With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or p...With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.展开更多
Regulation plays a pivotal role in mitigating the spread of rumors, serving as a vital tool for maintaining social stability and facilitating its evolution. A central challenge lies in establishing an effective regula...Regulation plays a pivotal role in mitigating the spread of rumors, serving as a vital tool for maintaining social stability and facilitating its evolution. A central challenge lies in establishing an effective regulatory framework despite limited resources available for combating rumor propagation. To address this challenge, this paper proposes a dynamic and adaptive regulatory system. First, based on observed regulatory patterns in real-world social networks, the rumor propagation process is divided into two distinct phases: regulation and intervention. Regulatory intensity is introduced as an indicator of user state transitions. Unlike traditional, non-adaptive regulatory models that allocate costs uniformly,the adaptive model facilitates flexible cost distribution through a manageable individual regulatory intensity. Moreover,by introducing adaptive strength, the two cost allocation models are integrated within a unified framework, leading to the development of a dynamic model for rumor suppression. Finally, simulation experiments on Barabási–Albert(BA)networks demonstrate that the adaptive regulatory mechanism significantly reduces both the scope and duration of rumor propagation. Furthermore, when traditional non-adaptive regulatory models show limited effectiveness, the adaptive model effectively curbs rumor propagation by optimizing cost allocation between regulatory and intervention processes, and by adjusting per-unit cost benefit differentials.展开更多
Rumor Control(RC),aimed at minimizing the spread of rumors in social networks,is of paramount importance,as the spread of rumors can lead to significant economic losses,societal disruptions,and even widespread panic.T...Rumor Control(RC),aimed at minimizing the spread of rumors in social networks,is of paramount importance,as the spread of rumors can lead to significant economic losses,societal disruptions,and even widespread panic.The RC problem has garnered extensive research attention,however,most existing solutions for rumor control face a trade-off between efficiency and effectiveness,which limits their practical application in real-world scenarios.In this light,this paper studies the Truth-spreading-based Rumor Control(TRC)problem,and introduces the Subgraphbased Greedy algorithm Optimized with CELF(SGOC),which employs subgraph techniques and the CELF strategy,as the basic solution for the TRC problem.To improve the performance of SGOC,we carefully design a shortest path length dictionary SPR and an Immune Nodes Set(INS),leading to the Shortest Path-Based Rumor Control(SPRC)algorithm.To further enhance the SPRC algorithm,we develop a pruning method that accelerates the construction process of INS,proposing the Improved Shortest Path-Based Rumor Control(ISPRC)algorithm,which demonstrates superior efficiency compared to both SPRC and SGOC.Extensive experiments conducted on five real-world datasets,demonstrate the effectiveness and efficiency of the proposed algorithms.展开更多
The proliferation of rumors on social media has caused serious harm to society.Although previous research has attempted to use deep learning methods for rumor detection,they did not simultaneously consider the two key...The proliferation of rumors on social media has caused serious harm to society.Although previous research has attempted to use deep learning methods for rumor detection,they did not simultaneously consider the two key features of temporal and spatial domains.More importantly,these methods struggle to automatically generate convincing explanations for the detection results,which is crucial for preventing the further spread of rumors.To address these limitations,this paper proposes a novel method that integrates both temporal and spatial features while leveraging Large Language Models(LLMs)to automatically generate explanations for the detection results.Our method constructs a dynamic graph model to represent the evolving,tree-like propagation structure of rumors across different time periods.Spatial features are extracted using a Graph Convolutional Network,which captures the interactions and relationships between entities within the rumor network.Temporal features are extracted using a Recurrent Neural Network,which accounts for the dynamics of rumor spread over time.To automatically generate explanations,we utilize Llama-3-8B,a large language model,to provide clear and contextually relevant rationales for the detected rumors.We evaluate our method on two real-world datasets and demonstrate that it outperforms current state-of-the-art techniques,achieving superior detection accuracy while also offering the added capability of automatically generating interpretable and convincing explanations.Our results highlight the effectiveness of combining temporal and spatial features,along with LLMs,for improving rumor detection and understanding.展开更多
In this study, we proposed a deterministic mathematical model that attempts to explain the propagation of a rumor using epidemiological models approach. The population is divided into four classes which consist of ign...In this study, we proposed a deterministic mathematical model that attempts to explain the propagation of a rumor using epidemiological models approach. The population is divided into four classes which consist of ignorant individuals, I(t), spreaders targeting community through media, M(t), spreaders targeting community through verbal communication, G(t) and stiflers, R(t). We explored existence of the equilibria and analyzed its stability. It was established that rumour-free equilibrium E0 is locally asymptotically stable if R0<1;meaning rumor can seize spreading in a population, and unstable if R0>1 leads to new rumor spreading in the population. Numerical simulations of the dynamic model are carried out on the system to confirm the analytical results. We see that the dynamics of rumor spreading show similar behavior to that found in the dynamics of infectious diseases except that the spread depends on the classes of spreader.展开更多
