Rumor Spreading Research Articles

Dynamics and event-triggered control of rumor propagation model with saturated incidence and time delay on heterogeneous networks

Considering the impact of user’s cognitive ability in rumor propagation, this paper proposes a new susceptible infection recovery (2SIR) rumor propagation model with saturation incidence and time delay on heterogeneous networks. First, the existence of the equilibria is discussed by using mean field theory and basic reproduction number. Second, the dynamic behaviors of the rumor-free and persistent equilibria are analyzed via the local linearization method, Lyapunov stability theory and irreducible matrix properties. Furthermore, an event-triggered impulsive control strategy is proposed to control the rumor spreaders and some corresponding control conditions are given. Finally, some numerical simulation results and a practical example are presented to verify the correctness of the theoretical results and reflect the impact of cognitive ability and time delay on rumor propagation.

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.

Dynamics and Control of a Novel Discrete Internet Rumor Propagation Model in a Multilingual Environment

In the Internet age, the development of intelligent software has broken the limits of multilingual communication. Recognizing that the data collected on rumor propagation are inherently discrete, this study introduces a novel SIR discrete Internet rumor propagation model with the general nonlinear propagation function in a multilingual environment. Then, the propagation threshold R0 is obtained by the next-generation matrix method. Besides, the criteria determining the spread or demise of rumors are obtained by the stability theory of difference equations. Furthermore, combined with optimal control theory, prevention and refutation mechanisms are proposed to curb rumors. Finally, the validity and applicability of the model are demonstrated by numerical simulations and a real bilingual rumor case study.

Para-athletes are exposed to bullying as much as non-disabled athletes

ABSTRACT Objectives Exposure to bullying and high levels of anxiety are important determinants of the quality and duration of athletes’ experiences of success. The aim of this study was to determine which behaviors are most commonly perceived as bullying by para athletes and whether the exposure to bullying or levels of trait anxiety differ between para athletes and non-disabled athletes. Methods It is a cross-sectional study. Two groups consisting of professional para athletes (n = 104) and non-disabled athletes (n = 129) between the ages of 18–45 affiliated to the national athletics federation were included in the study. The questionnaire included information about demographics and the two scales, the Negative Acts Questionnaire-Revised (NAQ-R) and the Sport Anxiety Scale-2 (SAS-2). Results Withholding information that could affect performance and spreading rumors (item 1,5) were the most frequently reported bullying behaviors. No statistically significant difference was found between para athletes and non-disabled athletes or between male and female genders in terms of total or any subdimension NAQ-R scores (p > 0.05). SAS-2 total and SAS-2-worry subdimension scores were significantly lower in the para athletes (p = 0.018 and p = 0.020). Total and all SAS-2 subdimension scores were significantly higher in female athletes (p < 0.05). Conclusion The knowledge about bullying exposure in para-athletes is quite limited. The para-athlete group has fewer opportunities than the non-disabled group in terms of financial opportunities, physical conditions, media coverage and many other aspects. However, in our study, they reported bullying exposure at a similar rate with the non-disabled group. Although there is a similar level of exposure, it is clear that the results will not be similar in the two groups due to the different psychological bases. According to the results of this study, it is thought that stakeholders who have responsibility for the para-athlete group should be further educated.

Dynamics of Rumour Spread with Moral Etiquette Class

Dynamics of Rumour Spread with Moral Etiquette Class

The Role of Artificial Intelligence Techniques in Spreading Rumors and Fake News, and Their Effect on National Security

The Role of Artificial Intelligence Techniques in Spreading Rumors and Fake News, and Their Effect on National Security

Timeliness-aware rumor sources identification in community-structured dynamic online social networks

