Opinion Evolution Research Articles

Research on the Sentiment Analysis and Evolutionary Mechanism of Sudden Network Public Opinion Based on SNA-ARIMA Model with Text Mining

This article is based on the massive text data in the digital era, using text mining methods to explore the evolution mechanism of online public opinion, in order to achieve the control of public opinion trends in sudden network events and regulate the stability of online and offline public opinion. We introduced time series for decision-making assistance, combined with time series autoregressive integrated moving average model (ARIMA) for prediction, and comprehensively evaluates the evolution of public opinion from two dimensions: sentimental orientation and social network analysis (SNA). Based on this, a new exploration model for public opinion evolution, SNA-ARIMA, is constructed. The research results indicate that the process of public opinion dissemination exhibits distinct phased characteristics, and each stage is comprehensively influenced by different key nodes or factors. This study provides decision support for managing public opinion crises, making the identification of key nodes in online public opinion events, the evolution mechanism of public opinion, and the guidance of public opinion crises more systematic, forward-looking, and scientific.

Enhancing Zero-Shot Stance Detection with Contrastive and Prompt Learning.

In social networks, the occurrence of unexpected events rapidly catalyzes the widespread dissemination and further evolution of network public opinion. The advent of zero-shot stance detection aligns more closely with the characteristics of stance detection in today's digital age, where the absence of training examples for specific models poses significant challenges. This task necessitates models with robust generalization abilities to discern target-related, transferable stance features within training data. Recent advances in prompt-based learning have showcased notable efficacy in few-shot text classification. Such methods typically employ a uniform prompt pattern across all instances, yet they overlook the intricate relationship between prompts and instances, thereby failing to sufficiently direct the model towards learning task-relevant knowledge and information. This paper argues for the critical need to dynamically enhance the relevance between specific instances and prompts. Thus, we introduce a stance detection model underpinned by a gated multilayer perceptron (gMLP) and a prompt learning strategy, which is tailored for zero-shot stance detection scenarios. Specifically, the gMLP is utilized to capture semantic features of instances, coupled with a control gate mechanism to modulate the influence of the gate on prompt tokens based on the semantic context of each instance, thereby dynamically reinforcing the instance-prompt connection. Moreover, we integrate contrastive learning to empower the model with more discriminative feature representations. Experimental evaluations on the VAST and SEM16 benchmark datasets substantiate our method's effectiveness, yielding a 1.3% improvement over the JointCL model on the VAST dataset.

Opinion Dynamics on Directed Complex Networks

We propose and analyze a mathematical model for the evolution of opinions on directed complex networks. Our model generalizes the popular DeGroot and Friedkin-Johnsen models by allowing vertices to have attributes that may influence the opinion dynamics. We start by establishing sufficient conditions for the existence of a stationary opinion distribution on any fixed graph, and then provide an increasingly detailed characterization of its behavior by considering a sequence of directed random graphs having a local weak limit. Our most explicit results are obtained for graph sequences whose local weak limit is a marked Galton-Watson tree, in which case our model can be used to explain a variety of phenomena, for example, conditions under which consensus can be achieved, mechanisms in which opinions can become polarized, and the effect of disruptive stubborn agents on the formation of opinions. Funding: This work was supported by the National Science Foundation [Grants NSF-DMS-1929298 and CMMI-2243261].

Transient behavior of gossip opinion dynamics with community structure

We study transient behavior of gossip opinion dynamics, in which agents randomly interact pairwise over a weighted graph with two communities. Edges within a community have identical weights different from edge weights between communities. We first derive an upper bound for the second moment of agent opinions. Using this result, we obtain upper bounds for probability that a large proportion of agents have opinions close to average opinions. The results imply a phase transition of transient behavior of the process: When edge weights within communities are larger than those between communities and those between regular and stubborn agents, most agents in the same community hold opinions close to the average opinion of that community with large probability, at an early stage of the process. However, if the difference between intra- and inter-community weights is small, most of the agents instead hold opinions close to everyone’s average opinion at the early stage. In contrast, when the influence of stubborn agents is large, agent opinions settle quickly to steady state. We then conduct numerical experiments to validate the theoretical results. Different from traditional asymptotic analysis in most opinion dynamics literature, the paper characterizes the influence of stubborn agents and community structure on the initial phase of the opinion evolution.

