Rumor Control Research Articles

Analysis of Rumor Propagation Model Based on Coupling Interaction Between Official Government and Media Websites

The COVID-19 pandemic has not only brought a virus to the public, but also spawned a large number of rumors. The Internet has made it very convenient for media websites to record and spread rumors, while the official government, as the subject of rumor control, can release rumor-refutation information to reduce the harm of rumors. Therefore, this study took into account information-carrying variables, such as media websites and official governments, and expanded the classic ISR rumor propagation model into a five-dimensional, two-level rumor propagation model that interacts between the main body layer and the information layer. Based on the constructed model, the mean field equation was obtained. Through mathematical analysis, the equilibrium point and the basic reproduction number of rumors were calculated. At the same time, stability analysis was conducted using the Routh Hurwitz stability criterion. Finally, a numerical simulation verified that when the basic regeneration number was less than 1, rumors disappeared in the system; when the basic regeneration number was greater than 1, rumors continued to exist in the system and rumors erupted. The executive power of the official government to dispel rumors, that is, the effectiveness of the government, played a decisive role in suppressing the spread of rumors.

MHPD: An efficient evaluation method for influence maximization on hypergraphs

Influence maximization problem (IM) has been extensively applied in fields such as viral marketing, rumor control, and infectious disease prevention. However, research on the IM problem has primarily focused on ordinary networks, with limited attention devoted to hypergraphs. Firstly, we propose an efficient evaluation method, i.e., the multiple-hop probability dissemination method (MHPD), aiming to accurately and rapidly evaluate the propagation capacity of selected nodes. The MHPD method is a universal approach that is applicable to various network types, including hypergraphs, and it accommodates multiple probabilistic spreading models. Then, based on MHPD, we propose two novel algorithms for solving IM problem, i.e., MHPD-greedy and MHPD-heuristic. MHPD-greedy employs MHPD to evaluate the marginal benefits of nodes and iteratively adds nodes with the maximum marginal benefit to the seed set. MHPD-heuristic utilizes MHPD to evaluate the propagation capacity of each node and select the top-K nodes as the seeds. Experimental results on eight real-world hypergraphs and eight synthetic hypergraphs demonstrate that MHPD is capable of achieving near-identical accuracy in evaluating the propagation capability of both individual nodes and seed sets, while only incurring an average time overhead of merely 0.25 % compared to Monte Carlo method. In comparison with seven cutting-edge algorithms, MHPD-heuristic demonstrates superior solution accuracy. Notably, MHPD-greedy maintains 98.81 % of the solution accuracy while requiring only 0.11 % of the time cost compared to the Greedy method. Furthermore, MHPD-greedy achieves an average performance improvement of 23.4 % over the best baseline algorithm.

Topic-aware Masked Attentive Network for Information Cascade Prediction

Predicting information cascades holds significant practical implications, including applications in public opinion analysis, rumor control, and product recommendation. Existing approaches have generally overlooked the significance of semantic topics in information cascades or disregarded the dissemination relations. Such models are inadequate in capturing the intricate diffusion process within an information network inundated with diverse topics. To address such problems, we propose a neural-based model using Topic-Aware Masked Attentive Network for Information Cascade Prediction (ICP-TMAN) to predict the next infected node of an information cascade. First, we encode the topical text into user representation to perceive the user-topic dependency. Next, we employ a masked attentive network to devise the diffusion context to capture the user-context dependency. Finally, we exploit a deep attention mechanism to model historical infected nodes for user embedding enhancement to capture user-history dependency. The results of extensive experiments conducted on three real-world datasets demonstrate the superiority of ICP-TMAN over existing state-of-the-art approaches.

Identifying influential spreaders in complex network based on the node’s weight and spreading probability

Identifying influential spreaders is a crucial aspect of network science with various applications, including rumor control, viral marketing and epidemic spread limitation. Despite the availability of various methods for identifying these spreaders in complex networks, there remains a fundamental question regarding their accurate and discriminative identification. To address the issues and account for each node’s propagation ability, we propose an algorithm to identify influential spreaders based on the node’s weight and spreading probability (NWSP) for identifying influential spreaders. The effectiveness of the proposed method is evaluated using the Susceptible–Infected–Recovered (SIR) model, Kendall’s Tau ([Formula: see text]) and monotonicity. The proposed method is compared with several well-known metrics, including degree centrality, K-shell decomposition, betweenness centrality, closeness centrality, eigenvector centrality and the centrality method based on node spreading probability (SPC), in ten real networks. Experimental results demonstrate the superiority ability of the proposed algorithm to accurately and discriminatively identify influential spreaders.