Identifying rumor sources in online social networks (OSNs) plays a crucial role in controlling the spread of rumors and mitigating their damage. However, the community structure of OSNs and timeliness of rumors increase the complexity of accurately characterizing rumor-spreading behavior, making it extremely challenging to identify the source of rumor in OSNs. Conventional studies on rumor source identification often overlook the community structure of OSNs and timeliness of rumors. This paper proposes a rumor sources identification framework that take these two aspects into consideration. First, we transfer the snapshots of a dynamic OSN into a community-structured dynamic OSN through 4 meticulously designed phases. Second, instead of considering a constant influence of rumors in traditional techniques, we introduce a time-varying dynamic propagation parameter to quantify the timeliness of rumor, and apply the propagation parameter to establish a microscopic rumor spreading model. This process addresses the issue of modeling the timeliness of rumor. Third, we adopt sensor-based observation techniques to collect the propagation information of rumor, and a hybrid sensor deployment strategy is designed to improve the efficiency of information collection. Fourth, we propose an algorithm for identifying single and multiple rumor sources in community-structured dynamic OSN, this algorithm initiates with the utilization of some criteria to analyze the gathered propagation information and estimate the suspicious communities that harbor the source of rumors. Subsequently, it applies maximum likelihood estimation method within each community to determine the ultimate source of the rumor. Experimental results on real-world and synthetic networks indicate that our method can accurately identify the real rumor sources. To the best of our knowledge, the proposed method is the first that can be used to identify rumor sources in community-structured dynamic OSNs.

Rumor Forwarding Prediction Model on Weibo Based on Uncertain Time Series

The rapid spread of rumors on social media is mainly caused by individual retweets. This paper applies uncertain time series analysis to analyze a rumor retweeting behavior on Weibo. First, the rumor forwarding is modeled using uncertain time series, including order selection, parameter estimation, residual analysis, uncertain hypothesis test and forecast and the validity of using uncertain time series analysis is further supported by analyzing the characteristics of the residual plot. The experimental results show that the uncertain time series analysis can better predict the next stage of rumor forwarding. The results of the study have important practical significance for rumor management and the management of social media information dissemination.

Interactive effects of trust in government information and perceived value on Chinese consumers' actual purchasing behavior toward contaminated seafood

Previous studies have separately examined the roles of trust in government information and perceived value in consumer food choices; however, the interactive effects of trust in government information and perceived value remain unknown. Therefore, this study explores the joint effects of trust in government information and perceived value on shaping consumers' actual food purchase behavior after food contamination. A logit model was used to estimate the interactive effects. The results from a sample of 710 Chinese consumers indicated that a lack of trust continues to make consumers concerned about the safety of seafood, which, together with the spread of internet rumors, leads to the fact that trust in government information does not directly affect consumers' actual purchase behavior. However, it can also indirectly influence purchase behavior through perceived risks and benefits. Specifically, trust in government information reduces risk perception among low-income consumers and enhances benefit perception among low-risk city consumers, thus enhancing purchasing behavior. Moreover, we find city heterogeneity in its impact on consumer purchasing behavior. Specifically, in low-risk cities, it significantly promotes consumer purchasing behavior, however, in high-risk cities, it does not directly affect consumer purchasing behavior. Overall, trust in government information does not directly affect consumer purchasing behavior. Perceived value is a key factor affecting consumer purchasing behavior. An interactive effect exists between trust in government information and perceived value on consumer purchasing behaviors. These results highlight the role of perceived value in amplifying the effects of trust in government information. These findings have significant implications for seafood producers and policymakers. Effective strategies should encompass both the provision of perceived value and the cultivation of trust in government information to promote consumer choices of contaminated food.