The influence of emotional tendency on the dissemination and evolution of opinions in two-layer social networks

To investigate the influence of emotional tendency on the dissemination and evolution of opinions, in this paper, we first propose a novel algorithm to construct a two-layer social networks, who is composed of a strong-tie subnet and a weak-tie subnet. Considering the weights on the strong-tie and weak-tie networks and the subjective emotional tendencies of individuals, the HK model with emotional tendency (i.e. E-HK model), the weight HK model (i.e. WHK model), and the weight HK model with emotional tendency (i.e. E-WHK model) are put forward. Finally, according to the algorithm and the E-WHK model newly presented, we use the seventh census data to construct a social network, and explore individuals' opinion evolution, as well as the influence of three factors, including the level of subjective emotional tendency, the trust radius, and the proportions of individuals with different emotional tendencies. Abundant simulations demonstrate that the E-WHK model can explain the dissemination and evolution of opinions very well. In addition, the greater the level of emotional tendency, the faster the convergence of individuals’ opinions. Increasing the trust radius will make completely positive and negative opinions decrease. Enhancing the proportion of optimists/pessimists, most individuals' ultimate opinions tend to be completely positive/negative. The results of this paper provide theoretical methods and analytical tools for managers in guiding public sentiment during public events.

THE SUPERVISING RIGHTS OF THE MINISTRY OF JUSTICE OVER THE PROFESSIONAL SELF-GOVERNMENT OF ATTORNEYS-AT-LAW AS AN EXAMPLE OF PARTICULAR FORMS OF PUBLIC MANAGEMENT

Public management is a discipline of science derived from management, legal, and political sciences. It addresses activities that ensure effective management of organized action of people aimed at creating public value and realizing the public interest in the process of rendering public services. One form of public management is ministry supervision over the professional self-government of attorneys-at-law, ensuring legality and serving the public good. One essential means of supervision in this area is opposition by the Ministry of Justice to a resolution of the council of a district chamber of attorneys-at-law on registration on the list of attorneys. Using the methods of dogmatic analysis and a jurisprudence survey, the author presents the ways of interpreting the legal provisions concerning the subject-matter opposition and the evolution of opinions formulated in this respect over the last few dozen years. The article aims to show how a strict interpretation of the concrete rights of a supervising organ influences the restrictions on supervising interference in the sphere reserved for the competencies of professional self-government – an entity of its essence autonomous and independent.

An evolutionary approach to extreme individual impact opinions based on time sunk costs

Large-scale studies indicate that the distinct approach to opinion fusion employed by extreme agents exerts a more potent influence on overall opinion evolution when compared to regular agents. The presence of extreme agents within the network tends to undermine the development of opinion neutrality, which is harmful to the guidance of online public opinion. Notably, prior research often overlooks the existence of opinion extreme agents in social networks. However, existing researches seldom consider the time sunk cost in the evolution of opinions. Building upon this foundation, we introduce a temporal dimension to the opinion evolution, integrating the time sunk cost with the opinion evolution process. Furthermore, we devise an agent partitioning method that categorizes agents into four states based on their opinion values: watch state, subjective state, firm state, and extreme state, with extreme state agents generally expressing radical opinions. We constructed an agent network based on the phenomenon of time sunk costs and proposed a model for the evolution of extreme opinions in this network. Our study found that the information sharing among extreme agents significantly influences the extremization of opinions in various networks. After restricting the exchange of opinions on extreme agents, the number of extreme agents in the network decreased by 40% to 50% compared to the initial situation. Additionally, we also discovered that imposing restrictions on extreme agents in the early stages can help increase the possibility of network opinions moving towards neutral positions. When restriction of extreme agents(REA) was performed at the beginning of the experiment compared to REA in the midway of the experiment, the final number of extreme state agents decreased by 15.57%. The results show that extreme agents have a great influence on the spread and evolution of extreme opinions on platforms.