HEDV-Greedy: An Advanced Algorithm for Influence Maximization in Hypergraphs

Influence maximization (IM) has shown wide applicability in various fields over the past few decades, e.g., viral marketing, rumor control, and prevention of infectious diseases. Nevertheless, existing research on IM primarily focuses on ordinary networks with pairwise connections between nodes, which fall short in the representation of higher-order relations. Influence maximization on hypergraphs (HIM) has received limited research attention. A novel evaluation function, which aims to evaluate the spreading influence of selected nodes on hypergraphs, i.e., expected diffusion value on hypergraph (HEDV), is proposed in this work. Then, an advanced greedy-based algorithm, termed HEDV-greedy, is proposed to select seed nodes with maximum spreading influence on the hypergraph. We conduct extensive experiments on eight real-world hypergraph datasets, benchmarking HEDV-greedy against eight state-of-the-art methods for the HIM problem. Extensive experiments conducted on real-world datasets highlight the effectiveness and efficiency of our proposed methods. The HEDV-greedy algorithm demonstrates a marked reduction in time complexity by two orders of magnitude compared to the conventional greedy method. Moreover, HEDV-greedy outperforms other state-of-the-art algorithms across all datasets. Specifically, under conditions of lower propagation probability, HEDV-greedy exhibits an average improvement in solution accuracy of 25.76%.

Exploring the evolutionary game of rumor control based on prospect theory

Exploring the evolutionary game of rumor control based on prospect theory

Rumor propagation model with neutral state

The development of social networks provides a broad platform for the dissemination of information and also leads to the proliferation of fake news and false information, which we collectively refer to as rumors. The spread of rumors causes unnecessary panic and loss to individuals and society. To reduce the negative impacts of rumors, an appropriate rumor control strategy is necessary. To come up with some reasonable strategies, we need to have a clearer understanding of the spread of rumors. In this paper, we analyze crowd attitudes during the spreading of rumors by setting the misinformation prevalent progress on the social network as a dynamic system. Considering that most people do not have a clear supportive or opposing attitude when exposed to rumor information, we introduce a new group, stiflers who remain neutral, based on the infectious disease model scheme. By deriving the mean-field equation describing the rumor propagation process, we judge the stability of the constructed model. Finally, we use the model to fit the real-world data related to COVID-19, and based on this, we discuss the properties of the model and propose related strategies.

Finding critical nodes in a complex network from information diffusion and Matthew effect aggregation

The identification of critical nodes is a crucial aspect in studying the spread of diseases, vaccination strategies, power grid robustness, advertisement placement, and rumor control. Consequently, this topic has become of immense interest in recent times. In the last decade, numerous methods have been proposed for identifying critical nodes, but each method has its own strengths and weaknesses, which can be attributed to the complex nature of networks and different scenarios. Therefore, it is unlikely that a single method can be applicable to all networks. To address the need for improved critical node identification in propagation scenarios, we propose a new approach called IDME (Information Diffusion and Matthew Effect aggregation). This approach is inspired by the real-world phenomenon of information diffusion and the Matthew effect. IDME simulates the dissemination of information in the real world and obtains information from multilayer neighbors, which is then aggregated using the Matthew effect. By considering its own information as well as that of its multilayer neighbors, IDME can more accurately identify critical nodes in networks while maintaining low time complexity. Experimental results on numerous real-world networks demonstrate that the IDME approach is effective in detecting critical nodes in networks and outperforms representative algorithms on most networks.