Digital character assassination in the Jordanian law

One of the rights inherent in a legal personality is the ability to maintain the individual's dignity and treat himself with respect, regardless of his social status. The rise of cybercrime has made it possible to launch digital attacks on someone's honor or reputation, creating a need for rules that limit what can be posted on personal pages and websites. The adoption of Cybercrime Law No. 17 of 2023 shows that Jordanian lawmakers have been proactive. Through the use of a descriptive and inductive analytical approach, this research demonstrated the uniqueness of this legislation, which prohibits the distribution or attribution of any works aimed at defaming a person’s reputation. Presenting an incorrect image of the targets and isolating them socially is the deliberate moral destruction of a person, organization, institution, or social group through spreading rumours, fabricating accusations, and manipulating data and facts. This procedure is similar to the deliberate assassination of individuals. Lawmakers in Jordan have tightened penalties for the perpetrators of this crime and those who helped commit it. The study found that although the goal of character assassination and the crime of digital defamation is to harm the victim's reputation and dignity, there is a significant difference. Character assassination differs from digital defamation in that it requires a specific goal: the moral destruction and social isolation of the targeted individual. This paper aims to clarify the term character assassination, analyze the legislative text that explicitly criminalizes it, identify the elements of this crime and how it is committed, verify the criminal protection procedures decided by the Jordanian legislator, and explain the reasons that prove these crimes. This paper relied on the descriptive approach in studying the phenomenon. Character assassination and gathering information related to this field.

A stochastic rumor spreading model with event-triggered discontinuous feedback control in multilingual online networks

A stochastic rumor spreading model with event-triggered discontinuous feedback control in multilingual online networks

Graph Contrastive Learning With Feature Augmentation for Rumor Detection

While online social media brings convenience to people’s communication, it has also caused the widespread spread of rumors and brought great harm. Recent deep-learning approaches attempt to identify rumors by engaging in interactive user feedback. However, the performance of these models suffers from insufficient and noisy labeled data. In this article, we propose a novel rumor detection model called graph contrastive learning with feature augmentation (FAGCL), which injects noise into the feature space and learns contrastively by constructing asymmetric structures. FAGCL takes user preference and news embedding as the initial features of the rumor propagation tree and then adopts a graph attention network to update node representations. To obtain the graph-level representation for rumor classification, FAGCL fuses multiple pooling techniques. Moreover, FAGCL adopts graph contrastive learning as an auxiliary task to constrain the representation consistency. Contrastive learning on noisy data mines the supervision information of the rumor propagation tree itself, making the model more robust and effective. Results on two real-world datasets demonstrate that our proposed FAGCL model achieves significant improvements over the baseline models.

Transformer-based models for combating rumours on microblogging platforms: a review

The remarkable success of Transformer-based embeddings in natural language tasks has sparked interest among researchers in applying them to classify rumours on social media, particularly microblogging platforms. Unlike traditional word embedding methods, Transformers excel at capturing a word’s contextual meaning by considering words from both the left and right of a word, resulting in superior text representations ideal for tasks like rumour detection on microblogging platforms. This survey aims to provide a thorough and well-organized overview and analysis of existing research on implementing Transformer-based models for rumour detection on microblogging platforms. The scope of this study is to offer a comprehensive understanding of this topic by systematically examining and organizing the existing literature. We start by discussing the fundamental reasons and significance of automating rumour detection on microblogging platforms. Emphasizing the critical role of text embedding in converting textual data into numerical representations, we review current approaches to implement Transformer models for rumour detection on microblogging platforms. Furthermore, we present a novel taxonomy that covers a wide array of techniques and approaches employed in the deployment of Transformer-based models for identifying misinformation on microblogging platforms. Additionally, we highlight the challenges associated with this field and propose potential avenues for future research. Drawing insights from the surveyed articles, we anticipate that promising results will continue to emerge as the challenges outlined in this study are addressed. We hope that our efforts will stimulate further interest in harnessing the capabilities of Transformer models to combat the spread of rumours on microblogging platforms.

Rumor detection in networks based on the BERT-CNN approach

As of June 2023, China has witnessed a surge in internet users, reaching 1.079 billion, with an internet penetration rate of 76.4%. This rapid expansion of social media and online communication channels has not only enhanced the ease of information sharing but has also intensified the spread of rumors and misinformation. This trend poses substantial threats to social order and public safety. In response to this challenge, this work introduces an innovative approach to detect online rumors, utilizing the BERT-CNN methodology. By harnessing the contextual understanding capabilities of the BERT model and the efficient feature extraction prowess of CNN models, the study has crafted a framework that seamlessly integrates these technologies. This integrated approach proves effective in accurately identifying and categorizing rumors in online data. Through extensive experiments conducted on various real-world datasets, a comparative performance analysis had conducted against existing technologies. The results reveal significant enhancements in key metrics such as accuracy, recall, and F1 score when compared to traditional methods. This affirms the efficacy of the BERT-CNN approach in the domain of online rumor detection. This research not only introduces a fresh perspective to rumor detection technology but also furnishes a robust tool to tackle the challenges associated with the dissemination of rumors in the digital age.

