Rumors In Networks Research Articles

A rumor propagation model based on nuclear fission

The propagation of online rumors is rapid, and its propagation mechanism has always been a research difficulty. In this paper, the network rumors are compared to the nuclear fission process, and a network rumors propagation model is constructed. First, the initial online rumors are compared to neutrons, uranium nuclei are compared to individual rumor receivers, and fission barriers are compared to individual active propagation thresholds. Second, the process of nuclear fission is analyzed and the degree of energy accumulation is used to compare the social impact of online rumors. Finally, the online rumor propagation model based on nuclear fission is constructed. Through experimental simulation, it has been shown that compared to classic infectious disease models, this model can better describe the propagation process of rumors in social networks. Through research and analysis, suggestions for suppressing rumors have been proposed, providing new ideas and references for future researchers.

Difference in Rumor Dissemination and Debunking Before and After the Relaxation of COVID-19 Prevention and Control Measures in China: Infodemiology Study.

The information epidemic emerged along with the COVID-19 pandemic. While controlling the spread of COVID-19, the secondary harm of epidemic rumors to social order cannot be ignored. The objective of this paper was to understand the characteristics of rumor dissemination before and after the pandemic and the corresponding rumor management and debunking mechanisms. This study aimed to provide a theoretical basis and effective methods for relevant departments to establish a sound mechanism for managing network rumors related to public health emergencies such as COVID-19. This study collected data sets of epidemic rumors before and after the relaxation of the epidemic prevention and control measures, focusing on large-scale network rumors. Starting from 3 dimensions of rumor content construction, rumor propagation, and rumor-refuting response, the epidemic rumors were subdivided into 7 categories, namely, involved subjects, communication content, emotional expression, communication channels, communication forms, rumor-refuting subjects, and verification sources. Based on this framework, content coding and statistical analysis of epidemic rumors were carried out. The study found that the rumor information was primarily directed at a clear target audience. The main themes of rumor dissemination were related to the public's immediate interests in the COVID-19 field, with significant differences in emotional expression and mostly negative emotions. Rumors mostly spread through social media interactions, community dissemination, and circle dissemination, with text content as the main form, but they lack factual evidence. The preferences of debunking subjects showed differences, and the frequent occurrence of rumors reflected the unsmooth channels of debunking. The χ2 test of data before and after the pandemic showed that the P value was less than .05, indicating that the difference in rumor content before and after the pandemic had statistical significance. This study's results showed that the themes of rumors during the pandemic are closely related to the immediate interests of the public, and the emotions of the public accelerate the spread of these rumors, which are mostly disseminated through social networks. Therefore, to more effectively prevent and control the spread of rumors during the pandemic and to enhance the capability to respond to public health crises, relevant authorities should strengthen communication with the public, conduct emotional risk assessments, and establish a joint mechanism for debunking rumors.

Harmfulness metrics in digital twins of social network rumors detection in cloud computing environment

Social network rumor harm metric is a task to score the harm caused by a rumor by analyzing the spreading range of the rumor, the users affected, the repercussions caused, etc., and then the harm caused by the rumor. Rumor hazard metric models can help rumor detection digital twins to understand and analyze user behaviors and assist social network network managers to make more informed decisions. However, there is a lack of models that can quantify the harm of rumors and automated harm metric models in rumor detection digital twins. To address this issue, this paper proposes an innovative social network rumor harm metric based on rumor propagation knowledge and a large language model (LLM), RSK-T5. The method first completes the joint task of rumor comment stance detection and sentiment analysis to capture critical features of rumor propagation. Then, this knowledge is used in the pre-training process of LLM to improve the model's understanding of rumor propagation patterns. Finally, the fine-tuning phase focuses on the hazard metrics task to improve the generalization energy. We compare with some existing variants of rumor detection methods, and experimental results demonstrate that RSK-T5 achieves the lowest MSE scores on three well-known rumor detection datasets. The ablative learning work demonstrates the effectiveness of RSK-T5's knowledge of two rumor spreads.

