Public Opinion Polarization Research Articles

社交媒体时代舆论极化背景下大学生媒介素养教育路径探究

In the age of social media, with the speed of in-depth media integration accelerating, social media is increasingly becoming an extension of college students’ lives, and media literacy education is becoming more and more important. However, the risk of polarization of public opinion in the social media era is a new situation and a new challenge for college students’ media literacy education at present. The phenomenon of public opinion polarization in social media has a negative impact on college students with its prominent irrational emotions, diversified views and frequent occurrence of cyber violence, which also makes college students face many difficulties in media literacy education. In order to address the potential seams of public opinion polarization, college students’ media education should start from the following points: establishing a scientific teaching system, improving college students’ ability to participate in and apply the media, and organizing diversified self-education for college students.

Digital news ecology and polarization in the context of deep learning: empowerment, entrenchment and reconciling failure

Abstract This paper aims to discuss the causes of public opinion polarization in digital news ecology and then analyze group polarization’s specific manifestations and carriers. Secondly, it introduces word embedding technology to analyze digital news text, utilizes a multi-head self-attention mechanism to construct a new classification model, and realizes collaborative filtering recommendations of digital news based on users’ interests and news hotspots. Finally, social media is taken as an example to analyze the factors that cause polarization in digital news ecology. The results show that the digital news ecosystem can empower the development of the polarization phenomenon, deeply involve users, and group users by solidifying their identity construction, social endorsement and other directions. It can also provide a space for the polarization phenomenon to be reconciled, but due to the emotional loss of control and the decline of publicity leading to the failure of reconciliation, the similarity of sadness emotion reconciliation is 36.94%. It is suggested that the digital news ecology provides technical support for developing polarization phenomena, enabling them to thrive in digital news.

An intelligent power grid emergency allocation technology considering secondary disaster and public opinion under typhoon disaster

Typhoon disaster may not only cause power outage physically, but also breed negative sentiments in public opinion and affect social stability. Therefore, an intelligent power grid allocation technology considering secondary disaster and public opinion under typhoon disaster is proposed. Firstly, an Extra-Tree (ET) method is used to predict the power outage in time sequence (e.g., landing period, 6 h later, and 12 h later) after typhoon lands. Secondly, a typhoon secondary disaster potential assessment model is established based on disaster intensity and geographical environment. Since power outages may also cause public opinion polarization, a public opinion analysis model is proposed to mine Micro-blog (similar to Twitter) tweet information and analyze the demand for power restoration. Then, according to the results of secondary disaster potential assessment and public opinion analysis, the allocation strategy optimization is described as a Mixed Integer Nonlinear Programming (MINLP) problem. It is solved by the Stud Genetic Algorithm (Stud GA) and A-star path optimization algorithm. Finally, Yangjiang city in China under Typhoon “Chaba” (2022) is selected as the study area to verify the effectiveness and feasibility. It shows the proposed method can respond to public electricity demand in time and eliminate public negative sentiments, while the repair process is not disturbed by secondary disasters.

Polarizing Political Polls: How Visualization Design Choices Can Shape Public Opinion and Increase Political Polarization.

While we typically focus on data visualization as a tool for facilitating cognitive tasks (e.g. learning facts, making decisions), we know relatively little about their second-order impacts on our opinions, attitudes, and values. For example, could design or framing choices interact with viewers' social cognitive biases in ways that promote political polarization? When reporting on U.S. attitudes toward public policies, it is popular to highlight the gap between Democrats and Republicans (e.g. with blue vs red connected dot plots). But these charts may encourage social-normative conformity, influencing viewers' attitudes to match the divided opinions shown in the visualization. We conducted three experiments examining visualization framing in the context of social conformity and polarization. Crowdworkers viewed charts showing simulated polling results for public policy proposals. We varied framing (aggregating data as non-partisan "All US Adults," or partisan "Democrat" / "Republican") and the visualized groups' support levels. Participants then reported their own support for each policy. We found that participants' attitudes biased significantly toward the group attitudes shown in the stimuli and this can increase inter-party attitude divergence. These results demonstrate that data visualizations can induce social conformity and accelerate political polarization. Choosing to visualize partisan divisions can divide us further.

