Opinion Spreading Research Articles

Spread of Online Public Opinion of Animal Epidemic Emergency: A Case Study of the H7N9 Incident Based on Healthcare Data Analytics.

Qualitative and quantitative analysis of online public opinion of the H7N9 animal epidemic incident in 2013 were conducted based on social network analysis (SNA) theories, SNA method, and life cycle theories. Trend and features of evolution of online public opinion of animal epidemic emergency were explored and structural characteristics of key nodes in these public opinion spreading networks were identified. The stages of spreading of public opinion of animal epidemics were investigated. This study provides references for the government to cope with online public opinion of animal epidemic emergencies in the future.

The Evolutionary Game Analysis of Public Opinion Supervision of Engineering Quality in the Network Citizen Journalism Environment

With the rapid development of mobile networks and citizen journalism, public opinion supervision has become an essential social supervision on engineering quality. They consider the dynamic characteristics of the spread process of public opinion and the game process of social supervision on engineering quality. The tripartite evolutionary game model of the government, contractors, and the public was constructed by coupling the Susceptible-Exposed-Infected-Removed (SEIR) model of public opinion spread and the evolutionary game model. Then, the influence laws of public opinion spread on the tripartite evolutionary game were analyzed and discussed through numerical simulation analysis. The results show that the public with more significant influence or authority is more able to restrain the quality behavior of government and contractors; increasing the probability of transforming ignorant into latent, the probability of converting latent into the communicator and topic derivation rate or reducing the direct immunization self-healing can improve the effectiveness of public opinion supervision; the true online public opinion can effectively restrain the quality behavior of contractors and urge the government to supervise actively. The research conclusions can provide a reference for improving the social supervision mechanism of engineering quality in the era of network citizen journalism.

Inferring microscale properties of interacting systems from macroscale observations

Emergent dynamics of complex systems are observed throughout nature and society. The coordinated motion of bird flocks, the spread of opinions, fashions and fads, or the dynamics of an epidemic, are all examples of complex macroscale phenomena that arise from fine-scale interactions at the individual level. In many scenarios, observations of the system can only be made at the macroscale, while we are interested in creating and fitting models of the microscale dynamics. This creates a challenge for inference as a formal mathematical link between the microscale and macroscale is rarely available. Here, we develop an inferential framework that bypasses the need for a formal link between scales and instead uses sparse Gaussian process regression to learn the drift and diffusion terms of an empirical Fokker-Planck equation, which describes the time evolution of the probability density of a macroscale variable. This gives us access to the likelihood of the microscale properties of the physical system and a second Gaussian process is then used to emulate the log-likelihood surface, allowing us to define a fast, adaptive MCMC sampler, which iteratively refines the emulator when needed. We illustrate the performance of our method by applying it to a simple model of collective motion.

Hopf bifurcation in an opinion model with state-dependent delay

In a recent paper [R. Qesmi, Dynamics of an opinion model with threshold-type delay, Chaos, Solitons & Fractals 98 (2020). (https://doi.org/10.1016/j.chaos.2020.110379)], we proposed a mathematical model of threshold-type delay differential equations describing the relationship between two subpopulations with opposite opinions and the opinion spread dynamics. The study there showed the possibility of a transcritical forward and backward bifurcations of positive equilibria. In the present paper, we show that the opinion model undergoes a Hopf bifurcation through which one of the bifurcation branches loses the stability and periodic solutions appear. One of the important consequences of the obtained dynamics is that the consensus of the both opinions could be lost by maintaining the balance between the time taken for an individual to become convinced of the outsider opinion, which need be short, and the number of individuals converted to the local opinion which need be low. Finally, we provide numerical simulations to illustrate and support our theoretical results.

Consensus, polarization, and coexistence in a continuous opinion dynamics model with quenched disorder.

A model of opinion dynamics is introduced in which each individual's opinion is measured on a bounded continuous spectrum. Each opinion is influenced heterogeneously by every other opinion in the population. It is demonstrated that consensus, polarization and a spread of moderate opinions are all possible within this model. Using dynamic mean-field theory, we are able to identify the statistical features of the interactions between individuals that give rise to each of the aforementioned emergent phenomena. The nature of the transitions between each of the observed macroscopic states is also studied. It is demonstrated that heterogeneity of interactions between individuals can lead to polarization, that mostly antagonistic or contrarian interactions can promote consensus at a moderate opinion, and that mostly reinforcing interactions encourage the majority to take an extreme opinion.

