cultural algorithm delivers superior performance at lower computational expense.

Opinion Maximization Through Unknown Influence Power in Social Networks Under Weighted Voter Model

Opinion maximization in social networks is an optimization problem, which targets at determining some influential individuals (i.e., seed nodes), propagating the desired opinion to their neighbors, and eventually obtaining maximum opinion spread. Previous studies assume that influence power of one individual is mainly calculated by using some network structure properties and once the opinion of one individual is determined, its opinion usually keeps unchanged. However, in the real scenario, the influence power of one individual may be unknown and should be closely associated with the dynamic opinion formation process. In this paper, we propose a novel Influence Power-based Opinion Framework (IPOF) to solve the opinion maximization problem, which is composed of two phases: 1) influence power estimation, and 2) elimination of influence overlapping (EIO). Specifically, we design the exponential influence power and estimate the unknown parameter of influence power through maximum likelihood estimation due to its simplicity, practicability, and superior convergence in large samples. To generate the opinion series dynamically, the weighted voter model is proposed by leveraging influence power and intimate degree. Moreover, we also prove that the likelihood function is concave by using Hessian matrix. To determine the initial seed nodes and facilitate large opinion propagation, influence power-based EIO algorithm is proposed. Experimental results in six social networks demonstrate that the proposed approach outperforms the state-of-the-art benchmarks.

Developing a framework of quality indicators for healthcare business cases: a qualitative document analysis consolidating insight from expert guidance and current practice

BackgroundBusiness cases are used to provide a structured justification in favour of investing in new projects, services or interventions. Despite the use of business cases in determining how limited resources will be allocated within England’s National Health Service (NHS), guidance concerning how to develop and evaluate business cases in the context of healthcare is inconstant and of varying relevance. This study aimed to develop a new framework of quality indicators for healthcare-related business cases by analysing the content of expert guidance documents and a sample of NHS business cases.MethodsQualitative document analysis was conducted on guidance documents (n = 7) and existing NHS business case documents (n = 18). Documents were purposefully sampled using criteria to ensure the framework reflected a diverse spread of expert opinion, and a varied sample of example business cases from current practice. Data were analysed using thematic and content analysis, and are presented in a visualised framework.ResultsSeven themes were identified within the qualitative document analysis (purpose, strategic priorities, options, benefits, costs, risks and evaluation). These themes were described and presented with a framework of quality indicators for healthcare-related business cases.ConclusionTo ou`r knowledge, this is the first framework of business case quality indicators designed specifically for use in a healthcare context. The framework presented in this study has implications for how business cases are developed and evaluated by decision makers. In the future it would be beneficial to investigate how the framework could be used in practice as a tool for critical appraisal.

Early prediction of the outcome of Kickstarter campaigns: is the success due to virality?

The spread of information, opinions, preferences, and behavior across social media is a crucial feature of the current functioning of our economy, politics, and culture. One of the emerging channels for spreading social collective action and funding of novelty in all these domains is Crowdfunding on various platforms such as Kickstarter, Indiegogo, Sellaband, and may others. The exact spreading mechanism of this collective action is not well-understood. The general belief is that virality plays a crucial role. Namely, the common hypothesis is that the information or behavior propagates through individuals affecting one another, presumably, through the links connecting them in social networks. The aim of our study is to find out the actual spreading mechanism in one particular case: spread of financial support for individual Kickstarter campaigns. To our surprise, our studies show that “virality” plays here only a minor role. We used this result to construct a simple behavior-grounded stochastic predictor of the success of Kickstarter campaigns which is not based on the viral mechanism. The crucial feature of the model underlying the prediction algorithm is that the success of a campaign depends less on the backers influencing one another (“virality”) but rather on the campaign appealing to a particular class of high-pledge backers. This appeal is usually revealed at the very beginning of the campaign and it is an excellent success predictor. The case of Kickstarter is consistent with a recently proposed generic hypothesis that popularity in social media arises more from independent responses by individuals belonging to a large homophily class rather than from percolation, self-exciting processes, and other cooperative mechanisms resulting from mutual influence between individuals. Thus, the very concept of “virality”, which implies contagion between participating individuals, plays only a minor role in the success mechanism proposed hereby. A more appropriate term for the mechanism underlying the social success in our model could be “social appeal” or “social fitness”.

