Opinion Polarization Research Articles

Kinetic Models for Epidemic Dynamics in the Presence of Opinion Polarization

Understanding the impact of collective social phenomena in epidemic dynamics is a crucial task to effectively contain the disease spread. In this work, we build a mathematical description for assessing the interplay between opinion polarization and the evolution of a disease. The proposed kinetic approach describes the evolution of aggregate quantities characterizing the agents belonging to epidemiologically relevant states and will show that the spread of the disease is closely related to consensus dynamics distribution in which opinion polarization may emerge. In the present modelling framework, microscopic consensus formation dynamics can be linked to macroscopic epidemic trends to trigger the collective adherence to protective measures. We conduct numerical investigations which confirm the ability of the model to describe different phenomena related to the spread of an epidemic.

Weaving It In: How Political Radio Reacts to Events

Abstract How do ideologically slanted media outlets react to politically relevant events? Previous research suggests that partisan media trumpet ideologically congenial events, such as opposing-party scandals, while ignoring bad news for their own side. Looking at reactions to newsworthy events on political radio—an often-partisan medium that reaches more Americans than Twitter—I find a different pattern. Based on recordings of hundreds of shows totaling two million broadcast hours, I demonstrate that regardless of their ideological leanings, political shows respond to events by dramatically increasing the attention they give to related policy issues. At the same time, liberal and conservative shows continue to frame those issues in very different ways. Instead of ignoring inconvenient events, partisan media “weave them in,” interpreting them in ways consistent with their ideological leanings. These media dynamics imply that nationally significant events can cause opinion polarization rather than convergence—becoming a divisive rather than a shared experience.

Harassment in Universities through the Eyes of Students

The article is devoted to the study of the attitude of university students to the problem of harassment in universities, including the identification of their existing stereotypes. Within the framework of this research, domestic and foreign studies of harassment in the academic environment were analyzed and a questionnaire was developed to identify the ideas of university students about the essence and practices of harassment. In total, 250 full-time students from one of the Russian universities took part in the survey. The conducted research has shown that in the students’ view, harassment is not limited to sexual harassment and at the same time includes many practices that were previously fully or partially acceptable for the field of education. In the minds of students, there is a denormalization of bullying, evaluation and commenting on personality and appearance, discrimination based on gender, age, political views and other parameters. The most common stereotypes among students are that harassment is a media construct, that the older generation of men is the bearer of harassment practices, and only women become victims; there is also a widespread belief that the victim herself should cope with the situation in case of harassment. It was also recorded a polarization of opinions in the student environment about the reality of the problem and the essence of harassment and the strategies of action in case of harassment. As a result of the study by the authors of the article, proposals were formulated on the implementation of forms of preventing harassment and resolving conflict situations at the university level.

A Geometric Model of Opinion Polarization

We introduce a simple geometric model of opinion polarization. It is a model of political persuasion as well as marketing and advertising, utilizing social values. It focuses on the interplay between different topics and persuasion efforts. We demonstrate that societal opinion polarization often arises as an unintended by-product of influencers attempting to promote a product or idea. We discuss a number of mechanisms for the emergence of polarization involving one or more influencers, sending messages strategically, heuristically, or randomly. We also examine some computational aspects of choosing the most effective means of influencing agents and the effects of those strategic considerations on polarization. Funding: J. Hązła was partially supported by the National Science Foundation [Grant DMS-1737944] and later, the Alexander von Humboldt Foundation [African Institute for Mathematical Sciences Rwanda research chair funding] as well as the German Academic Exchange Service (DAAD) [grant in cooperation with Goethe University in Frankfurt]. Y. Jin was partially supported by the Department of Defense [Grant ARO MURI W911NF1910217]. E. Mossel was partially supported by the Simons Investigator in Mathematics [Award 622132], the National Science Foundation [Awards DMS-1737944 and CCF-1918421] and the Department of Defense [Grant ARO MURI W911NF1910217] and Vannevar Bush [Faculty Fellowship ONR-N00014-20-1-2826]. G. Ramnarayan was partially supported by the National Science Foundation [Grant DMS-1737944] and the Department of Defense [Grant ARO MURI W911NF1910217].