Research on Evolutionary Model and Dynamic Analysis for Internet Public Opinion Spreading

Internet public opinion has developed rapidly in recent years, which has more influence on society. The relevant problems of Internet public opinion have become the hotspots of research. The complexity of Internet public opinion spreading brings difficulties to research and analysis. For the problems of Internet public opinion spreading, an evolutionary model of Internet public opinion spreading is built based on evolutionary game theory in this paper, considering three subjects including Internet media, Internet users, and government. Then, the evolutionary stable strategies of the Internet public opinion spreading system are studied. Finally, the influences of the stable strategy of the Internet public opinion spreading system are analyzed through simulation. Relevant conclusions are obtained. The results show that there are multiple possible stable strategies in the evolution of Internet public opinion. The different initial probabilities of the strategy lead to the different stable strategies of the evolutionary system. The stable strategy is influenced by some factors, such as the punished loss of Internet media, the gain of Internet users from government controlling, and the cost of government. These conclusions have a certain guiding significance to Internet public opinion management and control.

Research on Mining and Application of Group Events Based on Network Public Opinion Big Data

Network Public Opinion is significant in maintaining social harmony and stability and promoting transparency in government affairs. However, with the development of economy and transformation of society, our country has entered a high-risk period, which is full of unexpected public events. Unexpected mass accidents also cause hot discussions among the Internet users once they are exposed on the network. Different ideas, opinions, emotions, and attitudes about unexpected public events will be collected and collide on the Internet. It makes Network Public Opinion play an increasingly important role in the evolution of unexpected public events. It could promote the spread and upgrade of unexpected public events and bring more and more profound influence on to our social life. We use the case study method to analyze and solve the problems by applying the dynamic principles of the SIR epidemic model, comprehensively considering the social environment and various influencing factors, and constructing a mathematical model for the spread of network group events. The study uses Matlab to simulate the change trajectory of the number of participants in the network group events. By adjusting the number of contacts φ in the model, the development of network group emergencies can be effectively controlled and managed. As long as the government takes timely intervention measures, the dissemination of network group events can be basically controlled. Combined with public opinion big data to discover the important factors affecting the spread of public opinion, the control effect is obvious.

Internet Public Opinion Diffusion Mechanism in Public Health Emergencies: Based on Entropy Flow Analysis and Dissipative Structure Determination.

As an empirical case, this study selected the illegal production process incidents of rabies and DPT (Diphtheria, Pertussis, Tetanus) vaccines by Changchun Longevity Biotechnology Co., Ltd., which occurred in July 2018. Based on the four factors involved in the spread of public opinion, the public health emergency, netizen, network media, and government, Brusselator model, and entropy method were applied to calculate the positive and negative entropy—to verify whether the Internet public opinion system is a dissipative structure. This study verified four evolution mechanisms in Internet public opinion diffusion, among which the trigger point of entropy-control occurred in the germination mechanism, the entropy-controlled disposal point occurred in the outbreak and fluctuating mechanism, and then became latency in the elimination mechanism. It provides a theoretical reference for the government to judge the stage of such diffusion and improve the governance ability of the opinion mentioned above.

Internet public opinion dissemination mechanism of COVID-19: evidence from the Shuanghuanglian event

PurposeThe sudden outbreak of COVID-19 has become a major public health emergency of global concern. Studying the Internet public opinion dissemination mechanism of public health emergencies is of great significance for creating a legalized network environment, and it is also helpful for managers to make scientific decisions when encountering Internet public opinion crisis.Design/methodology/approachBased on the analysis of the process of spreading the Internet public opinion in major epidemics, a dynamic model of the Internet public opinion spread system was constructed to study the interactive relationship among the public opinion events, network media, netizens and government and the spread of epidemic public opinion. The Shuanghuanglian event in COVID-19 in China was taken as a typical example to make simulation analysis.FindingsResearch results show three points: (1) the government credibility plays a decisive role in the spread of Internet public opinion; (2) it is the best time to intervene when Internet public opinion occurred at first time; (3) the management and control of social media are the key to public opinion governance. Besides, specific countermeasures are proposed to assist control of Internet public opinion dissemination.Originality/valueThe epidemic Internet public opinion risk evolution system is a complex nonlinear social system. The system dynamics model is used to carry out research to facilitate the analysis of the Internet public opinion propagation mechanism and explore the interrelationship of various factors.