Modeling the opinion dynamics of superstars in the film industry

One of the most challenging questions in the film industry is to rank superstars, which ultimately affects some performance indicators like movie success. In this work, we address this question by means of opinion dynamics models, where the evolution of opinions in a population is analyzed. We apply a model of this kind to study the evolution of opinions about a set of well-known movie superstars in a real-world population. Also, we use real-world data from a specialized cinema website to model mass communication processes (representing film releases and their related news and marketing campaigns), and to measure the performance of our model. Our results show that the proposed model is able to accurately represent this complex system, where the opinion dynamics of superstars are mostly driven by emotional mechanisms, and reveal that film releases and their corresponding marketing campaigns only have a short term effect on those opinions. To the best of our knowledge, this is the first work that applies opinion dynamics models to the study of opinions about superstars in the film industry.

Expressive and Flexible Simulation of Information Spread Strategies in Social Networks Using Planning

In the digital age, understanding the dynamics of information spread and opinion formation within networks is paramount. This research introduces an innovative framework that combines the principles of opinion dynamics with the strategic capabilities of Automated Planning. We have developed, to the best of our knowledge, the first-ever numeric PDDL tailored for opinion dynamics. Our tool empowers users to visualize intricate networks, simulate the evolution of opinions, and strategically influence that evolution to achieve specific outcomes. By harnessing Automated Planning techniques, our framework offers a nuanced approach to devise sequences of actions tailored to transition a network from its current opinion landscape to a desired state. This holistic approach provides insights into the intricate interplay of individual nodes within a network and paves the way for targeted interventions. Furthermore, the tool facilitates human-AI collaboration, enabling users to not only understand information spread but also devise practical strategies to mitigate potential harmful outcomes arising from it. Demo Video link - https://tinyurl.com/3k7bp99h

Trust exploration- and leadership incubation- based opinion dynamics model for social network group decision-making: A quantum theory perspective

Social network (SN) holds significant sway over the consciousness and behavior of involved individuals, serving as an evolutionary medium for opinion dynamics models. Consensus is a fundamental concept in group decision-making (GDM). How to effectively explore the opinion evolution during the consensus-reaching process through SN is of paramount significance for decision-making science. Therefore, a dual-mechanism containing trust exploration and leadership incubation is developed for modeling opinion dynamics, creating a favorable condition for achieving consensus. First, a novel mechanism for analyzing the completeness of SN is proposed, encompassing a trust propagation process that considers trust discount and stability, as well as a trust aggregation method grounded by quantum theory. Second, a trust screening rule is discussed to retain the valid indirect trust relationships (TRs), and then a leadership incubation mechanism is developed to promote the effective achievement of consensus opinions in group decision-making. Finally, a numerical study is presented to elucidate the superiority and rationality of the proposed methods, and some simulation experiments and comparative analyses demonstrating the effectiveness and advantages of which are covered.

New consensus reaching process with minimum adjustment and feedback mechanism for large-scale group decision making problems under social trust networks

Large-scale group decision making problems in social networks environment have become an important research topic in decision making science. In this paper, we propose a new consensus reaching process with minimum adjustment and feedback mechanism considering public opinions for large-scale group decision making problems under social trust networks. The trust propagation operator based on Archimedean t-norm is used to calculate incomplete trust values first. The fast unfolding algorithm is used to divide experts into subgroups. The opinion evolution model is presented to calculate experts’ weights based on dynamic trust values and expert influence. Public opinions are used to determine consensus thresholds of attributes. A dual-path adjustment model is developed for group consensus in social network. Divide experts’ evaluation values into three different types: ‘conflict behavior’, ‘tolerant behavior’, ‘trust behavior’. Minimum cost adjustment models have been developed considering adjustment directions for the ‘conflict behavior’ and interactive path is activated for the ‘tolerant behavior’. The drugs selection from E-commerce platform for therapy of influenza at home is used to illustrate the new method and comparisons have also been conducted.

