Research on the propagation and governance of public opinion information under the joint action of internal and external factors

PurposeUnder the new media environment, while enjoying the convenience brought by the propagation of public opinion information (referred to as public opinion), learning the evolution process of public opinion and strengthening the governance of the spreading of public opinion are of great significance to promoting economic development and maintaining social stability as well as effectively resisting the negative impact of its propagation.Design/methodology/approachThinking about the results of empirical research and bibliometric analysis, this paper focused on introducing key factors such as information content, social strengthening effects, etc., from both internal and external levels, dynamically designed public opinion spreading rules and netizens' state transition probability. Subsequently, simulation experiments were conducted to discuss the spreading law of public opinion in two types of online social networks and to identify the key factors which influencing its evolution process. Based on the experimental results, the governance strategies for the propagation of negative public opinion were proposed finally.FindingsThe results show that compared with other factors, the propagation of public opinion depends more on the attributes of the information content itself. For the propagation of negative public opinion, on the one hand, the regulators should adopt flexible guidance strategy to establish a public opinion supervision mechanism and autonomous system with universal participation. On the other hand, they still need to adopt rigid governance strategy, focusing on the governance timing and netizens with higher network status to forestall the wide-diffusion of public opinion.Practical implicationsThe research conclusions put forward the enlightenment for the governance of public opinion in management practice, and also provided decision-making reference for the regulators to reasonably respond to the propagation of public opinion.Originality/valueOur research proposed a research framework for the discussion of public opinion propagation process and had important practical guiding significance for the governance of public opinion propagation.

Modeling and Quantifying the Role of User Active Search in COVID-19 Multi-Information Analysis and Prediction in the Chinese Sina-microblog

The study of information spreading is important and necessary, especially during the Coronavirus, when plenty of opinions aroused in the Chinese Sina-microblog. On the basis of previous studies, we propose a comprehensive susceptible-reading-forwarding-immune (SRFI) model with considering user active search. We establish differential equations introducing average active reading rate to describe the multi-information propagation process. By using typical event about COVID-19 during the outbreak of public opinion to carry out the numerical fitting experiment to estimate model parameters, fit real data, and analyze the calculated information transmission indexes, we verify the validity of the model. We analyze the sensitivity of multiple parameters to multi-information transmission index based on reading and forwarding and the effect of average active reading rate to show the influence of the new parameter on multi-information transmission. In addition, to compare the predictive ability of the previous model with our new model, we use the early prediction method. Result shows that our new model can forecast the process of multi-information transmission faster and more exactly. The conclusions above indicate that the role of user active search is not negligible and the I-SRFI model can help us design effective communication strategies for rapid implementation of public health interventions. © 2022 IEEE.

Italian top actors during the COVID-19 infodemic on Twitter

The COVID-19 pandemic has led to a corresponding infodemic, emphasised by the use of social media as the primary communication channel during lockdowns. This study was aimed at finding the accounts that spread information in Italian on COVID-19, and how such information was propagated in the first Western country to face a lockdown. The presented analysis shows that, besides authoritative news media and institutional accounts, a relevant role was played by actors from the 'civil society', which included a popular virologist as well as a far-right activist and an unfamiliar account supporting anti-government and anti-immigration ideas. Quite surprisingly, this latter account achieved the highest number of retweets despite a relatively low number of followers. Also, it showed information propagation paths similar to health experts and institutions. © 2022 Inderscience Enterprises Ltd.

External intervention model with direct and indirect propagation behaviors on social media platforms.

A significant distinction between the COVID-19 pandemic and previous pandemics is the significant role of social media platforms in shaping public adherence to non-pharmaceutical interventions and vaccine acceptance. However, with the recurrence of the epidemic, the conflict between epidemic prevention and production recovery has become increasingly prominent on social media. To help design effective communication strategies to guide public opinion, we propose a susceptible-forwarding-immune pseudo-environment (SFI-PE) dynamic model for understanding the environment with direct and indirect propagation behaviors. Then, we introduce a system with external interventions for direct and indirect propagation behaviors, termed the macro-controlled SFI-PE (M-SFI-PE) model. Based on the numerical analyses that were performed using actual data from the Chinese Sina microblogging platform, the data fitting results prove our models' effectiveness. The research grasps the law of the new information propagation paradigm, and our work bridges the gap between reality and theory in information interventions.