A framework of opinion dynamics using fuzzy linguistic 2-tuples

Opinion dynamics investigate the spreading and evolution of opinions among a set of individuals. This is especially relevant in decision-making — the process of selecting an alternative from a set of possible options —, that is commonly based on personal opinions which may evolve along time. In this work, we present a model of opinion dynamics where opinions are represented using fuzzy linguistic 2-tuples, a realistic representation of imprecise information. In our framework, the propagation of opinions in the communication is divided into three independent sub-processes. Additionally, we use a social network to represent agents’ interactions and an awareness deactivation mechanism to model the awareness dynamics in the system (i.e., options for which agents have opinions). Our opinion dynamics model can be easily integrated into an agent-based system to how opinions spread and evolve. Experimental results show the impact of the communication processes, the social network topology, the awareness deactivation mechanism, and the agents’ influence on the opinion dynamics of others. Furthermore, we present two case studies of our opinion dynamics model applied to marketing and politics.

Learning Hidden Influences in Large-Scale Dynamical Social Networks: A Data-Driven Sparsity-Based Approach, in Memory of Roberto Tempo

The processes of information diffusion across social networks (for example, the spread of opinions and the formation of beliefs) are attracting substantial interest in disciplines ranging from behavioral sciences to mathematics and engineering (see "Summary"). Since the opinions and behaviors of each individual are infl uenced by interactions with others, understanding the structure of interpersonal infl uences is a key ingredient to predict, analyze, and, possibly, control information and decisions [1]. With the rapid proliferation of social media platforms that provide instant messaging, blogging, and other networking services (see "Online Social Networks") people can easily share news, opinions, and preferences. Information can reach a broad audience much faster than before, and opinion mining and sentiment analysis are becoming key challenges in modern society [2]. The first anecdotal evidence of this fact is probably the use that the Obama campaign made of social networks during the 2008 U.S. presidential election [3]. More recently, several news outlets stated that Facebook users played a major role in spreading fake news that might have infl uenced the outcome of the 2016 U.S. presidential election [4]. This can be explained by the phenomena of homophily and biased assimilation [5]-[7] in social networks, which correspond to the tendency of people to follow the behaviors of their friends and establish relationships with like-minded individuals.

통일교육정책의 변동 요인에 관한 연구

This paper used Kingdon's multiple streams framework to analyze why the unification of the education policy was changed in 2008. As analytical results, in the problem steams, unification education reflects the reality as it is and strengthens security due to the opposition to the proliferation of weapons of mass destruction, domestic politicization of North Korea policy, discussion of North Korea's sudden change, North Korea's nuclear development, and the Cheonan and Yeonpyeong Island incidents. In the political streams, President Lee Myung-bak took office, who had the idea that overall change was necessary in the policy of appeasement toward North Korea. Support for North Korea policy by the Koreans, the spread of negative awareness about North Korea and unification, the spread of public opinion to reinforce the weakened sense of security, created conditions for changes in unification education. In the policy streams, Proposals that the existing education for unification needed to be changed suggested. These three streams were able to combine with the creation of policy opportunities such as 'change of government to conservatives' and 'the Cheonan/Yeonpyeong Island incident', leading to policy changes.

A Robust Reputation-Based Group Ranking System and Its Resistance to Bribery

The spread of online reviews and opinions and its growing influence on people’s behavior and decisions boosted the interest to extract meaningful information from this data deluge. Hence, crowdsourced ratings of products and services gained a critical role in business and governments. Current state-of-the-art solutions rank the items with an average of the ratings expressed for an item, with a consequent lack of personalization for the users, and the exposure to attacks and spamming/spurious users. Using these ratings to group users with similar preferences might be useful to present users with items that reflect their preferences and overcome those vulnerabilities. In this article, we propose a new reputation-based ranking system, utilizing multipartite rating subnetworks, which clusters users by their similarities using three measures, two of them based on Kolmogorov complexity. We also study its resistance to bribery and how to design optimal bribing strategies. Our system is novel in that it reflects the diversity of preferences by (possibly) assigning distinct rankings to the same item, for different groups of users. We prove the convergence and efficiency of the system. By testing it on synthetic and real data, we see that it copes better with spamming/spurious users, being more robust to attacks than state-of-the-art approaches. Also, by clustering users, the effect of bribery in the proposed multipartite ranking system is dimmed, comparing to the bipartite case.