A multiple features fusion-based social network node importance measure for rumor control

A multiple features fusion-based social network node importance measure for rumor control

Soft rumor control in mobile instant messengers

Mobile Instant Messengers (MIMs) are vastly used for communication and information sharing in recent years. However, interesting features of these applications such as group-based communication and broadcasting in channels cause rumors also to be spread in MIM networks more quickly than ordinary social networks. Although there are lots of works on modeling, analysis, and controlling rumor dissemination in social networks, the mentioned features of MIMs are not almost considered. In this paper we propose a new model for soft rumor control in MIMs that considers rumor propagation in groups and channels. By soft rumor control we mean measures for enhancing the people’s knowledge and awareness against the rumor to persuade them avoiding rumor dissemination. We suggest two soft rumor control mechanisms including a provenance based decision making process and making anti-rumor campaigns. In the first mechanism, in order to improve the ability of users to take proper actions against rumors, they are equipped with rumor provenance information including the level of trust to rumor spreader, reputation of the source of rumor and the degree of credibility of the rumor. In the second mechanism, some MIM users who have more serious concerns about the rumor effects try making an anti-rumor campaign to fight spreading the rumor. The proposed model is formalized as an extended Partially Observable Markov Decision Process (POMDP) to capture the dynamics of rumor propagation and the control mechanisms. To evaluate the proposed model, we conduct a number of extensive agent-based simulation experiments on a synthesized MIM network that show the effectiveness of the proposed mechanisms to control rumor propagation. We also conduct interesting sensitivity analysis to see the effects of different model parameters on the dynamics of the rumor propagation with control mechanisms. The proposed model helps MIM developers to provide facilities to control rumor by collective wisdom. Furthermore, it helps people, NGOs, political parties, and so on to improve their rumor fighting strategies by making properly designed anti-rumor campaigns.

Extending influence maximization by optimizing the network topology

Influence maximization has recently obtained significant attention in advertising and rumor control on a social network. However, most existing works on influence maximization focus on mining seed nodes with high influence, while the timeliness of information diffusion in dynamic social networks has not been fully explored, resulting in the diffusion effect is not up to the expected level. To fill this gap, we investigate how to maximize the influence spread range of seed set A by adding k latent edges to dynamic network Gt, which is called Dynamic Edge Addition (DEA). For this purpose, we design an Activation Probability-aware (AP) framework for adding edges to the network. Specifically, AP contains three modules: graph generation, influence estimation of nodes, and edge addition. The module of graph generation is used to obtain graph structure. In the module of influence estimation, we employ a baseline Influence Maximization via Martingales (IMM) algorithm and then improve it to make the seed more effective by setting a limit to the traversal depth of sampling. To capture the dynamic temporal changes of the network as soon as possible, we dynamically update the influence of nodes according to the propagation probability of the path. In the module of edge addition, we propose a novel strategy of edge addition by finding the shortest path length between the potential users and seed users, and realize the strategy in linear time. Extensive experiments on real-world datasets are conducted and the results show the superiority of the AP over other baselines.

SIRA: a model for propagation and rumor control with epidemic spreading and immunization for healthcare 5.0.

Healthcare social networks have a significant role in providing connected and personalized healthcare environment with real-time capabilities. However, building resilient, robust and technology-driven healthcare 5.0 has its own barriers. Especially with social media’s high susceptibility to rumors and fake news, these networks can harm the society. Many researchers have been investigating the process of information diffusion, and it has been one of the most intriguing issues in network analysis. Modeling rumor propagation is one of the prominent researched topics in recent years. Traditional models assume that rumor propagation happens only in one direction, where only supporters are supposed to be active, whereas, in a real-life situation, both supporters and deniers of the information operate simultaneously. In this paper, we introduce a model for the recovery of nodes in a setting where rumor propagation and rumor control happen simultaneously. We propose the Susceptible-Infected-Recovered-Anti-spreader model based on the notion of spreading of epidemics and also its applications to modeling the propagation of rumors and control of rumor. Our model assumes people have multiple forms of reactions to rumor, either posting it, deleting it or announcing the rumor as fake. This paper also suggests how the model can act as a simulation method to compare two node centrality algorithms where spreaders chosen from one centrality algorithm try to spread the rumor, and the anti-spreaders chosen from other centrality try to dispel the rumor and vice versa. We simulate the proposed algorithm on different weighted and unweighted real-world network datasets and establish that the experimental results agrees with the proposed model.

Health-Related Rumor Control through Social Collaboration Models: Lessons from Cases in China during the COVID-19 Pandemic.