Masking or Unmasking the Evil? Polish Opinion-Forming Weeklies vis-à-vis the Crisis in the Roman Catholic Church in Poland

Almost twenty years have passed since the end of John Paul II’s pontificate. The Roman Catholic Church in Poland faces a serious crisis, stimulated not only by secularisation processes, which are characteristic for Western Europe, but also by a whole series of adverse events that unfolded at the end of the year 2020; they resembled a disaster marathon and had a considerable potential to attract media attention. A wide spectrum of Polish opinion-forming weeklies, ranging from left-liberal to ultra-conservative and far-right ones, as well as those associated with the Polish Episcopate, published accounts of the situation within the Catholic Church. In this manner, press discourse shaped the public perception of Catholicism in Poland. An analysis of the periodicals’ content disclosed a strong polarisation of opinions and a variety of interpretations. Differences between the particular weeklies were identified at the level of agenda-setting and prioritisation, framing, persuasion techniques employed, and additional contexts that were evoked, including the legal, moral, historical, philosophical, and religious ones.

Emergence of Polarization in Coevolving Networks.

Polarization is a ubiquitous phenomenon in social systems. Empirical studies document substantial evidence for opinion polarization across social media, showing a typical bipolarized pattern devising individuals into two groups with opposite opinions. While coevolving network models have been proposed to understand polarization, existing works cannot generate a stable bipolarized structure. Moreover, a quantitative and comprehensive theoretical framework capturing generic mechanisms governing polarization remains unaddressed. In this Letter, we discover a universal scaling law for opinion distributions, characterized by a set of scaling exponents. These exponents classify social systems into bipolarized and depolarized phases. We find two generic mechanisms governing the polarization dynamics and propose a coevolving framework that counts for opinion dynamics and network evolution simultaneously. Under a few generic assumptions on social interactions, we find a stable bipolarized community structure emerges naturally from the coevolving dynamics. Our theory analytically predicts two-phase transitions across three different polarization phases in line with the empirical observations for the Facebook and blogosphere data sets. Our theory not only accounts for the empirically observed scaling laws but also allows us to predict scaling exponents quantitatively.

Working too much in China's tech industry: corporate social advocacy as a crisis response strategy to issue-based opinion polarization

PurposeOnce a corporate crisis is entangled with a social issue, how consumers make sense of the crisis can be impacted by issue-based opinion polarization. This study investigates the underlying mechanisms as consumers go through this process. This study also examines whether corporate social advocacy (CSA) can be an effective crisis-response strategy for mitigating reputational loss.Design/methodology/approachTheoretical inquiries were empirically tested using an online experiment (N = 792). The experiment set the context in China, in a working-overtime-issue-related crisis. It had a 2 (online exposure: anti-issue opinion vs. pro-issue opinion) × 2 (CSA: absence vs. presence) between-subject design with a continuous variable (pre-existing issue attitudes) measured before the manipulation.FindingsThis study found that pre-existing issue attitudes can be directly and indirectly associated with corporate reputation, for the issue attitudes influence how consumers attribute crisis blame. Such a direct effect of pre-existing issue attitudes varies depending on which polarized opinion consumers were exposed to on social media. This study also found CSA to be a robust crisis response strategy, through multiple mechanisms, in protecting the corporate reputation.Originality/valueScholars are scarcely aware of the threats that issue-based opinion polarization poses to corporate reputation. This study serves as an early attempt to provide theoretical explanations. In addition to this, this study extends the current conceptual understandings of CSA during corporate crises that involve social issues while adding fresh insights into the established typology of crisis-response strategies.

Strategies for reducing polarization in social networks

The widespread usage of social networks has intensified the phenomenon of opinion polarization. This polarization has a corrosive effect on the functioning of public opinions, societies, and democracies. Hence, it is vital to devise strategies for reducing polarization. In this paper, we propose a depolarization model based on the modified linear threshold model and the Hegselmann-Krause model. Using this model, we then devise three depolarization strategies (building heterogeneous edges, embedding local neutral opinion, and introducing global neutral opinion), and conduct depolarization experiments with varied parameters, including self-belief parameter, social influence moderating parameter, and the selection mode of new edges and active nodes. Our experiments with synthesized and real-world datasets have yielded some interesting results.