Combined Influence of Commenting and Forwarding on Information Propagation on the Chinese Sina Microblog

After the outbreak of the COVID-19 pandemic, social media platforms offer an essential channel for the public to obtain and discuss the latest development of the epidemic situation and vaccine research. On the Chinese Sina Microblog, which is one of the most popular social platforms in China, two unique interaction mechanisms promote the change of the intensity and breadth of online information propagation, namely 'commenting' and 'forwarding'. Based on that, we propose a Susceptible-Commenting-Forwarding-Immune (SCFI) dynamic model and use the actual public opinion event on the Chinese Sina Microblog to adopt a data-model dual-drive research approach. We focus on the differences between the influence of 'commenting community' and 'forwarding community' on the promotion of information propagation, which is conducive to grasping the law of public opinion propagation. Our experimental results show that the multiple interactive mechanisms can particularly affect public opinion propagation. Our conclusions can contribute to designing effective communication strategies for governments and related agencies to guide public opinion in response to public health emergencies. © 2022 IEEE.

The effect of information literacy heterogeneity on epidemic spreading in information and epidemic coupled multiplex networks.

With the COVID-19 pandemic, better understanding of the co-evolution of information and epidemic diffusion networks is important for pandemic-related policies. Using the microscopic Markov chain method, this study proposed an aware-susceptible-infected model (ASI) to explore the effect of information literacy on the spreading process in such multiplex networks. We first introduced a parameter that adjusts the self-protection related execution ability of aware individuals in order to emphasis the importance of protective behaviors compared to awareness in decreasing the infection probability. The model also captures individuals' heterogeneity in their information literacy. Simulation experiments found that the high information-literate individuals are more sensitive to information adoption. In addition, epidemic information can help to suppress the epidemic diffusion only when individuals' abilities of transforming awareness into actual protective behaviors attain a threshold. In communities dominated by highly literate individuals, a larger information literacy gap can improve awareness acquisition and thus help to suppress the epidemic among the whole group. By contrast, in communities dominated by low information-literate individuals, a smaller information literacy gap can better prevent the epidemic diffusion. This study contributes to the literature by revealing the importance of individuals' heterogeneity of information literacy on epidemic spreading in different communities and has implications for how to inform people when a new epidemic disease emerges.

Sentiment mutation and negative emotion contagion dynamics in social media: a case study on the Chinese Sina Microblog

Negative emotional contagion along with sentiment mutation through information propagation on social media is critical for mitigating disinformation and directing public opinion for compliance with key public interventions, such as vaccine uptake during a pandemic. Here, we develop a dynamic multiple negative emotional susceptible-forwarding-immune (MNE-SFI) model to examine how negative emotion spreads on social media and how sentiment mutation impacts by fitting the model to real multiple temporal information in messages with sentiments obtained from the Chinese Sina microblog. Emotional choices, meaning that individuals attempting to spread information are not only influenced by the objective emotions embedded in the influential information spread by influencers but also by subjective emotional tendencies, is an essential human behavior for information propagation. Hence, we seek to link the negative emotional contagion in the network at the macroscopic level to the emotional choices of individuals, and model parameters are used at the microcosmic level to measure the “copying” and “mutation” probabilities of negative sentiments in an event. Our results illustrate the emotional choices of users play essential roles in methods for mitigating harmful emotion spread and promoting meaningful emotion diffusion.

An evaluation of twitter datasets from non-pandemic crises applied to regional COVID-19 contexts

In 2020, we have witnessed an unprecedented crisis event, the COVID-19 pandemic. Various questions arise regarding the nature of this crisis data and the impacts it would have on the existing tools. In this paper, we aim to study whether we can include pandemic-type crisis events with general non-pandemic events and hypothesize that including labeled crisis data from a variety of non-pandemic events will improve classification performance over models trained solely on pandemic events. To test our hypothesis we study the model performance for different models by performing a cross validation test on pandemic only held-out sets for two different types of training sets, one containing only pandemic data and the other a combination of pandemic and non-pandemic crisis data, and comparing the results of the two. Our results approve our hypothesis and give evidence of some crucial information propagation upon inclusion of non-pandemic crisis data to pandemic data. © 2021 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.

The role of proactive behavior on COVID-19 infordemic in the Chinese Sina-Microblog: a modeling study.

In order to avoid forming an information cocoon, the information propagation of COVID-19 is usually created through the action of "proactive search", an important behavior other than "reactive follow". This behavior has been largely ignored in modeling information dynamics. Here, we propose to fill in this gap by proposing a proactive-reactive susceptible-discussing-immune (PR-SFI) model to describe the patterns of co-propagation on social networks. This model is based on the forwarding quantity and takes into account both proactive search and reactive follow behaviors. The PR-SFI model is parameterized by data fitting using real data of COVID-19 related topics in the Chinese Sina-Microblog, and the model is calibrated and validated using the prediction accuracy of the accumulated forwarding users. Our sensitivity analysis and numerical experiments provide insights about optimal strategies for public health emergency information dissemination.