Research on phenomenon of public opinion polarization on Weibo from the perspective of situational collapse

Research on phenomenon of public opinion polarization on Weibo from the perspective of situational collapse

A Novel Public Opinion Polarization Model Based on BA Network

At present, the polarization of online public opinion is becoming more frequent, and individuals actively participate in attitude interactions more and more frequently. Thus, online views have become the dominant force in current public opinion. However, the rapid fermentation of polarized public opinion makes it very easy for actual topic views to go to extremes. Significantly, negative information seriously affects the healthy development of the social opinion ecology. Therefore, it is beneficial to maintain national credibility, social peace, and stability by exploring the communication structure of online public opinions, analyzing the logical model of extreme public attitudes, and guiding the communication of public opinions in a timely and reasonable manner. Starting from the J–A model and BA network, this paper explores the specific attributes of individuals and opinion network nodes. By incorporating parameters such as individual conformity and the strength of individual online relationships, we established a model of online group attitude polarization, then conducted simulation experiments on the phenomenon of online opinion polarization. Through simulations, we found that individual conformity and the difference in environmental attitude greatly influence the direction of opinion polarization events. In addition, crowd mentality makes individuals spontaneously choose the side of a particular, extreme view, which makes it easier for polarization to form and reach its peak.

Modelling network public opinion polarization based on SIR model considering dynamic network structure

Under the dispute between China and the United States, the international field of public opinion is dominated by increasing tensions, and the network rumors triggered by the COVID-19 epidemic are intensifying. In view of the above-mentioned context, this paper focuses on the development and the evolution process of public opinions. Since the evolution of public opinion is often accompanied by the spread and diffusion of information, this paper combines the process of information diffusion with the development process of polarization behavior, and brings in the dynamic network and the timeliness factor of public opinion dissemination, so as to better explore the polarization process of public opinion under the dynamic network. Then, this paper focuses on the analysis of the parameters of the model and through the dynamic adjustment of parameters, finding out the main factors that affect the trend and development of network public opinion. In addition, this paper introduces an actual case, and takes the actual case data as the support to demonstrate the reliability and practical application value of the model. Finally, based on the simulation results and analysis of actual cases, this paper puts forward the corresponding preventive measures to alleviate the polarization behavior of the group.

Public opinion polarization on immigration in Italy: the role of traditional and digital news media practices

ABSTRACT Public opinion polarization on immigration can hinder social cohesion, integration policies and economic growth. Political campaigns and partisan news media systems have long been investigated in terms of potential drivers of mass polarization, often through a focus on one news media. Utilizing survey data collected by the Pew Research Center, the purpose of the present study is twofold: first, to provide insight into the state of polarization of the Italian public on immigrants-related issues during the campaign for 2018 General Elections, that is, in a context of increasing struggle on immigration between political elites and partisan news media; second, to analyze whether and how the frequency of use of traditional and digital news media and/or the political similarity of (online and offline) information networks have affected the probability of Italian citizens having polarized opinions on immigration. This study shows no significant divergences in the Italian public opinion toward extreme positions, but provide evidence of one-side extremism and alignment along two irreconcilable views. Furthermore, findings indicate deep differences in the effects of traditional and digital news media practices on individuals’ extreme and aligned positions on immigration. When various news media are considered, the effect of television is always the more robust.

Combining Public Opinion Dissemination with Polarization Process Considering Individual Heterogeneity.

The wide dissemination of false information and the frequent occurrence of extreme speeches on online social platforms have become increasingly prominent, which impact on the harmony and stability of society. In order to solve the problems in the dissemination and polarization of public opinion over online social platforms, it is necessary to conduct in-depth research on the formation mechanism of the dissemination and polarization of public opinion. This article appends individual communicating willingness and forgetting effects to the Susceptible-Exposed-Infected-Recovered (SEIR) model to describe individual state transitions; secondly, it introduces three heterogeneous factors describing the characteristics of individual differences in the Jager-Amblard (J-A) model, namely: Individual conformity, individual conservative degree, and inter-individual relationship strength in order to reflect the different roles of individual heterogeneity in the opinions interaction; thirdly, it integrates the improved SEIR model and J-A model to construct the SEIR-JA model to study the formation mechanism of public opinion dissemination and polarization. Transmission parameters and polarization parameters are simulated and analyzed. Finally, a public opinion event from the pricing of China’s self-developed COVID-19 vaccine are used, and related Weibo comment data about this event are also collected so as to verify the rationality and effectiveness of the proposed model.