Hate speech in the Internet context: Unpacking the roles of Internet penetration, online legal regulation, and online opinion polarization from a transnational perspective

Hate speech has plagued human societies for decades and severely influenced the whole world. By analyzing a balanced panel dataset covering 167 countries and 19 years derived from the V-Dem project, the relationships between critical Internet context indicators and online hate speech were disentangled, casting light on how to tackle the hate speech problem on a global scale. We found that Internet penetration was positively associated with online hate speech in general, confirming Internet's technological affordances have the potential to facilitate hate speech expression. Online legal regulation was negatively related to online hate speech but had no significant moderating effect between Internet penetration and online hate speech, reflecting an underlying tension among legal approaches’ comprehensive effects. Online opinion polarization was positively related to online hate speech and significantly moderated the relationship between Internet penetration and online hate speech, which indicated that a polarized opinion atmosphere might exacerbate hate speech and intensify intergroup conflicts. Our findings’ theoretical contributions and policy implications are further discussed.

Opinion Dynamics and Social Group Information Efficiency: an agent-based model

A platform for agent-based modelling of social processes is developed. The focus is on information exchange in a group of social agents. The dynamics of opinions on some topic is modelled, as well as the structure of social ties, which are weakened if opinions are distant and strengthened if opinions are close. In addition, to objectify the notion of "information efficiency of communication in a group", a test is proposed in which the structure of social ties is transferred to the structure of neural network connections, so that the strength of a tie is equated to a weight. Training of such a "sociomorphic" neural network was conducted and tested on the MNIST array, a test of the ability to recognisehandwritten digits from 0 to 9. The previously described effects of polarisation of opinions and division of the group into subgroups with high density of ties within subgroups and weak ties between agents from different subgroups were obtained in the variant of discussion of one topic. To approximate real groups, a topic selection algorithm was added to the basic opinion sharing algorithm, which is performed by comparing the cosine distance between semantic vectors - the vector of values assigned to each agent and the vector of each of the 9 topics. The topic vectors were generated based on the real news agenda of the news agency. The value vectors were taken from work in which a large sample of Russian youth was surveyed about their association with something most important in life. The modelling results show that the presence of several topics in the news agenda makes the communication process more flexible and multidimensional: even for the most semantically close to values and frequently chosen topics, polarisation of opinions was much slower than in the case of a single topic, and complete segmentation of the group was not observed over the modelled time period. This methodological approach can be useful for modelling conflicts in different communities, as well as for designing artificial communities with increased constructive creative potential. For example, the model can be used to optimise communications in complex multicultural societies

Конфессиональные модели церковной власти (философско-канонический анализ)

Introduction. Judgments on church authority have been transformed depending on the changing posi- tion of the Church in society and its relationship with the state. In the process of rethinking, there have developed special traditions of its understand- ing, which reflected the specifics of the existence of Christian communities in different cultural and legal conditions. The purpose of the study is to outline the traditions of understanding church authority in three Christian denominations and offer grounds for their comparison in the philosophical and canonical aspect. Methods. A comparative method is used on the basis of analyzing philosophical literature and canon-legal sources. Scientific novelty of the research. The article for the first time systematizes confessional differences in interpreting the form, content and levels of church authority. Results. It is shown that the decisive factor de- termining the differences between confessional traditions in the understanding of church authority is the nature of the boundary between the sacred and government authorities. Catholicism, consider- ing the governmental power established by God, includes it in the sacred, along with the teaching and sacramental. Protestantism, on the contrary, radically opposes it to the sacred, recognizing it as the prerogative of the state. Orthodoxy, as a rule, does not recognize government power as sacred, but it does not separate it from the church. Conclusions. An analysis of the positions ex- pressed by representatives of various Christian denominations regarding church authority makes it possible for us to conclude that the Orthodox tradi- tion tends to defend a position equidistant from polar opinions.

SMART PUBLIC OPINION MONITORING AND ANALYSIS SYSTEM FOR ETHNIC REGIONS: A MULTI-MODAL APPROACH USING MACHINE LEARNING

In ethnic regions, public opinion plays a vital role in shaping social stability and harmony. However, monitoring and analyzing public opinion in these regions can be challenging due to the complex linguistic and cultural factors involved. To address this issue, this study proposes a Smart Public Opinion Monitoring and Analysis System for Ethnic Regions, which utilizes a multi-modal approach for data acquisition and machine learning techniques for analysis. The system collects public opinion data from multiple modalities, including text, speech, image, and video, and uses intelligent speech processing and text translation to analyze non-textual data. The sentiment analysis module evaluates the polarity of public opinion, while the topic modeling module identifies key topics and keywords of public interest. The system also includes an early warning and risk detection module that uses machine learning algorithms to detect potential risks and generate alerts in real-time. Finally, the data visualization module presents the analysis results in an intuitive and user-friendly manner. The proposed system has been tested on a dataset of public opinion data from ethnic regions, and the results demonstrate its effectiveness in monitoring and analyzing public opinion in different modalities. The system can help government agencies and relevant stakeholders to respond quickly to potential risks and maintain social stability in ethnic regions.