Dynamic analysis of a rumor propagation model based on a familiarity mechanism and refuters

The spread of rumors not only causes massive economic losses, but also seriously threatens social harmony and stability of human livelihoods. As a result, preventing rumors from spreading is critical. Individuals' familiarity with rumors and refuters has a significant impact on their spread. We develop a rumor propagation model that introduces familiar mechanisms and refuters, as well as demonstrating the existence of an equilibrium point and its local and global asymptotic stability in the model. We also calculate the model's basic regeneration number. Subsequently, we select two appropriate control variables: the contact rate between the ignorant, who are familiar with the rumor, and the disseminators; and the probability that the disseminators will become refuters. We discover that the control effect is optimal when the first variable is minimum and the second variable is maximum. Numerical simulation verifies the accuracy of the obtained results, and sensitivity analysis of the parameters is investigated. According to the research, an increase in the proportion of disseminators who become refuters will slow the spread of rumors, while an increase in the population of ignorant people who are familiar with rumors contacting the disseminators will accelerate the spread of rumors.

Assessing the Influence of Unverified Instagram Blog Content on the Cognitive and Behavioral Pattern of Undergraduates: An Empirical Study of Obafemi Awolowo University (OAU)

The proliferation of unverified Instagram blog content in Nigeria raises concerns about the credibility and trustworthiness of information disseminated through this medium and unverified contents on Instagram blogs lack proper fact-checking, leading to the spread of misinformation, rumors, and fake news which is why this research investigated the impact of unverified Instagram blogs’ contents on Nigerian Undergraduates in Obafemi Awolowo University, Ile-Ife. The study adopted the gratifications theory, which suggests that media users play an active role in choosing and using the media and that the user has alternate choices to satisfy their need. The study also adopted a survey research method to conduct the study through the use of questionnaire. The research sample size turned out to be 543 respondents but only 403 respondents completed the questionnaire from three selected faculties, which translated to a response rate of over 74%. The statistical analysis was carried out using the statistical package for social sciences (SPSS 20). The study findings revealed high level of agreement among respondents regarding their engagement with Instagram blogs, mirrored by a weighted mean of 4.04. Additionally, the study also revealed a moderate level of agreement among with a weighted mean of 3.57 regarding respondent’s perception and attitude towards Instagram blogs’ contents. Moreover, the majority of respondent agreed that exposure to unverified information on instagram can negatively influence students’ academic performance, reflected by a weighted mean of 3.99. The research recommended that a supportive online environment should be prioritized as a fundamental intervention strategy to enhance the cognitive and behavioral pattern of undergraduate students’ such as awareness campaigns, digital programs and peer support network as well as inaugurating promotion of positive engagement is essential to counteract the prevalence of objectifying content on social media platforms. This study, therefore, concludes that unverified information on social media platform such as instagram can influence students’ engagement, perceptive, attitude and academic performance.

Prediction of influential nodes in social networks based on local communities and users’ reaction information

Identifying influential nodes is one of the basic issues in managing large social networks. Identifying influence nodes in social networks and other networks, including transportation, can be effective in applications such as identifying the sources of spreading rumors, making advertisements more effective, predicting traffic, predicting diseases, etc. Therefore, it will be important to identify these people and nodes in social networks from different aspects. In this article, a new method is presented to identify influential nodes in the social network. The proposed method utilizes the combination of users’ social characteristics and their reaction information to identify influential users. Since the identification of these users in the large social network is a complex process and requires high processing power and time, clustering and identifying communities have been used in the proposed method to reduce the complexity of the problem. In the proposed method, the structure of the social network is divided into its constituent communities and thus the problem of identifying influential nodes (in the entire network) turns into several problems of identifying an influential node (in each community). The suggested method for predicting the nodes first predicts the links that may be created in the future and then identifies the influential nodes based on an iterative strategy. The proposed algorithm uses the criteria of centrality and influence domain to identify this category of users and performs the identification process both at the community and network levels. The efficiency of the method has been evaluated using real databases and the results have been compared with previous works. The results demonstrate that the proposed method provides a more suitable performance in detecting the influential nodes and is superior in terms of accuracy, recall and processing time.