RBOTUE: Rumor Blocking Considering Outbreak Threshold and User Experience

When a rumor breaks out in online social network (OSN), it can lead to significant negative impact on human society, especially in the context of public emergencies, such as pandemic. Toward restraining rumor outbreak in OSN, one of the effective containment measures is to block influential users to minimize the spread of rumors. However, most of previous efforts ignore the imbalance between the cost and effect of rumor suppression. To fill this gap, from the perspective of public opinion crisis, a dynamic rumor spread model called PISIR model is established, which takes into account the overall popularity and individual tendency of rumors. Based on this model, two rumor blocking algorithms considering outbreak threshold and user experience, called 1-Hop and 2-Hop RBOTUE algorithms, are proposed, respectively. In the algorithms, a hyperbolic discount effect-based user experience mode is introduced as the constraint to ensure the user experience in OSN, then the blocking strategy is implemented on the selected subset of nodes to keep the rumor spread scale always below the outbreak warning line. The experimental results in two synthetic networks and four real OSNs indicate that both 1-Hop and 2-Hop RBOTUE algorithms have lower rumor infection rate and require less number of blocked nodes, which means that proposed algorithms can achieve better blocking performance with less restraining cost of rumors in mainstream social networks, and the two algorithms also have different adaptability for different OSNs.

The Civil Law Regulatory Path of Internet Rumor Governance in the Context of Big Data

Relying on the Internet, network rumors have the characteristics of fast spreading speed, great influence and strong harm, which will have a certain impact on people's production and life, therefore, they have also entered the vision of the government governance, but the delineation of the boundaries between the governance of network rumors and the protection of the freedom of expression has caused a lot of social controversy. At present, China's network rumor governance system faces legal dilemmas such as low legal status, legislative gaps, lagging legislation, fragmentation of the legal system, and the strong color of public law; China's network rumor governance system also faces practical dilemmas such as the difficulty of private remedies, and the impossibility of recovering the damaged reputation in a timely manner. Therefore, in order to better manage rumors, maintain the peace and stability of the country and society, and protect the immediate interests of the people, we should start from legislation and control rumors by law. Firstly, the definition of rumor should be clarified through legislation, secondly, the types and methods of punishment should be clarified through legislation, and finally, the legal system of Internet rumor management in China should be improved through multi-channel management of Internet rumors, so as to realize the grand goal of China under the rule of law.

A local rewiring strategy for accelerating information propagation under complex propagation mechanism

Controlling the spread of information or rumors in complex networks has always been a subject of extensive attention and research. Generally speaking, taking the most influential node in the network as the propagation spreader or cutting off the most important link in the propagation process can maximize the promotion or suppression of information transmission. Therefore, many scholars have put forward many measurement methods to identify specific nodes and links in the network, and their theories are becoming more and more mature. However, in the face of large-scale networks, most of these methods are exposed to high computational complexity, low accuracy, and only applicable to specific networks. Some scholars try to enhance (inhibit) the dissemination of information by adding or deleting links, but the structural characteristics of the original network will be changed to a large extent, which also means that some basic functions related to the network have been changed. In this work, we propose a rewiring strategy to accelerate information propagation under complex propagation mechanism, which increases the number of redundant links by rewiring the second-order neighborhood of the initial propagation node. At the same time, the rewiring strategy preserves the degree distribution of nodes and modularization feature of the original network to a large extent. We apply our strategy to simulate the complex propagation of the information propagation (IST(I)) model on different networks. The simulation results show that the rewired network can accelerate the propagation of information more effectively, and the number of spreaders in the network increases significantly, and the propagation rate of information is enhanced significantly. In addition, this paper also proposed for the first time an index that quantifies both the transmission ability and infectivity of the information at the micro level, namely the number of regenerated infections which was optimally estimated by statistical inference. We also compare the changes of various topological characteristics of the network before and after the rewiring. We find that the structural characteristics related to the propagation dynamics of the network after the rewiring are significantly enhanced.