On incomplete matrix information completion methods and opinion evolution: Matrix factorization towards adjacency preferences

Completeness and consistency are widely acknowledged as indispensable prerequisites for applying fuzzy preference relations in resolving real-world problems. Interactions between decision-makers in the field of group decision-making can have a profound effect on preference changes, especially in situations of incomplete information. However, preference matrices generated using existing models often suffer from low consistency during opinion evolution, which further increases the risk of wrong decisions. To cope with these problems, this paper proposes an opinion evolution method under incomplete information. The method consists of two main components: (1) A preference prediction model based on matrix factorization algorithm and social trust networks under incomplete information, and (2) A model for decision-makers’ preference evolution under dynamic trust scenarios. In Part (1), to simplify the preference collection method and reduce the occurrence of inconsistent information, this paper presents the concept of a consistent adjacency fuzzy preference matrix. To advance the utilization of machine learning algorithms for consistent preference relations, this paper employs the matrix factorization method with stochastic gradient descent to predict missing preferences in adjacency fuzzy preference matrix. In Part (2), to address the shortcomings in past opinion evolution models, this paper simulates the change of decision-makers’ opinions in different stages of interactions under variable trust weights by using alternative ranking similarity and trust thresholds. To illustrate the practical application of the proposed model, this article uses the enterprise supplier selection problem as a case study. The model’s effectiveness and feasibility are then validated through a comparative analysis with existing methods.

Evaluating public opinions: informing public health policy adaptations in China amid the COVID-19 pandemic

Public concern regarding safety policies serious consequences is anticipated to persist over an extended duration. A study examining a case of rapid public health policy adaptation in China during the COVID-19 epidemic was conducted by gathering public opinion data from major social media platforms. A systematic approach to comprehend public opinion was developed. Five fundamental elements and four dimensions were delineated. An indicator system was established utilizing the K-means text clustering model. Public prediction, expectation, and their evolution underlying public concern were elucidated employing TF–IDF text mining models. The HMM elucidated the way public opinion influences policy adjustments. The findings underscore that public concern regarding enduring events undergoes temporal shifts, mirroring the evolution of public opinion towards policy. Public opinion aroused by both the original event and derived events collaboratively influence policy adjustments. In China, public opinion serves as a mechanism for policy feedback and oversight; notably, negative public sentiment plays a pivotal role in expediting policy transitions. These findings aid in refining policies to mitigate emergencies through a feedback loop, thereby averting the emergence of safety risks such as social unrest prompted by public opinion.

Modeling Group Opinion Evolution on Online Social Networks: A Gravitational Field Perspective

The research on group behavior is effective for establishing a good network environment since people in social networks tend to form groups spontaneously. Most studies on group behavior on online social networks assume that all individuals are reduced to one cluster, ignoring the existence of potential clusters and their importance in group opinion dynamics. This article introduces a novel group-gravitational field (GGF) model to investigate the opinion evolution based on group behavior by the following aspects: 1) the GGF model reduces a cluster in the social network into a charge and the whole network into a gravitational field; 2) the GGF model calculates the initial influence of a cluster according to the topology information and further constructs a gravity matrix of the network based on the Coulomb law; and 3) opinion-leader clusters exert the internal field force on common opinion clusters inside the gravitational field. The GGF model simulates the evolution of opinions among clusters in a network and studies the law of group behavior according to the influence between clusters based on Coulomb’s law. Experiments on real social networks verify that the GGF model enhances the speed of opinion evolution significantly. The simulation experiments indicate that the existence of clusters promotes the rapid convergence of opinions, a gathering of followers influences information dissemination in social networks, and the GGF model fits the reality better. This article provides a new approach to network supervision and control.

Opinion and Action Interactive Evolution Based on Social Network Leadership and Opinion Estimation of Action

Opinion and Action Interactive Evolution Based on Social Network Leadership and Opinion Estimation of Action

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.

