Characterizing super-spreading in microblog: An epidemic-based information propagation model

Complexity of Government response to COVID-19 pandemic: a perspective of coupled dynamics on information heterogeneity and epidemic outbreak

This study aims at modeling the universal failure in preventing the outbreak of COVID-19 via real-world data from the perspective of complexity and network science. Through formalizing information heterogeneity and government intervention in the coupled dynamics of epidemic and infodemic spreading, first, we find that information heterogeneity and its induced variation in human responses significantly increase the complexity of the government intervention decision. The complexity results in a dilemma between the socially optimal intervention that is risky for the government and the privately optimal intervention that is safer for the government but harmful to the social welfare. Second, via counterfactual analysis against the COVID-19 crisis in Wuhan, 2020, we find that the intervention dilemma becomes even worse if the initial decision time and the decision horizon vary. In the short horizon, both socially and privately optimal interventions agree with each other and require blocking the spread of all COVID-19-related information, leading to a negligible infection ratio 30 days after the initial reporting time. However, if the time horizon is prolonged to 180 days, only the privately optimal intervention requires information blocking, which would induce a catastrophically higher infection ratio than that in the counterfactual world where the socially optimal intervention encourages early-stage information spread. These findings contribute to the literature by revealing the complexity incurred by the coupled infodemic-epidemic dynamics and information heterogeneity to the governmental intervention decision, which also sheds insight into the design of an effective early warning system against the epidemic crisis in the future.

Modeling the Influence of Fake Accounts on User Behavior and Information Diffusion in Online Social Networks

The mechanisms of information diffusion in Online Social Networks (OSNs) have been studied extensively from various perspectives with some focus on identifying and modeling the role of heterogeneous nodes. However, none of these studies have considered the influence of fake accounts on human accounts and how this will affect the rumor diffusion process. This paper aims to present a new information diffusion model that characterizes the role of bots in the rumor diffusion process in OSNs. The proposed SIhIbR model extends the classical SIR model by introducing two types of infected users with different infection rates: the users who are infected by human (Ih) accounts with a normal infection rate and the users who are infected by bot accounts (Ib) with a different diffusion rate that reflects the intent and steadiness of this type of account to spread the rumors. The influence of fake accounts on human accounts diffusion rate has been measured using the social impact theory, as it better reflects the deliberate behavior of bot accounts to spread a rumor to a large portion of the network by considering both the strength and the bias of the source node. The experiment results show that the accuracy of the SIhIbR model outperforms the SIR model when simulating the rumor diffusion process in the existence of fake accounts. It has been concluded that fake accounts accelerate the rumor diffusion process as they impact many people in a short time.

Public engagement in government officials' posts on social media during coronavirus lockdown

BACKGROUND

Social media has been a common platform to disseminate health information by government officials during the COVID-19 pandemic. However, little is known about the determinants of public engagement in officials' posts on social media, especially during lockdown.

OBJECTIVES

This study aims to investigate how the public engages in officials' posts about COVID-19 on social media and to identify factors influencing the levels of engagement.

METHODS

A total of 511 adults aged 18 or over completed an online questionnaire during lockdown in Iraq. Levels of engagement in officials' posts on social media, trust in officials and compliance of government instructions were assessed.

RESULTS

Fear of COVID-19 and trust in officials were positively associated with compliance of government instructions. Trust in officials was also associated with active engagement in officials' posts on social media, including commenting, posting and sharing of the posts.

CONCLUSIONS

Trust in government has been established during the COVID-19 pandemic. Public engagement in officials' posts is crucial to reinforce health policies and disseminate health information.