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.

Digital Power in Latin America: Hashtags and deepfakes as political tools

Latin American countries are among the top ten countries in the world where the Internet, social networks, and new technologies including deepfakes, synthetic media, etc. have gained popularity among various stakeholders. Digital diplomacy being a channel to make influence on target audience through social media has turned out to be a driver in domestic and foreign policies of the countries, including Uruguay, Brazil, Venezuela, etc. The digital diplomacy is the cohesive element of the cyber power interpreted here as the ability of a country to influence on technological and political processes in the world. The article discusses digital instruments of influence, notably hashtag diplomacy and deepfakes. They are most often applied in Latin American countries in political events. While the hashtag diplomacy steers a rapid mobilization of citizens, deepfakes make the audience be manipulated through the creating of synthetic content (generated by algorithms or artificial intelligence) of the media, false news and events that contributes to the polarization of opinions, the spreading of rumors and conspiracy theories, and extremist actions as well. The high efficiency and deceptive potential of the technology make it possible to consider it as a national threat. In order to illustrate the activity of state and non-state actors in social networks, the authors used the case of the TikTok social platform. The research methodology is based on data retrieved from the Latin American segment of the Internet, and on the subsequent qualitative analysis. The study has made a classification of deepfakes that are most actively used for political purposes in the Latin American region. The article analyzes examples of both destructive and “soft” use of deepfakes.

Syntax-Based Aspect Sentiment Quad Prediction by Dual Modules Neural Network for Chinese Comments

Aspect-Based Sentiment Analysis (ABSA) is one of the essential research in the field of Natural Language Processing (NLP), of which Aspect Sentiment Quad Prediction (ASQP) is a novel and complete subtask. ASQP aims to accurately recognize the sentiment quad in the target sentence, which includes the aspect term, the aspect category, the corresponding opinion term, and the sentiment polarity of opinion. Nevertheless, existing approaches lack knowledge of the sentence’s syntax, so despite recent innovations in ASQP, it is poor for complex cyber comment processing. Also, most research has focused on processing English text, and ASQP for Chinese text is almost non-existent. Chinese usage is more casual than English, and individual characters contain more information. We propose a novel syntactically enhanced neural network framework inspired by syntax knowledge enhancement strategies in other NLP studies. In this framework, part of speech (POS) and dependency trees are input to the model as auxiliary information to strengthen its cognition of Chinese text structure. Besides, we design a relation extraction module, which provides a bridge for the overall extraction of the framework. A comparison of the designed experiments reveals that our proposed strategy outperforms the previous studies on the key metric F1. Further experiments demonstrate that the auxiliary information added to the framework improves the final performance in different ways.

Испанский социум: стабильная нестабильность

Instability has become a “new normal” for Spain. This relatively young democracy with numerous semiperipheral traits is seeing new challenges overlap the long-standing old ones, which makes instability greater than in more developed European Union countries. The article is the first Russian academic paper to cover the entirety of destabilizing factors in contemporary Spanish society. Among the destabilizers one can identify those that may be traced back to Spain’s historical past. These include polarization of opinions on the legacy of the Civil War and the Franco regime, elements of authoritarian attitudes in mass consciousness and political practices of organizations, as well as conflicts between supporters of monarchy and the republican movement, between avid catholics and secularists. Another contributing factor is fragility of the political system that shows itself in personalist (caudillist) parties, whose mechanisms for intra-party democracy are still immature. There is also a disproportionate electoral legislation, politicized judiciary, and the unfinished nation state that has failed to truly integrate Catalonia and the Basque Country. Another destabilizer is volatile social and economic development. Spain has one of the highest rates of unemployment and precarious employment in the EU, intensive growth of precariat, flawed healthcare and education systems, immigration galvanizing both indigenous nationalism and xenophobia, with the reluctance of many immigrants to adapt to Spanish culture and society. Minimizing risks related to destabilizing factors is an imperative for Spain’s present and future.