EOM-NPOSESs: Emergency Ontology Model Based on Network Public Opinion Spread Elements

The construction of an emergency ontology model plays an important role in emergency management, which is an important basis for emergency public opinion management and decision-making. Integration of network public opinion spread elements into the emergency ontology model is crucial for realizing knowledge sharing in the field of emergency and public opinion responses. In this study, we crawl a large amount of emergency data from different data sources and construct an emergency dataset. Based on this dataset, we analyze the public opinion elements of emergencies and propose an emergency ontology model based on network public opinion spread elements (EOM-NPOSESs). Thereafter, we consider the coronavirus disease (COVID-19) emergency as an example to construct the EOM-NPOSESs. Finally, we design some strategies to realize rule reasoning and present the COVID-19 emergency application based on the constructed EOM-NPOSESs and the geographic information system platform. The results demonstrate that EOM-NPOSESs can not only describe the semantic relationship between emergencies and emergency elements but also perform semantic logical reasoning on different emergencies.

Network Public Opinion Monitoring System for Agriculture Products Based on Big Data

The influence of online public opinion on agricultural product safety on the society is increasing. In order to correctly guide the direction of online public opinion on agricultural products, help the agricultural sector turn from passive to active public opinion, timely prevent the spread of negative public opinion, and reduce the negative impact on public opinion hot events, it is especially important to improve the ability of monitoring agricultural products’ network public opinion. This research is based on big data technology to develop an agricultural products’ network public opinion monitoring system that can collect, process, and analyze data in real time, discover and track hot topics, and calculate and visualize the polarity of public sentiment. The use of big data technology to increase the processing speed aims to strengthen the public’s supervision of the public opinion on the network security of agricultural products and provide an effective basis of the decision-making of relevant departments.

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.

Реалии применения антикоагулянтной терапии при COVID-19

Введение. Поступление вируса SARS-CoV-2 в организм человека сопровождается развитием COVID-19-ассоциированной коагулопатии, часто реализующейся в различных тромботических осложнениях. Актуальные клинические рекомендации описывают основные подходы к лечению и профилактике венозных тромбоэмболических осложнений (ВТЭО), однако реализация их не всегда представляется возможной. В реальной практике встречается значительное количество отклонений и нарушений, в том числе носящих системный характер, связанных как с ограниченностью представлений клиницистов о патогенетических аспектах развития инфекционного процесса, так и с избыточным желанием предотвратить отдельные из них. Цель исследования: изучение состояния реальной клинической практики в области лечения и профилактики развития тромботических осложнений у пациентов c новой коронавирусной инфекцией COVID-19 (НКИ COVID-19). Материалы и методы. В основе работы лежит анонимный опрос 223 врачей-специалистов, занимающихся лечением пациентов с хроническими и острыми заболеваниями сосудов. В опросник включено 18 вопросов, описывающих отношение врачей к проблеме в целом, а также касающихся выбора тактики лечения, применения антикоагулянтных препаратов и методов контроля системы гемостаза. Результаты. Подавляющее большинство участников опроса информированы и озадачены увеличением риска тромботических осложнений при НКИ COVID-19. При этом приоритетной целью антикоагулянтной терапии (АКТ) в острой фазе заболевания участники назвали лечение COVID-19-ассоциированной коагулопатии, а в стадии реконвалесценции — профилактику ВТЭО. Максимальную степень доверия при назначении АКТ имеют низкомолекулярные гепарины. Прямые оральные антикоагулянты, несмотря на отсутствие качественных рандомизированных исследований, подтверждающих их эффективность, большая часть врачей использует на амбулаторном этапе. Вызывает озабоченность значительный разброс вариантов и невысокий процент корректных ответов в вопросах, посвященных лабораторному контролю за системой гемостаза при АКТ. Заключение. Выполненный нами анализ свидетельствует о существенном расхождении реального применения антикоагулянтных препаратов у пациентов с НКИ COVID-19 с действующими клиническими рекомендациями. Кроме того, полученные результаты свидетельствуют о необходимости повышения уровня образованности практикующих специалистов в вопросах свертывания крови. Background. The SARS-CoV-2 virus invasion lead to COVID-19-associated coagulopathy accompanied with increased incidence of thrombotic complications. Current clinical guidelines give the main approaches to the treatment and prevention of them; however their implementation is not always possible in practice. In fact, there are a lot of violations including with a systemic genesis and associated either with the low understanding of infectious process pathogenesis aspects by clinicians or with their excessive desire to prevent coagulation disturbances. the revealing of real clinical practice conditions in the treatment and prevention of thrombotic complications in patients with coronavirus infection COVID-19. Materials/Methods. We provided an anonymous poll for 223 experts treating patients with chronic and acute vascular diseases. The questionnaire included 18 questions to identify as the experts attitude to this challenge as a whole as well as to their choice of treatment tactics, and anticoagulant drugs, and methods of laboratory monitoring of blood coagulation. Results. Most participants know the increased risk of thrombotic complications in COVID-19 and they are puzzled by it The treatment of COVID-19-associated coagulopathy is considered as the priority goal of anticoagulant therapy in the acute phase of COVID-19 whereas the prevention of venous thromboembolic complications is noted as main goal in convalescences. Low molecular weight heparins have gotten the highest confidence in the administration among anticoagulants. In turn, the most of experts use direct oral anticoagulants in outpatients even despite no confirmation is for DOAC’s effectiveness from randomized trials in this time. Besides it was revealed wide spread of opinions and low count of correct responses about laboratory control of the hemostatic system and anticoagulants. Conclusion. The analysis showed a serious inconsistency between the real anticoagulants administration in patients with COVID-19 and the recommendations of clinical guidelines. This circumstance obviates the need to raise educational level of physicians and surgeons in the field of blood coagulation.