How to improve the rumor-confutation ability of official rumor-refuting account on social media: A Chinese case study

Accelerated by the widespread use of social networks, the propagation of rumors has been a destructive factor in the Internet environment. Chinese governments at different administrative levels have established official rumor-refuting accounts on social media to confute rumors, but the spread of rumors in social networks remains a significant concern due to the immature construction of rumor-refuting accounts. To identify the deficiencies of official rumor-refuting accounts at different administrative levels and how to improve their rumor-confutation ability, this paper applies a multi-criteria decision-making (MCDM) method to evaluate Chinese official rumor-refuting accounts on social media, then discusses the differences among them to provide targeted suggestions for improvement. Based on the 10,253 rumor-refuting articles published by 26 Chinese official rumor-refuting accounts on WeChat (a combined social media of Twitter and WhatsApp in China) in 2021, this paper first proposes a targeted rumor-refuting ability evaluation criteria system based on Source-Message-Channel-Receiver (SMCR) theory of communication. Then it applies the Stochastic Multi-criteria Acceptability Analysis-Analytic Hierarchy Process Sort-II with intuitionistic trapezoidal fuzzy numbers (SMAA-AHPSort-II-ITFN) method to evaluate and compare the official rumor-refuting accounts at different administrative levels. The evaluation results show significant differences in the ability of various rumor-refuting accounts to confute rumors. Finally, specific suggestions are provided for improving the ability of official rumor-refuting accounts.

A Dual Rumor Spreading Model with Consideration of Fans versus Ordinary People

The spread of rumors in online social networks (OSNs) has caused a serious threat to the normal social order. In order to describe the rumor-spreading dynamics in OSNs during emergencies, a novel model with consideration of fans versus ordinary people is proposed in this paper. In contrast to previous studies, we consider the case that two rumors exist simultaneously. It is assumed that one is an entertainment rumor that fans care about, and the other is a common rumor. First, we derive the mean-field equations that describe the dynamics of this dual rumor propagation model and obtain the threshold parameter. Secondly, after finding the necessary and sufficient conditions for the existence of equilibriums, we examine the equilibrium’s local and global stability. Finally, simulations are used to explain how various parameters affect the process of spreading rumors.

A review of rumor detection techniques in social networks

Social networks have accelerated the speed and scope of information dissemination. However, the lack of regulation and freedom of speech on social platforms has resulted in the widespread dissemination of the unverified message. Therefore, rapid and effective detection of social network rumors is essential to purify the network environment and maintain public security. Currently, the defects of rumor detection technology are that the detection time is too long and the timeliness is poor. In addition, the differences based on specific regions or specific fields will lead to deviations in the training dataset. In this paper, firstly, the definition of rumor is described, and the current problems and detection process of rumor detection are described; Secondly, introduce different data acquisition methods and analyze their advantages and disadvantages; Thirdly, according to the development of rumor detection technology, the existing rumor detection methods of artificial, machine learning and deep learning are analyzed and compared; Finally, the challenges of social network rumor detection technology are summarized.

Debunking Rumors in Networks

We study the diffusion of a true and a false message (the rumor) in a social network. Upon hearing a message, individuals may believe it, disbelieve it, or debunk it through costly verification. Whenever the truth survives in steady state, so does the rumor. Communication intensity in itself is irrelevant for relative rumor prevalence, and the effect of homophily depends on the exact verification process and equilibrium verification rates. Our model highlights that successful policies in the fight against rumors increase individuals’ incentives to verify. (JEL D83, D85, L82, Z13)

Investigation of Effective Factors on Rumor Management in the Context of Social Networks in Tourism Industry Marketing

Effective management of rumors in social networks is very important for government tourism organizations to maintain their reputation, attract tourists and promote their economic growth. The main purpose of the current research is to investigate the effective factors on rumor management in the context of social networks in the tourism industry. The research method is descriptive-survey. The statistical population was the employees of organizations and recreation and tourism centers in the public sector. The sampling method is a multi-stage cluster and the questionnaires were randomly distributed in each stage. To investigate the research questions, relevant statistical tests were used, including the path analysis test, and factor analysis was used to determine the convergent and divergent validity of the research questionnaire. The statistical software used was SPSS and PLS. The findings showed that the effective factors on the management of rumors in social networks in the field of government tourism in Iran included malicious rumors and innocent rumors. It includes reference support and building trust. According to the results of the research, it was suggested that government tourism managers can use them to benefit government tourism organizations by managing innocent rumors in social networks, and also by managing malicious rumors that spread on the network. are in the field of tourism, they can control their negative impact on the achievement of the organization's goals.