Battle of Opinions Over Evolving Social Networks

Social networking environments provide major platforms for the discussion and formation of opinions in diverse areas, including, but not limited to, political discourse, market trends, news, and social movements. Often, these opinions are of a competing nature, e.g., radical vs. peaceful ideologies, correct information vs. misinformation, and one technology vs. another. We study the battles of such competing opinions over evolving social networks. The novelty of our model is that it captures the exposure and adoption dynamics of opinions that account for the preferential and random nature of exposure as well as the persuasion power and persistence of different opinions. We provide a complete characterization of the mean opinion dynamics over time as a function of the initial adoption, as well as the particular exposure, adoption, and persistence dynamics. Our analysis, supported by case studies, reveals the key metrics that govern the spread of opinions and establishes the means to engineer the desired impact of an opinion in the presence of other competing opinions.

Memory-induced complex contagion in epidemic spreading

Albeit epidemic models have evolved into powerful predictive tools for the spread of diseases and opinions, most assume memoryless agents and independent transmission channels. We develop an infection mechanism that is endowed with memory of past exposures and simultaneously incorporates the joint effect of multiple infectious sources. Analytic equations and simulations of the susceptible-infected-susceptible model in unstructured substrates reveal the emergence of an additional phase that separates the usual healthy and endemic ones. This intermediate phase shows fundamentally distinct characteristics, and the system exhibits either excitability or an exotic variant of bistability. Moreover, the transition to endemicity presents hybrid aspects. These features are the product of an intricate balance between two memory modes and indicate that non-Markovian effects significantly alter the properties of spreading processes.

Hipsters on networks: How a minority group of individuals can lead to an antiestablishment majority.

The spread of opinions, memes, diseases, and “alternative facts” in a population depends both on the details of the spreading process and on the structure of the social and communication networks on which they spread. One feature that can change spreading dynamics substantially is heterogeneous behavior among different types of individuals in a social network. In this paper, we explore how antiestablishment nodes (e.g., hipsters) influence the spreading dynamics of two competing products. We consider a model in which spreading follows a deterministic rule for updating node states (which indicate which product has been adopted) in which an adjustable probability of the nodes in a network are hipsters, who choose to adopt the product that they believe is the less popular of the two. The remaining nodes are conformists, who choose which product to adopt by considering which products their immediate neighbors have adopted. We simulate our model on both synthetic and real networks, and we show that the hipsters have a major effect on the final fraction of people who adopt each product: even when only one of the two products exists at the beginning of the simulations, a small fraction of hipsters in a network can still cause the other product to eventually become the more popular one. To account for this behavior, we construct an approximation for the steady-state adoption fractions of the products on -regular trees in the limit of few hipsters. Additionally, our simulations demonstrate that a time delay in the knowledge of the product distribution in a population, as compared to immediate knowledge of product adoption among nearest neighbors, can have a large effect on the final distribution of product adoptions. Using a local-tree approximation, we derive an analytical estimate of the spreading of products and obtain good agreement if a sufficiently small fraction of the population consists of hipsters. In all networks, we find that either of the two products can become the more popular one at steady state, depending on the fraction of hipsters in the network and on the amount of delay in the knowledge of the product distribution. Our simple model and analysis may help shed light on the road to success for antiestablishment choices in elections, as such success—and qualitative differences in final outcomes between competing products, political candidates, and so on—can arise rather generically in our model from a small number of antiestablishment individuals and ordinary processes of social influence on normal individuals.