Hierarchical coarse-grained approach to the duration-dependent spreading dynamics on complex networks

Various coarse-grained models have been proposed to study the spreading dynamics on complex networks. A microscopic theory is needed to connect the spreading dynamics with individual behaviors. In this letter, we unify the description of different spreading dynamics by decomposing the microscopic dynamics into two basic processes, the aging process and the contact process. A hierarchical duration coarse-grained (DCG) approach is proposed to study the duration-dependent processes. Applied to the epidemic spreading, such formalism is feasible to reproduce different epidemic models, e.g., the SIS and the SIR models, and to associate the macroscopic spreading parameters with the microscopic mechanism. The DCG approach enables us to study the steady state of the duration-dependent SIS model. The current hierarchical formalism can also be used to describe the spreading of information and public opinions, or to model a reliability theory on networks.

Cascading dominates large-scale disruptions in transport over complex networks

The core functionality of many socio-technical systems, such as supply chains, (inter)national trade and human mobility, concern transport over large geographically-spread complex networks. The dynamical intertwining of many heterogeneous operational elements, agents and locations are oft-cited generic factors to make these systems prone to large-scale disruptions: initially localised perturbations amplify and spread over the network, leading to a complete standstill of transport. Our level of understanding of such phenomena, let alone the ability to anticipate or predict their evolution in time, remains rudimentary. We approach the problem with a prime example: railways. Analysing spreading of train delays on the network by building a physical model, supported by data, reveals that the emergence of large-scale disruptions rests on the dynamic interdependencies among multiple ‘layers’ of operational elements (resources and services). The interdependencies provide pathways for the so-called delay cascading mechanism, which gets activated when, constrained by local unavailability of on-time resources, already-delayed ones are used to operate new services. Cascading locally amplifies delays, which in turn get transported over the network to give rise to new constraints elsewhere. This mechanism is a rich addition to some well-understood ones in, e.g., epidemiological spreading, or the spreading of rumours and opinions over (contact) networks, and stimulates rethinking spreading dynamics on complex networks. Having these concepts built into the model provides it with the ability to predict the evolution of large-scale disruptions in the railways up to 30-60 minutes up front. For transport systems, our work suggests that possible alleviation of constraints as well as a modular operational approach would arrest cascading, and therefore be effective measures against large-scale disruptions.

Memory-Based Reduced Modelling and Data-Based Estimation of Opinion Spreading

We investigate opinion dynamics based on an agent-based model and are interested in predicting the evolution of the percentages of the entire agent population that share an opinion. Since these opinion percentages can be seen as an aggregated observation of the full system state, the individual opinions of each agent, we view this in the framework of the Mori–Zwanzig projection formalism. More specifically, we show how to estimate a nonlinear autoregressive model (NAR) with memory from data given by a time series of opinion percentages, and discuss its prediction capacities for various specific topologies of the agent interaction network. We demonstrate that the inclusion of memory terms significantly improves the prediction quality on examples with different network topologies.