Modeling Multidimensional Public Opinion Polarization Process under the Context of Derived Topics.

With the development of Internet technology, the speed of information dissemination and accelerated updates result in frequent discussion of topics and expressions of public opinion. In general, multi-dimensional discussion topics related to the same event are often generated in the network, and the phenomenon of multi-dimensional public opinion polarization is formed under the mutual influence of groups. This paper targets the phenomenon of multi-dimensional public opinion polarization under topic-derived situations as the research object. Firstly, this paper identifies the factors influencing multi-dimensional public opinion polarization, including the mutual influence of different topic dimensions and the interaction of viewpoints within the same topic. Secondly, the topic correlation coefficient is introduced to describe the correlation among topics in different dimensions, and the individual topic support degree is used to measure the influence of topics in different dimensions and that of information from external intervention on individual attitudes. Thirdly, a multi-dimensional public opinion polarization model is constructed by further integrating multi-dimensional attitude interaction rules. Finally, the influence of individual participation, topic status, topic correlation coefficient and external intervention information on the multi-dimensional public opinion polarization process is analyzed through simulation experiments. The simulation results show that: (1) when there is a negative correlation between multi-dimensional topics, as the number of participants on different dimensional topics becomes more consistent, the conflict between multi-dimensional topics will weaken the polarization effect of overall public opinion. However, the effect of public opinion polarization will be enhanced alongwith the enhancement in the confidence of individual opinions. (2) The intervention of external intervention information in different dimensions at different times will further form a multi-dimensional and multi-stage public opinion polarization, and when the multi-dimensional topics are negatively correlated, the intervention of external intervention information will have a stronger impact on the multi-dimensional and multi-stage public opinion polarization process. Finally, the rationality and validity of the proposed model are verified by a real case.

Modeling Public Opinion Polarization in Group Behavior by Integrating SIRS-Based Information Diffusion Process

Currently, China is in the period of social transformation. Such transformation continuously results in high group polarization behaviors, which attracts many attentions. In order to explore the evolutionary mechanism and formation process of group polarization behavior, this paper proposes a group polarization model which is integrated into the Susceptible-Infected-Recovered-Susceptible (SIRS) epidemic model. In this paper, firstly, the SIRS epidemic model and the factors of relationship strength are introduced based on the J-A model (proposed by Jager and Amblard) to enhance the information transmission and interaction among individuals. In addition, the BA network (proposed by Barabasi and Albert) model is used as the agent adjacency model due to its closeness to the real social network structure. After that, the Monte Carlo method is applied to conduct experimental simulation. Subsequently, this paper analyzes the simulation results in threefold: (1) comparison of polarization processes with and without integration of the SIRS epidemic model; (2) adjusting the immune recovery parameter γ and the relationship strength z to explore the role of these two parameters in the polarization process; and (3) comparing the polarization effects of different network structures. Through the experiments, we find that BA network is more polarized than small-world network in the same scale. Finally, corresponding measures are proposed to prevent and mitigate the occurrence of group polarization.

Negativity makes us polarized: a longitudinal study of media tone and opinion polarization in Hong Kong

ABSTRACT Public opinion polarization has recently become a prominent social problem in many societies, but whether negative media tone is a possible culprit for opinion polarization remains unclear. Using opinion-polling data on approval ratings of Chief Executive from the Public Opinion Program at the University of Hong Kong, we examined the longitudinal relationship between media tone and opinion polarization in Hong Kong. Opinion polarization is conceptualized as the dispersion and the bimodality of opinion distribution, and is operationalized by opinion distribution variance, kurtosis and a composite score. Positive and negative media tones are coded using computerized text analysis programs. Time series analysis suggests that negative news media tone Granger causes opinion polarization but the reverse causal relationship does not hold. In addition, positive news media tone is not related to opinion polarization.

