Opinion Diffusion Research Articles

Public Perception Analysis of Hot Topics in Intellectual Property Based on Microblog Data

Public opinion is a crucial factor for government policy making, implementation, evaluation, and social development. This paper analyzes the public opinion perception of intellectual property subject based on microblog data and puts forward the concept and calculation model for the breadth, depth, and comprehensive value of public perception. Then, taking the opinion evaluation in microblog platform as an example, this paper studies the public perception of intellectual property subject and illustrates the changing trend of public perception through related events in microblog. The results show that the four topics of trademark, intellectual property, copyright, and patent are the hot topics of public perception, the breadth, depth, and comprehensive value of public perception for the four topics fluctuated greatly, on the whole. In addition, when the content of microblog is related to personal benefits, business benefits, and personal interests, it can cause widespread public concern. Additionally, the public mainly participate in the dissemination of public opinion on intellectual property subject through forwarding. Finally, this paper puts forward some suggestions from the perspective of how to maximize the effect of intellectual property policy.

Understanding the Complexity of Business Information Dissemination in Social Media: A Meta-Analysis of Empirical Evidence from China

With the development of social networks, the complexity of the factors affecting the users’ information dissemination is increasing and the complexity of online social networks and influencing factors of individual behaviors and attitudes make the development of online public opinion present a dynamic, complex, and multifactor evolution. Analyzing the influencing factors of public opinion dissemination is conducive to optimize company management and information diffusion management. However, there has been no comprehensive analysis of the complex factors that influence the dissemination of information; this study focused on synthesizing 20 empirical studies on the influencing factors of China public opinion dissemination from the perspective of the user, and a meta-analysis was conducted. We establish the influencing factors of users’ information adoption model from three aspects of information source reliability, perceived information quality, and the heat of public opinion events based on elaboration likelihood model. The results indicated that the main influencing factors of public opinion communication are authority, reliability, quality of information form, quality of information editing, quality of information utility, and event attendance preference. Among the factors, authority and quality of information editing have more significant impacts on users’ information adoption behavior in the dissemination of public opinion. In addition, whether the type of event was a public emergency had a moderating effect. The results are helpful to explore the universality of the influencing factors so as to help related regulators better build a multiangle supervision mechanism and conduct early warning of information diffusion.

Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach

The networked opinion diffusion in online social networks is often governed by the two genres of opinions— endogenous opinions that are driven by the influence of social contacts among users, and exogenous opinions which are formed by external effects like news and feeds. Accurate demarcation of endogenous and exogenous messages offers an important cue to opinion modeling, thereby enhancing its predictive performance. In this article, we design a suite of unsupervised classification methods based on experimental design approaches, in which, we aim to select the subsets of events which minimize different measures of mean estimation error. In more detail, we first show that these subset selection tasks are NP-Hard. Then we show that the associated objective functions are weakly submodular, which allows us to cast efficient approximation algorithms with guarantees. Finally, we validate the efficacy of our proposal on various real-world datasets crawled from Twitter as well as diverse synthetic datasets. Our experiments range from validating prediction performance on unsanitized and sanitized events to checking the effect of selecting optimal subsets of various sizes. Through various experiments, we have found that our method offers a significant improvement in accuracy in terms of opinion forecasting, against several competitors.

Bi-layer voter model: modeling intolerant/tolerant positions and bots in opinion dynamics

The diffusion of opinions in social networks is a relevant process for adopting positions and attracting potential voters in political campaigns. Opinion polarization, bias, targeted diffusion, and the radicalization of postures are key elements for understanding the voting dynamics’ challenges. In particular, social bots are currently a new element that can have a pronounced effect on the formation of opinions during electoral processes by, for instance, creating fake accounts in social networks to manipulate elections. Here, we propose a voter model incorporating bots and radical or intolerant individuals in the decision-making process. The dynamics of the system occur in a multiplex network of interacting agents composed of two layers, one for the dynamics of opinions where agents choose between two possible alternatives, and the other for the tolerance dynamics, in which agents adopt one of the two tolerance levels. The tolerance accounts for the likelihood to change opinion in an interaction, with tolerant (intolerant) agents switching opinion with probability 1.0 (\(\gamma \le 1\)). We find that intolerance leads to a consensus of tolerant agents during an initial stage that scales as \(\tau ^+ \sim \gamma ^{-1} \ln N\), who then reach an opinion consensus during the second stage in a time that scales as \(\tau \sim N\), where N is the number of agents. Therefore, very intolerant agents (\(\gamma \ll 1\)) could considerably slow down dynamics towards the final consensus state. We also find that the inclusion of a fraction \(\sigma _{{\mathbb {B}}}^-\) of bots breaks the symmetry between both opinions, driving the system to a consensus of intolerant agents with the bots’ opinion. Thus, bots eventually impose their opinion to the entire population, in a time that scales as \(\tau _B^- \sim \gamma ^{-1}\) for \(\gamma \ll \sigma _{{\mathbb {B}}}^-\) and \(\tau _B^- \sim 1/\sigma _{{\mathbb {B}}}^-\) for \(\sigma _{{\mathbb {B}}}^- \ll \gamma \).