Effect of decay behavior of information on disease dissemination in multiplex network

The diseases dissemination always brings serious problems in the economy and livelihood issues. It is necessary to study the law of disease dissemination from multiple dimensions. Information quality about disease prevention has a great impact on the dissemination of disease, that is because only the real information can inhibit the dissemination of disease. In fact, the dissemination of information involves the decay of the amount of real information and the information quality becomes poor gradually, which will affect the individual's attitude and behavior towards disease. In order to study the influence of the decay behavior of information on disease dissemination, in the paper, an interaction model between information and disease dissemination is established to describe the effect of the decay behavior of information on the coupled dynamics of process in multiplex network. According to the mean-field theory, the threshold condition of disease dissemination is derived. Finally, through theoretical analysis and numerical simulation, some results can be obtained. The results show that decay behavior is a factor that greatly affects the disease dissemination and can change the final size of disease dissemination. The larger the decay constant, the smaller final size of disease dissemination. In the process of information dissemination, emphasizing key information can reduce the impact of decay behavior.

User Trust Inference in Online Social Networks: A Message Passing Perspective

Online social networks are vital environments for information sharing and user interactivity. To help users of online social services to build, expand, and maintain their friend networks or webs of trust, trust management systems have been deployed and trust inference (or more generally, friend recommendation) techniques have been studied in many online social networks. However, there are some challenging issues obstructing the real-world trust inference tasks. Using only explicit yet sparse trust relationships to predict user trust is inefficient in large online social networks. In the age of privacy-respecting Internet, certain types of user data may be unavailable, and thus existing models for trust inference may be less accurate or even defunct. Although some less interpretable models may achieve better performance in trust prediction, the interpretability of the models may prevent them from being adopted or improved for making relevant informed decisions. To tackle these problems, we propose a probabilistic graphical model for trust inference in online social networks in this paper. The proposed model is built upon the skeleton of explicit trust relationships (the web of trust) and embeds various types of available user data as comprehensively-designed trust-aware features. A message passing algorithm, loop belief propagation, is applied to the model inference, which greatly improves the interpretability of the proposed model. The performance of the proposed model is demonstrated by experiments on a real-world online social network dataset. Experimental results show the proposed model achieves acceptable accuracy with both fully and partially available data. Comparison experiments were conducted, and the results show the proposed model’s promise for trust inference in some circumstances.

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.

Modeling and analyzing cross-transmission dynamics of related information co-propagation

The dissemination of one public hot event is usually affected by some related information, and the implication of co-propagation by different information is critical for the integrated analysis. To help in designing effective communication strategies during the whole event, we propose the cross-transmission susceptible-forwarding-immune (CT-SFI) model to describe the dynamics of co-propagation particularly with focus on the cross-transmission effects. This model is based on the forwarding quantity and takes into account the behavior that users may have a strong attraction or continuous attraction within or without an active time after contacting one information. Data fitting using the real data of Chinese Sina-microblog can accurately parameterize the model and parameter sensitivity analysis gives some strategies for co-propagation.

The COVID-19 social media infodemic

We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number [Formula: see text] for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors' amplification.

Host-membrane interacting interface of the SARS coronavirus envelope protein: Immense functional potential of C-terminal domain

The Envelope (E) protein in SARS Coronavirus (CoV) is a small structural protein, incorporated as part of the envelope. A major fraction of the protein has been known to be associated with the host membranes, particularly organelles related to intracellular trafficking, prompting CoV packaging and propagation. Studies have elucidated the central hydrophobic transmembrane domain of the E protein being responsible for much of the viroporin activity in favor of the virus. However, newer insights into the organizational principles at the membranous compartments within the host cells suggest further complexity of the system. The lesser hydrophobic Carboxylic-terminal of the protein harbors interesting amino acid sequences- suggesting at the prevalence of membrane-directed amyloidogenic properties that remains mostly elusive. These highly conserved segments indicate at several potential membrane-associated functional roles that can redefine our comprehensive understanding of the protein. This should prompt further studies in designing and characterizing of effective targeted therapeutic measures.

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The SARS CoV Envelope protein is a small structural protein of the virus, responsible for viroporin like activity.