Modeling and simulation analysis of public opinion polarization in a dynamic network environment

SummaryAt present, there are many group incidents on the Internet, which has caused a high level of public opinion. Hot issues on the Internet can often trigger intense discussions among netizens and finally evolve toward bipolarization or multipolarization. This article focuses on the phenomenon of public opinion polarization under the dynamic network. First, the single‐dimensional evaluation system for the JA model was expanded to multidimensional and analyzes the main factors that cause the polarization of public opinion of network groups from multiple dimensions of hot events. Second, the strength of the relationship between individuals and its dynamic changes were taken into account in the model. The strength of the relationship between individuals and its changes will affect the interaction of opinions, and thus affect the degree and speed of group polarization. Third, combining the changing process of the strength of relationship, the dynamic connection mechanism of network nodes was set to make the network structure more consistent with the social network in real life. Finally, we verify the reliability and scientificity of the model by combining practical cases.

Public Opinion Polarization by Individual Revenue from the Social Preference Theory.

Social conflicts occur frequently during the social transition period and the polarization of public opinion happens occasionally. By introducing the social preference theory, the target of this paper is to reveal the micro-interaction mechanism of public opinion polarization. Firstly, we divide the social preferences of Internet users (network nodes) into three categories: egoistic, altruistic, and fair preferences, and adopt the revenue function to define the benefits obtained by individuals with different preferences among their interaction process so as to analyze their decision-making behaviors driven by the revenue. Secondly, the revenue function is used to judge the exit rules of nodes in a network, and then a dynamic network of spreading public opinion with the node (individual) exit mechanism is built based on a BA scale-free network. Subsequently, the influences of different social preferences, as well as individual revenue on the effect of public opinion polarization, are analyzed through simulation experiments. The simulation results show that (1) Different social preferences demonstrate different influences on the evolution of public opinions, (2) Individuals tend to interact with ones with different preferences, (3) The network with a single preference or a high aggregation is more likely to form public opinion polarization. Finally, the practicability and effectiveness of the proposed model are verified by a real case.

Role of voting intention in public opinion polarization.

We introduce and study a simple model for the dynamics of voting intention in a population of agents that have to choose between two candidates. The level of indecision of a given agent is modeled by its propensity to vote for one of the two alternatives, represented by a variable p∈[0,1]. When an agent i interacts with another agent j with propensity p_{j}, then i either increases its propensity p_{i} by h with probability P_{ij}=ωp_{i}+(1-ω)p_{j}, or decreases p_{i} by h with probability 1-P_{ij}, where h is a fixed step. We assume that the interactions form a complete graph, where each agent can interact with any other agent. We analyze the system by a rate equation approach and contrast the results with Monte Carlo simulations. We find that the dynamics of propensities depends on the weight ω that an agent assigns to its own propensity. When all the weight is assigned to the interacting partner (ω=0), agents' propensities are quickly driven to one of the extreme values p=0 or p=1, until an extremist absorbing consensus is achieved. However, for ω>0 the system first reaches a quasistationary state of symmetric polarization where the distribution of propensities has the shape of an inverted Gaussian with a minimum at the center p=1/2 and two maxima at the extreme values p=0,1, until the symmetry is broken and the system is driven to an extremist consensus. A linear stability analysis shows that the lifetime of the polarized state, estimated by the mean consensus time τ, diverges as τ∼(1-ω)^{-2}lnN when ω approaches 1, where N is the system size. Finally, a continuous approximation allows us to derive a transport equation whose convection term is compatible with a drift of particles from the center toward the extremes.

When Polls Disagree: How Competitive Results and Methodological Quality Shape Partisan Perceptions of Polls and Electoral Predictions

AbstractThis study leverages a survey experiment in the lead up to the 2016 U.S. presidential election to evaluate how partisan biases, poll results, and their methodological quality interact to shape people’s assessments of polling accuracy and electoral expectations. In a nationally representative sample, we find that individuals disproportionately find polls more credible when their preferred candidate is leading. Partisan biases are mitigated when the polls themselves vary in objective indicators of quality: while more educated respondents are more likely to identify high-quality polls accurately, low education respondents’ bias was reduced when they encountered polls with varying methodological quality. Finally, these moderators influence respondents’ electoral expectations as well. We discuss the implications for journalistic coverage of polls, public opinion, and political polarization.