Electoral forecasting using a novel temporal attenuation model: Predicting the US presidential elections

Electoral forecasting is an ongoing scientific challenge with high social impact, as current data-driven methods try to efficiently combine statistics with economic indices and machine learning. However, recent studies in network science pinpoint towards the importance of temporal characteristics in the diffusion of opinion. As such, we combine concepts of micro-scale opinion dynamics and temporal epidemics, and develop a novel macro-scale temporal attenuation (TA) model, which uses pre-election poll data to improve forecasting accuracy. Our hypothesis is that the timing of publicizing opinion polls plays a significant role in how opinion oscillates, especially right before elections. Thus, we define the momentum of opinion as a temporal function which bounces up when opinion is injected in a system of voters, and dampens down during states of relaxation. We validate TA on survey data from the US Presidential Elections between 1968 and 2016, and TA outperforms several statistical estimates, ARIMA models, as well as the best pollsters at their time, in 10 out of 13 presidential elections. We present two different implementations of the TA model, which accumulate an average forecasting error of 3.04 points over the 48-year period. Conversely, statistical methods accumulate 7.48 points error, and the best pollsters accumulate 3.64 points. Overall, TA offers increases of 23–37% in forecasting performance compared to the state of the art. We show that the effectiveness of TA does not drop when relatively few polls are available; moreover, with increasing availability of pre-election surveys, we believe that our TA model will become a reference alongside other modern election forecasting techniques.

How privacy concerns and cultural differences affect public opinion during the COVID-19 pandemic: a case study

PurposeCOVID-19, an infectious disease first identified in China, has resulted in an ongoing pandemic all over the world. Most of the countries have been experiencing a difficult period during the fighting of this pandemic. The purpose of this study is to explore the effect of privacy concerns and cultural differences on public opinion related to the pandemic. The authors conducted a comparative analysis of public opinion in the US and in China as a case study, in order to determine the results.Design/methodology/approachNational policies on important issues faced during the COVID-19 pandemic in the US and in China were examined through a comparative analysis. The authors used text clustering and visualization to mine public opinion on two popular social media platforms, Twitter and Weibo. From the perspectives of concern for privacy and of national culture, this study combines qualitative and quantitative analysis to discover the acceptance level of national policies by the public in the two countries.FindingsThe anti-pandemic policies and measures of the US and China reflect the different characteristics of their respective political systems and national cultures. When considering the culture of the US, it is hard to establish and enforce a rigorous regulation on either mask wearing in public or home quarantine on the national level. The opinions of US people are diverse, regarding national COVID-19 policies, but they are rather unified on privacy issues. On the other hand, Chinese people show a high acceptance of national policies based on their mask-wearing customs and their culture of collectivism.Originality/valuePrior studies have paid insufficient attention to the ways in which user privacy and cultural difference affect public opinion on national policies between the US and China. This case study that compares public opinion on current and topical issues which are closely bound up with public life shows originality, as it innovatively provides a cross-cultural perspective on the research of public opinion dissemination during emergencies by considering the ongoing COVID-19 pandemic.