Widely spread health-related rumors may mislead the public, escalate social panic, compromise government credibility, and threaten public health. Social collaboration models that maximize the functions and advantages of various agents of socialization can be a promising way to control health-related rumors. Existing research on health-related rumors, however, is limited in studying how various agents collaborate with each other to debunk rumors. This study utilizes content analysis to code the text data of health-related rumor cases in China during the COVID-19 pandemic. The study found that socialized rumor-debunking models could be divided into the following five categories: the government-led model, the media-led model, the scientific community-led model, the rumor-debunking platform-led model, and the multi-agent collaborative model. In addition, since rumors in public health crises often involve different objects, rumor refutation requires various information sources; therefore, different rumor-debunking models apply. This study verifies the value of socialized collaborative rumor debunking, advocates and encourages the participation of multiple agents of socialization and provides guidance for establishing a collaborative rumor-debunking model, thereby promoting efficient rumor-debunking methods and improving the healthcare of society.

Interaction between official institutions and influential users of rumor control in online social networks.

Online interactions have become major channels for people to obtain and disseminate information during the new normal of COVID-19, which can also be a primary platform for rumor propagation. There are many complex psychological reasons for spreading rumors, but previous studies have not fully analyzed this problem from the perspective of the interaction between official institutions and influential users. The purpose of this study is to determine optimal strategies for official institutions considering the impact of two different influential user types (trolls and reputed personalities) by designing two game-theoretic models, namely “Rumor Clarification and Interaction Model” and “Rumor Verification and Interaction Model,” which can, respectively decide whether to clarify and when to clarify. The results of this article show that clarification strategies can be decided according to the characteristics of rumors and the influential user’s reactions. Meanwhile, publishing verified information prevents trolls’ “loophole advantages” and prevents reputed personalities from spreading false information due to the vague authenticity of rumors. Results also show that the verification strategy is limited by cost, period, and verification index.

Trust in government buffers the negative effect of rumor exposure on people's emotions.

Although we are surrounded by various kinds of rumors during the coronavirus disease pandemic, little is known about their primary content, what effect they might have on our emotions, and the potential factors that may buffer their effect. Combining qualitative (study 1 extracted 1907 rumors from top rumor-refuting websites using the Python Web Crawler and conducted content analysis) and quantitative (study 2 conducted an online survey adopting a three-wave design, N = 444) research methods, the current study revealed that government-related rumors accounted for the largest proportion of rumors during the outbreak stage of the pandemic and were positively associated with the public’s negative emotions. We also found that trust in government negatively moderated the relationship between government-related rumors and negative emotions. Specifically, when people had low trust in government, exposure to government-related rumors was positively associated with negative emotions. However, when people had high trust in government, the association was non-significant. For positive emotions, we found no significant effects of government-related rumors. The findings highlight the importance of rumor control during public emergencies and cultivating public trust in government in the long run.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12144-022-03508-x.

Modeling and quantifying the influence of rumor and counter-rumor on information propagation dynamics

Today, the spread of Internet rumors has become increasingly easier, however, once they spread widely, the network environment and even the social order will be broken down. Therefore, to help in designing effective rumor control strategies, we propose the two-stage rumor propagation dynamics model, in which the susceptible/educated-infected-recovered (SO-S/EIR) dynamics model is used to characterize the first stage and the susceptible/educated-infected-denied-recovered (C-S/EIDR) dynamics model is to characterize the second stage. In our model, according to the difference of rumor spreaders' confidence in the rumor, we divide them into super-infected and normal-infected, moreover, the global model incorporates vigilantes and rumor debunkers to reflect the diversity of public opinions. Our model is based on the forwarded quantity in the Chinese Sina-microblog and takes into account the impact of interactions between forwarding users, own cognitions, and own knowledge level on the propagation of the rumor. Our Model-based analytic and numerical analyses show that forwarding and transfer probabilities in our model are in accordance with the actual accident situation, besides, our sensitivity analyses describe important factors that affect the spread of the rumor in support of decision strategies to refute the rumor.