Public Opinion Evolution in Cyberspace: A Case Analysis of Pelosi’s Visit to Taiwan

The dynamics of public opinion on social media affects people’s feeling and minds about international affairs and leads to the reconstruction of societal states for international conflicts. In this article, we analyze the topics’ evolution on social media during the Pelosi visit. Such kind of analysis should help the related departments sense and beware the situation effectively and efficiently, and may provide technical supports for proper policy making and responses. To facilitate this purpose, a new method is proposed and an abbreviated large-graph clustering (ALGC) algorithm has been designed to generate documents and topic representation for alleviating the overhead of high computational complexity of large graphs by reducing the dimensionality of the attention matrix and adjacency matrix. The evolution pattern of topics is also analyzed in and between different time periods. Experiment results show that the proposed method performs well, achieving a high clustering accuracy with lower computational cost. The dataset used in this article is also released for public analysis.

Queer temporalities in TV series Veneno: breaking down the spiral of silence through trans visibility

This study explores the implications of media visibility in the evolution of public opinion about trans people in the Spanish context. We focus on the socio-cultural impact of the TV series ‘Veneno’ (2020, Atresmedia) in relation to the breaking down of the spiral of silence (Noelle-Neumann, 1993) on trans issues. Based on Esteban Muñoz's (2020) theoretical approach to queer temporalities, we conducted a 'close reading' analysis (Brummett 2018) of the diachronic narrative of the television series Veneno (2020). Through a linear observation of the past, present and future of media representation and its relationship with the real-life situation of trans people, an evolution is detected from ostracism and fetishization in the past, to a future shift towards greater normalization and livable life forms. But the results also reveal a spiral of meanings that interconnect with different temporalities and contexts, reflecting the non-linear nature of queer temporalities. Additionally, the paper discusses how this series, due to its media impact, has contributed to socio-political public debate on the need to address the rights of trans people in modern-day Spanish society. Finally, its impact not only relies on improving trans visibility by portraying trans narratives, but we also discuss how the series is playing an important role in queering the media industry itself, ensuring trans people to work both in front and behind the cameras.

A neural probabilistic bounded confidence model for opinion dynamics on social networks

Opinion dynamics (OD) models simulate the evolution of personal opinions using computer science, social dynamics, physics, and complexity science tools on social networks. However, current opinion dynamics models mainly utilize agent-based models to simulate user interactions but still need to leverage large-scale data fully. Social networks typically involve a vast number of users and information, making it difficult for models to capture the complex interactions in real social networks. Additionally, many models overlook the specific mechanisms of user interactions, such as how network individuals influence and interact with one another, resulting in inaccuracies in modeling individual opinion evolution. This paper proposes a novel approach that integrates the probabilistic bounded confidence model (PBC) with deep learning, i.e., Neural Probabilistic Bounded Confidence (NPBC), to tackle these challenges. The NPBC model adopts an attention network, a long short-term memory (LSTM) network, and a feedforward neural network to approximate the evolution of individual opinions within networks. It satisfies the data approximation and the users’ opinion update rule that conforms to the PBC model. This model enhances the understanding of opinion dynamics, helping to develop effective strategies for opinion guidance in social networks. Finally, the proposed NPBC model is evaluated on three synthetic and Twitter datasets. The results demonstrate that the proposed NPBC model performs better than the six baseline methods in predicting user opinions. Compared to baseline models, the model’s accuracy (ACC) and comprehensive evaluation (F1) scores improve by up to 18% and 7%, respectively.

Directed propaganda in the majority-rule model

Advertisement and propaganda have changed continuously in the past decades, mainly due to the people’s interactions at online platforms and social networks, and operate nowadays reaching a highly specific online audience instead targeting the masses. The impacts of this new media effect, oriented directly for a specific audience, are investigated on this study, in which we focus on the opinion evolution of agents in the majority-rule model, considering the presence of directed propaganda. We introduce p as the probability of a “positive” external propaganda and q as the probability to the agents follow the external propaganda. Our results show that the usual majority-rule model stationary state is reached, with a full consensus, only for two cases, namely when the external propaganda is absent or when the media favors only one of the two opinions. However, even for a small influence of external propaganda, the final state is reached with a majority opinion dominating the population. For the case in which the propaganda influence is strong enough among the agents, we show that the consensus cannot be reached at all, and we observe the polarization of opinions. In addition, we show through analytical and numerical results that the system undergoes an order–disorder phase transition that occurs at [Formula: see text] for the case [Formula: see text].