Public Opinion Dissemination with Incomplete Information on Social Network: A Study Based on the Infectious Diseases Model and Game Theory

Given the fragmentation of public opinion dissemination and the lag of network users' cognition, the paper analyzes public opinion dissemination with incomplete information, which can provide reference for us to control and guide the spread of public opinion. Based on the derivative and secondary radiation of public opinion dissemination with incomplete information, the Susceptible-Susceptible-Infected-Recovered-Recovered-Infected (SSIRR-I) model is proposed. Given the interaction between users, the Deffuant opinion dynamics model and evolutionary game theory are introduced to simulate the public opinion game between dissemination and immune nodes. Finally, the numerical simulation and results analysis are given. The results reveal that the rate of opinion convergence significantly affects disseminating public opinion, which is positively correlated with the promotion effect of the dissemination node and negatively correlated with the suppression effect of the immune node of public opinion dissemination. Derivative and secondary radiations have different effects on public opinion dissemination in the early stage, but promote public opinion dissemination in the later stage. The dominant immune nodes have an apparent inhibitory effect on the spread of public opinion; nevertheless, they cannot block the dissemination of public opinion.

Majority Opinion Diffusion in Social Networks: An Adversarial Approach

We introduce and study a novel majority based opinion diffusion model. Consider a graph G, which represents a social network. Assume that initially a subset of nodes, called seed nodes or early adopters, are colored either black or white, which correspond to positive or negative opinion regarding a consumer product or a technological innovation. Then, in each round an uncolored node, which is adjacent to at least one colored node, chooses the most frequent color among its neighbors. Consider a marketing campaign which advertises a product of poor quality and its ultimate goal is that more than half of the population believe in the quality of the product at the end of the opinion diffusion process. We focus on three types of attackers which can select the seed nodes in a deterministic or random fashion and manipulate almost half of them to adopt a positive opinion toward the product (that is, to choose black color). We say that an attacker succeeds if a majority of nodes are black at the end of the process. Our main purpose is to characterize classes of graphs where an attacker cannot succeed. In particular, we prove that if the maximum degree of the underlying graph is not too large or if it has strong expansion properties, then it is fairly resilient to such attacks. Furthermore, we prove tight bounds on the stabilization time of the process (that is, the number of rounds it needs to end) in both settings of choosing the seed nodes deterministically and randomly. We also provide several hardness results for some optimization problems regarding stabilization time and choice of seed nodes.

Multi-Party Campaigning

We study a social choice setting of manipulation in elections and extend the usual model in two major ways: first, instead of considering a single manipulating agent, in our setting there are several, possibly competing ones; second, instead of evaluating an election after the first manipulative action, we allow several back-and-forth rounds to take place. We show that in certain situations, such as in elections with only a few candidates, optimal strategies for each of the manipulating agents can be computed efficiently. Our algorithmic results rely on formulating the problem of finding an optimal strategy as sentences of Presburger arithmetic that are short and only involve small coefficients, which we show is fixed-parameter tractable -- indeed, one of our contributions is a general result regarding fixed-parameter tractability of Presburger arithmetic that might be useful in other settings. Following our general theorem, we design quite general algorithms; in particular, we describe how to design efficient algorithms for various settings, including settings in which we model diffusion of opinions in a social network, complex budgeting schemes available to the manipulating agents, and various realistic restrictions on adversary actions.

Reduced Meat Consumption: from Multicriteria Argument Modelling to Agent-Based Social Simulation

A second nutrition transition seems to be emerging towards more plant-based diets, curbing meat consumption in developed countries at the beginning of the 21st century. This shift suggests that rational arguments tend to influence an increasing number of individuals to adopt vegetarian diets. This work aimed to understand and simulate the impact of different types of messages on the choice to change food diets at the individual level, and the impact of the diffusion of opinions at the collective level. It provided two results: (1) a network of arguments around vegetarian diets is modelled using an abstract argumentation approach. Each argument, formalized by a node, was connected with other arguments by arrows, thus formalizing relationships between arguments. This methodology made it possible to formalize an argument network about vegetarian diets and to identify the importance of health arguments compared to ethical or other types of arguments. This methodology also identified key arguments as a result of their high centrality in being challenged or challenging other arguments. The results of constructing this argument network suggested that any controversy surrounding vegetarian diets will be polarized around such high centrality arguments about health. Even though few ethical arguments appeared in our network, the health arguments concerning the necessity or not of animal products for humans were indirectly connected with ethical choices towards vegetarian diets; (2) an agent-based simulation of the social diffusion of opinions and practices concerning meat consumption is then introduced. The purpose of this simulation was to capture the balance of vegetarian vs. meat-based diets. It contributes to modelling consumer choices by exploring the balance between individual values and external influences such as social pressure, communication campaigns and sanitary, environmental or ethical crises.