Percolation and Internet Science

Percolation, in its most general interpretation, refers to the “flow” of something (a physical agent, data or information) in a network, possibly accompanied by some nonlinear dynamical processes on the network nodes (sometimes denoted reaction–diffusion systems, voter or opinion formation models, etc.). Originated in the domain of theoretical and matter physics, it has many applications in epidemiology, sociology and, of course, computer and Internet sciences. In this review, we illustrate some aspects of percolation theory and its generalization, cellular automata and briefly discuss their relationship with equilibrium systems (Ising and Potts models). We present a model of opinion spreading, the role of the topology of the network to induce coherent oscillations and the influence (and advantages) of risk perception for stopping epidemics. The models and computational tools that are briefly presented here have an application to the filtering of tainted information in automatic trading. Finally, we introduce the open problem of controlling percolation and other processes on distributed systems.

The Motive of Compensatory House-Purchase: Continuous Improvement in the Sex Ratio of Higher Education and the Rise of Housing Prices

In recent years, a very important phenomenon has emerged in the marriage market, which is the change of the sex ratio of higher education （manifested as the significant improvement of women’s education compared with men’s education）. This paper uses micro survey data and pro-vincial panel data to investigate the relationship between this phenomenon and the rise of housing prices comprehensively. It attempts to answer the following two questions: （1） How does the change of the sex ratio of higher education affect residents’ house-purchase behavior? （2） Does the micro motive caused by the change of the sex ratio of higher education promote the rise of housing prices at the macro level? Based on the marriage theory of Becker, this paper puts forward a new motive of house-purchase, which provides a theoretical explanation for the increasing pressure of men of house-purchase and links the sex ratio of higher education to the rise of housing prices in China. The main logic of this paper is that, because of the mechanism inflexibility of the marriage income distribution, the bargaining power of men and women before marriage is not consistent with the income distribution after marriage. With the continuous improvement of the sex ratio of higher education （women/men）, men will compensate for the improvement of women’s education level by buying a house in the marriage matching. Family-level data and provincial-level data confirm this hypothesis. Family-level results show that households with wives of higher education are more likely to buy houses than to rent. In the provincial panel regression, the ratio of females above junior college to males is used to measure the sex ratio of higher education, and the results show that the faster the sex ratio of higher education increases, the faster the housing price rises. This paper expands the research on the influence of the marriage market on housing prices. Existing research ignores this important phenomenon in the marriage market, that is, the number of marriageable women is decreasing relatively, and their quality is also greatly improving relatively. This paper has important theoretical significance. The hypothesis proposed in this paper complements the research of Wei et al. （2012）, and does not have a major defect in their paper, that is, the imbalance of the sex ratio of marriageable population mainly occurs in economically backward regions, while the rise of housing prices mainly occurs in economically developed regions. The compensatory effect caused by the sex ratio of higher education and the tournament effect caused by the imbalance of the sex ratio of marriageable population jointly affect the house-purchase pressure of men, if we only emphasize the tournament effect caused by the imbalance of the sex ratio of marriageable population, we cannot explain many other phenomena. Moreover, this paper has important practical significance. The spread of the public opinion that mother-in-laws drives up housing prices” illustrates that buying a house for marriage in China is the most important motive of house-purchase, hence this paper points out that the government should weaken the influence of the compensatory house-purchase motive on housing prices through many measures, such as guiding the public opinion.

SETQR Propagation Model for Social Networks

The study of the laws and influencing factors of information dissemination in social networks is critical to analyzing the spread of public opinion, preventing the spread of rumors, and guiding information transmission. This paper addresses the shortcomings of the traditional SEIR model. We establish the SETQR model and use the probability theorem to derive the law of information propagation. Furthermore, the equilibrium point and the basic regeneration number of the SETQR model are obtained using differential dynamics and the regenerative matrix method. The stability of the SETQR model at the equilibrium point is derived theoretically. Finally, experimental verification is conducted. The simulation results indicate that the SETQR model achieves local stability at the equilibrium point, which is consistent with the results of the theoretical analysis. Through further simulation, the effects of time lag, containment, and forgetting mechanisms on the speed of information dissemination and the time required for the network to reach equilibrium are analyzed.