Research on the Harm and Countermeasures of Network Rumors

Research on the Harm and Countermeasures of Network Rumors

SEDIS—A Rumor Propagation Model for Social Networks by Incorporating the Human Nature of Selection

The explosive evolution of the internet has paved the path for the rise of social networks, which can help people connect remotely. Currently, social networks are commonly used for sharing thoughts, feelings, information, and personal life, which vary from individual to individual. The world has witnessed a tremendous increase in social media usage in the last decade, and more people are expected to spend their time online after the COVID-19 pandemic. This increases the rapid propagation of rumors and fake news within societies and communities. On one end, social networks act as an excellent platform for digital marketing and sharing information. However, on the other end, social network rumors and fake news create a significant impact on society, including riots. To study and analyze social network rumors, several mathematical rumor propagation epidemic models have been proposed. The majority are related to disease-spreading epidemic models and reject the human aspect of social selection. This paper introduces a new mathematical rumor propagation model for social networks by incorporating the human psychological aspect of selection as a separate state. Our mathematical analysis and computational simulation proved that the model exists within the system. It was also proven that the system is always non-negative and there always exists a solution in the system. Our implementation of an intervention mechanism within the discrete compartmental model simulation proved the necessity of an effective interference that can help to prevent the implications of uncontrolled rumor dissemination within social networks.

Pattern study and parameter identification of a reaction-diffusion rumor propagation system with time delay

In this paper, a reaction-diffusion dynamic model with time delay is established under the background of network rumors, and the behavior of susceptible users and infected users in spreading rumors on the network is analyzed to help us study the mechanism of rumor propagation. Firstly, the Turing instability condition of delay approximate system is studied. Secondly, based on the amplitude equation method, the theoretical conditions of different patterns near Turing bifurcation point are given, and different types of Turing patterns are divided. Then, the accuracy of the theory is verified by numerical simulation under different parameters, and the effectiveness of the model is verified by Monte Carlo simulation. In addition, the effects of different networks and time-delay conditions on pattern formation are studied. Finally, in order to help the model apply to the actual situation, the parameter identification and optimization control of the system are carried out.

A novel rumor detection with multi-objective loss functions in online social networks

COVID-19 quickly swept across the world, causing the consequent infodemic represented by the rumors that have brought immeasurable losses to the world. It is imminent to achieve rumor detection as quickly and accurately as possible. However, the existing methods either focus on the accuracy of rumor detection or set a fixed threshold to attain early detection that unfortunately cannot adapt to various rumors. In this paper, we focus on textual rumors in online social networks and propose a novel rumor detection method. We treat the detection time, accuracy and stability as the three training objectives, and continuously adjust and optimize this objective instead of using a fixed value during the entire training process, thereby enhancing its adaptability and universality. To improve the efficiency, we design a sliding interval to intercept the required data rather than using the entire sequence data. To solve the problem of hyperparameter selection brought by integration of multiple optimization objectives, a convex optimization method is utilized to avoid the huge computational cost of enumerations. Extensive experimental results demonstrate the effectiveness of the proposed method. Compared with state-of-art counterparts in three different datasets, the recognition accuracy is increased by an average of 7%, and the stability is improved by an average of 50%.

Psychological Analysis on Network Rumors Spread in COVID-19 Epidemic.

Since the outbreak of the COVID-19 epidemic, related rumors have been spread rapidly on social media, which has seriously affected public normal life and caused mental panic and anxiety. This paper, by analyzing the spread of online rumors and their impact on public mental health during the COVID-19 epidemic, aims to put forward relevant suggestions for dealing with rumors and improving public mental health. According to the actual rumors collected during the epidemic period as the content of the questionnaire, an online rumor survey was carried out. A total of 1,200 questionnaires were distributed, and 1,165 valid questionnaires were collected. Among the sources of information about the epidemic, 78.2% of the respondents obtained epidemic information through online channels. 633 respondents were concerned about the epidemic situation, accounting for 54.3%. The respondents have different susceptibility to the epidemic sources, epidemic spread, epidemic treatment, epidemic influence, and epidemic prevention. They account for 37.2%, 43. 4%, 45.2%, 39.4%, and 46.1% respectively. 646 respondents expressed great anxiety about the epidemic situation, accounting for 55.5%. The source (t=26.33, P<0.05), transmission (t=28.32, P<0.05), prevention (t=35.36, P<0.05), treatment (t=40.32, P<0.05), and impact of the epidemic (t=42.01, P<0.05) can significantly predict the transmission intention. To control the spread of epidemic rumors and reduce the negative impact of such information on public mental health, the government and various walks of life need to collaborate to solve the problem from the aspects of information disclosure system, supervision means, and network communication system.