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

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.

Research on The Numerical Accuracy of Network Public Opinion Propagation Model Based on Runge-Kutta Method

In a SIsIoR public opinion transmission model with discussion mechanism and emotional tendency of public opinion, government intervention is added to list the corresponding dynamic differential equation. It is found that there is no exact solution to the equation. Theoretically, the equation satisfies Routh-Hurwitz stability and Lipschitz continuity, and finally runs to the local equilibrium point. Explicit Euler method and 4th order Runge-Kutta method are used for numerical simulation, and the results are real and effective. By comparing the two methods, it is found that the numerical results of the fourth-order Runge-Kutta method are more accurate, and the iteration step size is shorter, which is more consistent with the theoretical expectation. This method is more suitable for the simulation of network public opinion dissemination, and has the advantages of stronger verifiability and higher numerical accuracy of the model. The simulation of network public opinion transmission based on Runge-Kutta method can provide reliable numerical basis for the guidance of public opinion under the environment of netizens’ emotional tendency, and provide scientific decision-making basis for the government to intervene in network public opinion transmission.

Using sentiment analysis to predict opinion inversion in Tweets of political communication

Social media networks have become an essential tool for sharing information in political discourse. Recent studies examining opinion diffusion have highlighted that some users may invert a message's content before disseminating it, propagating a contrasting view relative to that of the original author. Using politically-oriented discourse related to Israel with focus on the Israeli–Palestinian conflict, we explored this Opinion Inversion (O.I.) phenomenon. From a corpus of approximately 716,000 relevant Tweets, we identified 7147 Source–Quote pairs. These Source–Quote pairs accounted for 69% of the total volume of the corpus. Using a Random Forest model based on the Natural Language Processing features of the Source text and user attributes, we could predict whether a Source will undergo O.I. upon retweet with an ROC-AUC of 0.83. We found that roughly 80% of the factors that explain O.I. are associated with the original message's sentiment towards the conflict. In addition, we identified pairs comprised of Quotes related to the domain while their Sources were unrelated to the domain. These Quotes, which accounted for 14% of the Source–Quote pairs, maintained similar sentiment levels as the Source. Our case study underscores that O.I. plays an important role in political communication on social media. Nevertheless, O.I. can be predicted in advance using simple artificial intelligence tools and that prediction might be used to optimize content propagation.

Disagreement after News: Gradual Information Diffusion or Differences of Opinion?

Abstract This paper considers the puzzle of increased trading volume around information releases through the lens of canonical models of disagreement. I use a unique data set of clicks on news by key finance professionals to simultaneously measure gradual information diffusion and differences of opinion. I find that neither channel subsumes the other and that the two are complementary in generating trading volume around news events. Their relative strengths depend on the characteristics of the underlying information: gradual information diffusion matters more for straightforward news, while differences of opinion play a larger role around textually ambiguous news. (JEL G12, G14, G41, D84) Received January 12, 2020; editorial decision: November 24, 2020 by Editor: Jeffrey Pontiff. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Using a novel genetic algorithm to assess peer influence on willingness to use pre-exposure prophylaxis in networks of Black men who have sex with men

The DeGroot model for opinion diffusion over social networks dates back to the 1970s and models the mechanism by which information or disinformation spreads through a network, changing the opinions of the agents. Extensive research exists about the behavior of the DeGroot model and its variations over theoretical social networks; however, research on how to estimate parameters of this model using data collected from an observed network diffusion process is much more limited. Existing algorithms require large data sets that are often infeasible to obtain in public health or social science applications. In order to expand the use of opinion diffusion models to these and other applications, we developed a novel genetic algorithm capable of recovering the parameters of a DeGroot opinion diffusion process using small data sets, including those with missing data and more model parameters than observed time steps. We demonstrate the efficacy of the algorithm on simulated data and data from a social network intervention leveraging peer influence to increase willingness to take pre-exposure prophylaxis in an effort to decrease transmission of human immunodeficiency virus among Black men who have sex with men.