Gendered forms of authority and solidarity in the management of ethno-religious conflicts

ABSTRACT In both Ambon in Indonesia and Jos in Nigeria, existing communal ethno-religious tensions quickly spiraled into uncontrolled violence, and people organized to counteract conflict escalation through the development of conflict management mechanisms. Many of these mechanisms draw on gender as a resource in various intersectional ways. Micro-analytical literature on conflict tends to remain gender blind, ignoring a potent social force in conflict dynamics, while feminist literature on conflict focuses either on norms, symbols, discourse, and representations, or on women's efforts for peace. This article thus seeks to address an existing gap in the literature by exploring the intersectional relationships between gender dynamics and conflict management at the local level in Ambon and Jos. Our analysis draws on more than 110 interviews and focus group discussions. Engaging in a paired comparison between the two cities, we identify inductively two intersectionally gendered logics of ethno-religious conflict management present in both settings: deployments of gendered authority (in women's practices of “checkmating” and men's efforts of rumor control) and of gendered cross-community solidarity (in interfaith markets, rituals, and dialogues). This article ultimately contributes to expanding, deepening, and challenging existing research on conflict, suggesting that adopting a feminist, constructivist micro-level lens allows for the excavation of important and often invisibilized gendered logics of conflict management.

Computing Influential Nodes Using the Nearest Neighborhood Trust Value and PageRank in Complex Networks.

Computing influential nodes gets a lot of attention from many researchers for information spreading in complex networks. It has vast applications, such as viral marketing, social leader creation, rumor control, and opinion monitoring. The information-spreading ability of influential nodes is greater compared with other nodes in the network. Several researchers proposed centrality measures to compute the influential nodes in a complex network, such as degree, betweenness, closeness, semi-local centralities, and PageRank. These centrality methods are defined based on the local and/or global information of nodes in the network. However, due to their high time complexity, centrality measures based on the global information of nodes have become unsuitable for large-scale networks. Very few centrality measures exist that are based on the attributes between nodes and the structure of the network. We propose the nearest neighborhood trust PageRank (NTPR) based on the structural attributes of neighbors and nearest neighbors of nodes. We define the measure based on the degree ratio, the similarity between nodes, the trust values of neighbors, and the nearest neighbors. We computed the influential nodes in various real-world networks using the proposed centrality method. We found the maximum influence by using influential nodes with SIR and independent cascade methods. We also compare the maximum influence of our centrality measure with the existing basic centrality measures.

A Study on the Effectiveness of Rumor Control via Social Media Networks to Alleviate Public Panic About COVID-19.

The COVID-19 outbreak triggered a massive spread of unverified news on social media and has become a source of rumors. This paper studies the impact of a virtual rumor control center (RCC) on Weibo user behavior. The collected COVID-19 breaking news stories were divided into positive, negative, and neutral categories, while the moderating effect model was used to analyze the influence of anti-rumor on user behavior (forwarding, liking, and commenting). Our research found that rumor refuting does not directly affect user behavior but does have an indirect moderating effect. Rumor refuting has a profound impact on user forwarding behavior in cases of positive and negative news. Specifically, when the epidemic becomes more serious, the role of rumor refuting becomes critical, and vice versa. Refuting rumors reduces user willingness to forward positive or negative news, with more impact on negative news. Time lag analysis shows a significant moderation of unverified news within 72 h of refuting rumors but indicated an apparent weakening trend over time. Furthermore, we discovered non-linear feature and counter-cyclical phenomena in the moderating effect of rumor refutation.

Modeling Rumor Diffusion Process With the Consideration of Individual Heterogeneity: Take the Imported Food Safety Issue as an Example During the COVID-19 Pandemic

At present, rumors appear frequently in social platforms. The rumor diffusion will cause a great impact on the network order and the stability of the society. So it's necessary to study the diffusion process and develop the rumor control strategies. This article integrates three heterogeneous factors into the SEIR model and designs an individual state transition mode at first. Secondly, based on the influencing factors such as the trust degree among individuals, an individual information interaction mode is constructed. Finally, an improved SEIR model named SEIR-OM model is established, and the diffusion process of rumors are simulated and analyzed. The results show that: (1) when the average value of the interest correlation is greater, the information content deviation is lower, but the rumor diffusion range will be wider. (2) The increase of the average network degree intensifies influence of rumors, but its impact on the diffusion has a peak. (3) Adopting strategies in advance can effectively reduce the influence of rumors. In addition, the government should enforce rumor-refuting strategies right after the event. Also, the number of rumor-refuting individuals must be paid attention to. Finally, the article verifies the rationality and effectiveness of the SEIR-OM model through the real case.