Studies of Chordal Ring Networks via Double Metric Dimensions

Locating the sources of information spreading in networks including tracking down the origins of epidemics, rumors in social networks, and online computer viruses, has a wide range of applications. In many situations, identifying where an epidemic started, or which node in a network served as the source, is crucial. But it can be difficult to determine the root of an outbreak, especially when data are scarce and noisy. The goal is to find the source of the infection by analysing data provided by only a limited number of observers, such as nodes that can indicate when and where they are infected. Our goal is to investigate where the least number of observers should be placed, which is similar to how to figure out the minimal doubly resolving sets in the network. In this paper, we calculate the double metric dimension of chordal ring networks C R n 1,3,5 by describing their minimal doubly resolving sets.

F‐SWIR: Rumor Fick‐spreading model considering fusion information decay in social networks

SummaryThe spread of rumors has a major negative impact on social stability. Traditional rumor spreading models are mostly based on infectious disease models and do not consider the influence of individual differences and the network structure on rumor spreading. In this paper, we propose a rumor Fick‐spreading model that integrates information decay in social networks. The dissemination of rumors in social networks is random and uncertain and is affected by the dissemination capabilities of individuals and the network environment. The rumor Fick‐transition coefficient and Fick‐transition gradient are defined to determine the influence of the individual transition capacity and the network environment on rumor propagation, respectively. The Fick‐state transition probability is used to describe the probability of change of an individual's state. Moreover, an information decay function is defined to characterize the self‐healing probability of individuals. According to the different roles and reactions of users during rumor dissemination, the user state and the rumor dissemination rules among users are refined, and the influence of the network structure on the rumor dissemination is ascertained. The experimental results demonstrate that the proposed model outperforms other rumor spread models.

Early detection of rumors based on source tweet-word graph attention networks.

The massively and rapidly spreading disinformation on social network platforms poses a serious threat to public safety and social governance. Therefore, early and accurate detection of rumors in social networks is of vital importance before they spread on a large scale. Considering the small-world property of social networks, the source tweet-word graph is decomposed from the global graph of rumors, and a rumor detection method based on graph attention network of source tweet-word graph is proposed to fully learn the structure of rumor propagation and the deep representation of text contents. Specifically, the proposed model can adequately capture the contextual semantic association representation of source tweets during the propagation and extract semantic features. For the data sparseness of the early stage of information dissemination, text attention mechanism based on opinion similarity can aggregate and capture more tweet propagation structure features to help improve the efficiency of early detection of rumors. Through the analysis of the experimental results on real public datasets, the rumor detection performance of the proposed method is better than that of other baseline methods. Especially in the early rumor detection tasks, the proposed method can detect rumors with an accuracy of nearly 90% in the early stage of information dissemination. And it still has good robustness with noise interference.

An entropy-based method to control COVID-19 rumors in online social networks using opinion leaders

- In the ongoing COVID-19 pandemic, people spread various COVID-19-related rumors and hoaxes that negatively influence human civilization through online social networks (OSN). The proposed research addresses the unique and innovative approach to controlling COVID-19 rumors through the power of opinion leaders (OLs) in OSN. The entire process is partitioned into two phases; the first phase describes the novel Reputation-based Opinion Leader Identification (ROLI) algorithm, including a unique voting method to identify the top-T OLs in the OSN. The second phase describes the technique to measure the aggregated polarity score of each posted tweet/post and compute each user's reputation. The empirical reputation is utilized to calculate the user's trust, the post's entropy, and its veracity. If the experimental entropy of the post is lower than the empirical threshold value, the post is likely to be categorized as a rumor. The proposed approach operated on Twitter, Instagram, and Reddit social networks for validation. The ROLI algorithm provides 91% accuracy, 93% precision, 95% recall, and 94% F1-score over other Social Network Analysis (SNA) measures to find OLs in OSN. Moreover, the proposed approach's rumor controlling effectiveness and efficiency is also estimated based on three standard metrics; affected degree, represser degree, and diffuser degree, and obtained 26%, 22%, and 23% improvement, respectively. The concluding outcomes illustrate that the influence of OLs is exceptionally significant in controlling COVID-19 rumors.