CNN‐based politics public opinion analysis of undergraduates: A case study with CDN deployment

In the era of social media, the political speech of undergraduates blots out the sky and covers up the earth, which perhaps generates the negative effect. To enhance the management ability of government departments, it is considerably necessary to pay more attention to the public opinion. However, the current researches on the public opinion mainly focus on mathematical modeling or data mining irrespective of public opinion analysis. Consider that Convolutional Neural Network (CNN) has the strong data analysis ability, this paper uses CNN to realize the special application, that is, politics public opinion analysis of undergraduates, which has two functions. On one hand, greatly help the government departments eliminate the crisis timely; On the other hand, correctly guide the political education of undergraduates. Besides, this paper also presents a case study based on Content Delivery Network (CDN) deployment, in which a monitor system of public opinion analysis is implemented to analyze the undergraduates' political speech. Finally, with three public opinion dissemination modes consideration, the experiments are made. The results show that CNN has better training ability and the whole deployment is more significant compared to the state‐of‐the‐art schemes.

Optimal majority dynamics for the diffusion of an opinion when multiple alternatives are available

We consider opinion diffusion on social graphs where agents hold opinions and where social pressure leads them to conform to the opinion manifested by the majority of their neighbors. Within this setting, we look for dynamics that allows us to maximize the diffusion of a target opinion given the initial opinions of all agents. In particular, we focus on the setting where more than two opinions are available to the agents, and we show that the properties of this setting are entirely different from those characterizing the setting where agents hold binary opinions only. Indeed, while it is well-known that greedy dynamics are always optimal ones in the binary case, this is no longer true in our more general setting and—rather surprisingly—even if just three opinions are available. Moreover, while it is possible to decide in polynomial time if a dynamics leading to consensus exists when agents have two available opinions, the problem becomes computationally intractable with three opinions, regardless of the fraction of agents that have the target opinion as their initial opinion.

Network reconstruction of social networks based on the public information.

The structure of a social network plays a crucial role for dynamic analysis, which is invisible in most scenes. In this paper, we present a model for reconstructing the social network by taking into account the public opinion diffusion dynamic model for specific agenda. First, the initial polarity attitude of users i for the agenda, oi, is set in the range [-1,1], where negative and positive attitudes are set as -1 and 1, respectively, while 0 means that user i's attitude is uncertain. Second, we present an optimization model for detecting the relationship among each pair of users based on the generated public observable information. The experimental results for four synthetic networks and three real-world social networks show that the reconstruction accuracy depends on the uncertainty of the initial attitudes greatly. This work is helpful for revealing the structure of social networks in terms of public information.

HK–SEIR model of public opinion evolution based on communication factors

Microblog, with its good interaction and convenient dissemination, has become the main platform for public opinion dissemination. How to discover the law of public opinion dissemination, and to identify the public opinion accurately have become the hot researches. In this paper, we define the user influence, topic popularity, topic interest to analysis the process of opinions fusion among the users under the interest and confidence threshold. We propose a new public opinion evolution HK–SEIR model which combines the opinion fusion HK and the epidemic transmission SEIR models. Firstly, the topic interest degree is added to the opinion fusion HK model, and the interaction behavior between the users under the interest and confidence threshold is analyzed. Then, we calculate the probability of topic propagation caused by the interaction of opinions between users under group pressure, and the probability that users change from the infected state to the removed state under topic popularity. Finally, we analyze the changes of the susceptible, exposed, infected and removed states in the process of public opinion communication. The experiment proves that the HK–SEIR model is closer to the work-rest rules of public opinion communication than SEIR, SIR model. The density peak time is closer to the peak of real public opinion communication. We find that the user interest is the main factor influencing the public opinion dissemination after the interaction of user opinions fusion reaches a certain degree. The negative public opinion of the higher proportion can easily reach the peak of public opinion propagation.

Information diffusion under public crisis in BA scale-free network based on SEIR model — Taking COVID-19 as an example

In the view of the fact that online information in complex networks has an increasingly powerful impact on real society, an improved network public opinion diffusion model under public crisis is established in this paper. The model updates SEIR infectious disease model by using Mean Field Theory based on BA scale-free network, which presents scale-free characteristics as well as enhances the model accuracy and applicability. It conducts a sensitivity analysis and takes “2019-nCoV Explosion” as a sample, crawling through related 180-day microblogs, forwards, comments and likes on Weibo. The results demonstrate that the propagation is greatly influenced by propagation probability, the network structure, the initial spreader and social effects, which contributes to the understanding of network public opinion diffusion and the control of the information dissemination direction.