The dynamic analysis of the rumor spreading and behavior diffusion model with higher-order interactions

Rumor spreading occurs not only between two individuals but also among multiple individuals or influenced by groups. However, pairwise interactions in complex networks are insufficient to describe this process. In this study, we propose a rumor spreading model with higher-order interactions, in which the rumor propagation process is represented by simplicial complexes. By selecting the propagation coefficient and time delay as the threshold, the model shows richer dynamic behaviors, such as the bi-stability, discontinuous transition, forward bifurcation, backward bifurcation, Hopf bifurcation, and periodic oscillation. Besides, for exploring the collective behavior induced by rumors, the rumor synchronization model is first established by applying the adaptive feedback mechanism, which erects a theoretical bridge between rumor spreading and behavior diffusion. Moreover, an improved optimal control strategy considering system gains is performed to curb rumor diffusion. Numerical simulations suggest that rumor spreading models with higher-order interactions are more consistent with actual data than network-based ones. Our results may shed some light on comprehending higher-order interactions in rumor spreading and the coupling between rumor and behavior, and provide a promising approach to control rumors and irrational behaviors.

Specific challenges of researching stress in the context of quiet political repression

Political repression beneath the threshold of criminal prosecution is a phenomenon of past and present, predominantly authoritarian, regimes. This so-called quiet repression includes measures such as the limitation of freedom of speech, surveillance of (perceived) political opponents, or the spreading of rumors to socially isolate targets. Such experiences of chronic stress show significant psychological and physiological health consequences in affected individuals. However, societal awareness of quiet repression measures remains limited, hindering victims' access to support and complicating healthcare interventions. In the current paper, we present the design of a study conducted with individuals who endured quiet repression measures in the former German Democratic Republic (GDR), a socialist state closely aligned with the former Soviet Union. We discuss the challenges encountered over the course of the study, and present the solutions found. Although every study population has their unique challenges and needs, we wish to inform future sensitive research within the realm of quiet political repression. Given the limited understanding of the phenomenon, there is a pressing need for further investigation aiming to improve acceptance and care for past and future victims.

Exploring deep learning: Preventing HIV through social media data

This paper explores the potential of deep learning in analyzing social media data to identify and support populations at high risk for HIV. With the widespread use of social media, there is an opportunity to leverage this data source for public health research and intervention. Deep learning, a subset of machine learning that utilizes artificial neural networks to analyze complex data, offers a promising approach to extract meaningful insights from large volumes of social media data. The paper begins with an overview of the HIV epidemic and the importance of early detection and prevention efforts. It then introduces deep learning and its applications in public health, highlighting its ability to analyze unstructured data such as text, images, and videos. A literature review is conducted to examine previous studies on using social media data for health surveillance and the applications of deep learning in this context. The review discusses the challenges and limitations of using social media data for public health research, including issues related to privacy, data bias, and algorithm transparency. The methodology section outlines the data collection process, including the sources of social media data such as Twitter and Facebook, and the preprocessing steps to clean and prepare the data for analysis. The section also describes the deep learning models selected for analyzing social media content and the evaluation metrics used to assess their performance. Case studies are presented to illustrate the application of deep learning in identifying HIV-related discussions on social media platforms, analyzing images for signs of risky behavior, and tracking the spread of HIV-related rumors and misinformation. Ethical considerations related to privacy, data bias, and algorithm transparency are discussed, along with recommendations for future research and application. The paper concludes with a summary of key findings and a call to action for further exploration and implementation of deep learning in public health.