Emotional foundations of the public climate change divide

The worldwide rise of climate skeptical political leaders endangers sorely needed political efforts to mitigate climate change. In addition, climate skepticism expressed by the political elites may spread to the electorate, thus ultimately affecting mitigation actions at the population level. It is crucial to better understand the psychological mechanisms underlying elite influences on public opinion formation and polarization. Here we show how affective processes contribute to these top-down influences using longitudinal data in the context of the 2016 U.S. presidential election. Findings revealed a general decrease in climate change beliefs immediately after the presidential election (November 2016). We moreover found an increase in positive emotions and a decrease in negative emotions toward the Republican Party after the election of President Trump. Most importantly, the positive emotional shift towards the Republican Party mediated the decrease in climate change beliefs: Individuals with pronounced increases in positive emotions toward the Republican Party most strongly reduced their belief in climate change after the presidential election. The effect was intensified among Republican partisans, pointing towards a mechanism underlying political polarization. Using data based on a major real-world political event, our findings illustrate how partisans update their beliefs by referring to the positions of relevant political authorities. We moreover demonstrate how emotions drive top-down influences of political leaders on partisans’ opinions and beliefs. Finally, our findings reveal how intensified emotions can contribute to the aggravation of the public climate change divide.

Modeling of the Public Opinion Polarization Process with the Considerations of Individual Heterogeneity and Dynamic Conformity

Nowadays, hot issues are likely become bipolar or multipolar after heated discussion on the Internet. This article is focused on the study of the polarization phenomenon and establishes a public opinion polarization model with the considerations of individual heterogeneity and dynamic conformity. At first, this article introduces the dynamic changing function of an individual’s conformity tendency to other’s attitudes in the interaction process. It further defines the influential weight between different interactive individuals, and expands the interactive individual from complete homogeneity to initial attitude heterogeneity, and finally, conformity heterogeneity. Then, through simulation experiments, we find that the degree of changing in individual attitude is limited. That is, it is difficult for the individuals who have one directional attitude at the initial time to change into another opposite attitude through interaction. In addition, individuals with low conformity within a certain threshold are more likely to form polarization. Finally, the rationality and effectiveness of the proposed model are verified by the typical case “Mimeng Event”.

Sentiment analysis of political communication: combining a dictionary approach with crowdcoding

Sentiment is important in studies of news values, public opinion, negative campaigning or political polarization and an explosive expansion of digital textual data and fast progress in automated text analysis provide vast opportunities for innovative social science research. Unfortunately, tools currently available for automated sentiment analysis are mostly restricted to English texts and require considerable contextual adaption to produce valid results. We present a procedure for collecting fine-grained sentiment scores through crowdcoding to build a negative sentiment dictionary in a language and for a domain of choice. The dictionary enables the analysis of large text corpora that resource-intensive hand-coding struggles to cope with. We calculate the tonality of sentences from dictionary words and we validate these estimates with results from manual coding. The results show that the crowdbased dictionary provides efficient and valid measurement of sentiment. Empirical examples illustrate its use by analyzing the tonality of party statements and media reports.

Political Depolarization in German Public Opinion, 1980–2010

Little is known about political polarization in German public opinion. This article offers an issue-based perspective and explores trends of opinion polarization in Germany. Public opinion polarization is conceptualized and measured as alignment of attitudes. Data from the German General Social Survey (1980 to 2010) comprise attitudes towards manifold issues, which are classified into several dimensions. This study estimates multilevel models that reveal general and issue- as well as dimension-specific levels and trends in attitude alignment for both the whole German population and sub-groups. It finds that public opinion polarization has decreased over the last three decades in Germany. In particular, highly educated and more politically interested people have become less polarized over time. However, polarization seems to have increased in attitudes regarding gender issues. These findings provide interesting contrasts to existing research on the American public.