Distance from Unimodality for the Assessment of Opinion Polarization

Commonsense knowledge is often approximated by the fraction of annotators who classified an item as belonging to the positive class. Instances for which this fraction is equal to or above 50% are considered positive, including however ones that receive polarized opinions. This is a problematic encoding convention that disregards the potentially polarized nature of opinions and which is often employed to estimate subjectivity, sentiment polarity, and toxic language. We present the distance from unimodality (DFU), a novel measure that estimates the extent of polarization on a distribution of opinions and which correlates well with human judgment. We applied DFU to two use cases. The first case concerns tweets created over 9 months during the pandemic. The second case concerns textual posts crowd-annotated for toxicity. We specified the days for which the sentiment-annotated tweets were determined as polarized based on the DFU measure and we found that polarization occurred on different days for two different states in the USA. Regarding toxicity, we found that polarized opinions are more likely by annotators originating from different countries. Moreover, we show that DFU can be exploited as an objective function to train models to predict whether a post will provoke polarized opinions in the future.

Vanishing Opinions in Latané Model of Opinion Formation.

In this paper, the results of computer simulations based on the Nowak-Szamrej-Latané model with multiple (from two to five) opinions available in the system are presented. We introduce the noise discrimination level (which says how small the clusters of agents could be considered negligible) as a quite useful quantity that allows qualitative characterization of the system. We show that depending on the introduced noise discrimination level, the range of actors' interactions (controlled indirectly by an exponent in the distance scaling function, the larger the exponent, the more influential the nearest neighbors are) and the information noise level (modeled as social temperature, which increases results in the increase in randomness in taking the opinion by the agents), the ultimate number of the opinions (measured as the number of clusters of actors sharing the same opinion in clusters greater than the noise discrimination level) may be smaller than the number of opinions available in the system. These are observed in small and large information noise limits but result in either unanimity, or polarization, or randomization of opinions.

АНАЛИЗ ТОНАЛЬНОСТИ ТЕКСТОВЫХ ОТЗЫВОВ С ПРИМЕНЕНИЕМ ТОНАЛЬНЫХ СЛОВАРЕЙ И КАРДИНАЛЬНОСТИ НЕЧЕТКОГО МНОЖЕСТВА

Sentiment or opinion analysis aims to determine the polarity of people's opinions in relationto any product, service, event or any person. One of the most common methods used in sentimentanalysis of text content is natural language processing. Sentiment analysis of natural languagetext can be assessed using numerous methodologies such as machine learning algorithms andstatistical tools, while the application of fuzzy logic is not common. The use of fuzzy logic waschosen for the following reasons. First, fuzzy logic handles linguistic uncertainty well. This way ofdefining the problem leads to a reduction in bias, both positively and negatively. Secondly, learning approaches based on fuzzy rules are fundamentally different from those learning approachesthat are widely used in sentiment classification, such as support vector machines, naive Bayes,etc., as they relate to generative learning, i.e. i.e. the goal of learning is to assess the degree towhich an instance belongs to each individual class. The proposed model for sentiment analysis oftext reviews is based on the use of tone lexicons using fuzzy logic and consists of four main stages.The steps include tokenization, word bag model formulation, sentiment fuzzy score formulation,and polarity assignment. In the proposed model, the power of the fuzzy set is used as a measure ofthe evaluation of the indicators of the polarity of words. Word polarity values are obtained byapplying two sentiment lexicons: SentiWordNet and AFINN. Two versions of the model were createddepending on the type of vocabulary used: based on SentiWordNet and AFINN. Comparisonof the presented approach based on fuzzy logic with other dictionary-based methods demonstratesthe superiority of the developed models based on the application of fuzzy logic.

Reducing socio-ecological conflict using social influence modelling

Polarisation of opinions across communities can lead to social conflict, reputational damage and the disruption of operations and markets. Social influence models have been widely used to better understand processes driving conflict from a theoretical perspective. Using aquaculture as a case study, we demonstrate how such models can be extended to accurately hindcast the transition from population consensus to high conflict, including observed catastrophic tipping points. We then use the model to quantitatively evaluate strategies aimed at reducing aquaculture conflict. We found that persuasive advocacy was ineffective and often counterproductive, whereas meaningful engagement, collaborative learning and improving scientific literacy targeted broadly across the population was effective in moderating opinions and reducing conflict. When such messaging was targeted too narrowly or too infrequently, it tended to be negated by ongoing exchange of misinformation within the population. Both the modelling approach and lessons on effective communication strategies are relevant to a broad range of environmental conflicts.