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Membrane- E protein interaction provides an useful insight into gaining mechanistic insight into its viroporin functions.

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The central hydrophobic transmembrane domain of E protein, known to affect ion-channel formation.

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The C-terminal region of the protein show further potential host-membrane directed functional roles.

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The highly conserved amyloidogenic amino acid stretches of the C-terminal suggest for its contribution to CoV propagation.

Quantify the role of superspreaders -opinion leaders- on COVID-19 information propagation in the Chinese Sina-microblog

BACKGROUD

Effective communication of accurate information through social media constitutes an important component of public health interventions in modern time, when traditional public health approaches such as contact tracing, quarantine and isolation are among the few options for the containing the disease spread in the population. The success of control of COVID-19 outbreak started from Wuhan, the capital city of Hubei Province of China relies heavily on the resilience of residents to follow public health interventions which induce substantial interruption of social-economic activities, and evidence shows that opinion leaders have been playing significant roles in the propagation of epidemic information and public health policy and implementations.

METHODS

We design a mathematical model to quantify the roles of information superspreaders in single specific information which outbreaks rapidly and usually has a short duration period, and to examine the information propagation dynamics in the Chinese Sina-microblog. Our opinion-leader susceptible-forwarding-immune (OL-SFI) model is formulated to track the temporal evolution of forwarding quantities generated by opinion leaders and normal users.

RESULTS

Data fitting from the real data of COVID-19 obtained from Chinese Sina-microblog can identify the different contact rates and forwarding probabilities (and hence calculate the basic information forwarding reproduction number of superspreaders), and can be used to evaluate the roles of opinion leaders in different stages of the information propagation and the outbreak unfolding.

CONCLUSIONS

The parameterized model can be used to nearcast the information propagation trend, and the model-based sensitivity analysis can help to explore important factors for the roles of opinion leaders.

Identifying and quantifying potential super-spreaders in social networks

Quantifying the nodal spreading abilities and identifying the potential influential spreaders has been one of the most engaging topics recently, which is essential and beneficial to facilitate information flow and ensure the stabilization operations of social networks. However, most of the existing algorithms just consider a fundamental quantification through combining a certain attribute of the nodes to measure the nodes' importance. Moreover, reaching a balance between the accuracy and the simplicity of these algorithms is difficult. In order to accurately identify the potential super-spreaders, the CumulativeRank algorithm is proposed in the present study. This algorithm combines the local and global performances of nodes for measuring the nodal spreading abilities. In local performances, the proposed algorithm considers both the direct influence from the node's neighbourhoods and the indirect influence from the nearest and the next nearest neighbours. On the other hand, in the global performances, the concept of the tenacity is introduced to assess the node's prominent position in maintaining the network connectivity. Extensive experiments carried out with the Susceptible-Infected-Recovered (SIR) model on real-world social networks demonstrate the accuracy and stability of the proposed algorithm. Furthermore, the comparison of the proposed algorithm with the existing well-known algorithms shows that the proposed algorithm has lower time complexity and can be applicable to large-scale networks.

Social Media Usage During Disasters: Exploring the Impact of Location and Distance on Online Engagement

Social media play an important role in emergency management. The location of citizens and distance from a disaster influence the social media usage patterns. Using the Tianjin Port Explosion, we apply the correlation analysis and regression analysis to explore the relationship between online engagement and location. Citizens' online engagement is estimated by social media. Three dimensions of the psychological distance - spatial, temporal, and social distances - are applied to measure the effects of location and distance. Online engagement is negatively correlated to such 3 kinds of the distance, which indicates that citizens may pay less attention to a disaster that happens at a far away location and at an area of less interaction or at a relatively long period of time. Furthermore, a linear model is proposed to measure the psychological distance. The quantification relationship between online engagement and psychological distance is discussed. The result enhances our understanding of social media usage patterns related to location and distance. The study gives a new insight on situation awareness, decision-making during disasters.