Rumors regarding epidemic diseases such as COVID 19,medicines and treatments,diagnostic methods and public emergencies can have harmful impacts on health and political,social and other aspects of people’s lives,espec...Rumors regarding epidemic diseases such as COVID 19,medicines and treatments,diagnostic methods and public emergencies can have harmful impacts on health and political,social and other aspects of people’s lives,especially during emergency situations and health crises.With huge amounts of content being posted to social media every second during these situations,it becomes very difficult to detect fake news(rumors)that poses threats to the stability and sustainability of the healthcare sector.A rumor is defined as a statement for which truthfulness has not been verified.During COVID 19,people found difficulty in obtaining the most truthful news easily because of the huge amount of unverified information on social media.Several methods have been applied for detecting rumors and tracking their sources for COVID 19-related information.However,very few studies have been conducted for this purpose for the Arabic language,which has unique characteristics.Therefore,this paper proposes a comprehensive approach which includes two phases:detection and tracking.In the detection phase of the study carried out,several standalone and ensemble machine learning methods were applied on the Arcov-19 dataset.A new detection model was used which combined two models:The Genetic Algorithm Based Support Vector Machine(that works on users’and tweets’features)and the stacking ensemble method(that works on tweets’texts).In the tracking phase,several similarity-based techniques were used to obtain the top 1%of similar tweets to a target tweet/post,which helped to find the source of the rumors.The experiments showed interesting results in terms of accuracy,precision,recall and F1-Score for rumor detection(the accuracy reached 92.63%),and showed interesting findings in the tracking phase,in terms of ROUGE L precision,recall and F1-Score for similarity techniques.展开更多
A mathematical model described the propagation of information including rumor and truth presented and its properties investigated. We explored exists of the equilibria, local stability and global asymptotical stabilit...A mathematical model described the propagation of information including rumor and truth presented and its properties investigated. We explored exists of the equilibria, local stability and global asymptotical stability, and obtained the propagation threshold of rumor spreading. Numerical simulation is shown to demonstrate our results. Uncertainty and sensitivity analysis shows the importance of the parameters in our model.展开更多
As new media communication becomes popular and the technological threshold of personal expression has been lowered,the phenomenon of online rumors has also emerged with new communication characteristics as individuals...As new media communication becomes popular and the technological threshold of personal expression has been lowered,the phenomenon of online rumors has also emerged with new communication characteristics as individuals are continuously empowered to communicate.This paper quantifies a rumor spreading event from 2020 to 2021 that generated a lot of discussion.Using a content analysis approach,we examined how different subjects in the online public opinion chaos have influenced the spreading process of the event,and used the study of online rumor spreading mechanism to gain insight into the public opinion governance in the new media era.The study found that media platforms,the parties involved,and the general public as important participants have fully utilized the voice channel of the online communication platform to play a role behind the rumor event.展开更多
In this paper, to study rumor spreading, we propose a novel susceptible-infected-removed （SIR） model by introducing the trust mechanism. We derive mean-field equations that describe the dynamics of the SIR model on ...In this paper, to study rumor spreading, we propose a novel susceptible-infected-removed （SIR） model by introducing the trust mechanism. We derive mean-field equations that describe the dynamics of the SIR model on homogeneous networks and inhomogeneous networks. Then a steady-state analysis is conducted to investigate the critical threshold and the finaJ size of the rumor spreading. We show that the introduction of trust mechanism reduces the final rumor size and the velocity of rumor spreading, but increases the critical thresholds on both networks. Moreover, the trust mechanism not only greatly reduces the maximum rumor influence, but also postpones the rumor terminal time, which provides us with more time to take measures to control the rumor spreading. The theoretical results are confirmed by sufficient numerical simulations.展开更多
With the advent of the information age of networks,the study about rumor propagation in social networks has become increasingly significant.In this paper,a rumor propagation model with nonlinear functions and time del...With the advent of the information age of networks,the study about rumor propagation in social networks has become increasingly significant.In this paper,a rumor propagation model with nonlinear functions and time delay in social networks is proposed.First,according to the nextgeneration matrix method,we work out the basic reproduction number.Second,we discuss the existence of the rumor-prevailing equilibrium points.Third,we demonstrate the stabilities of equilibrium points and analyze the sufficient conditions for Hopf bifurcation.Finally,the correctness of the theory is verified and several vital conclusions are obtained by numerical simulations.展开更多
Based on the characteristics of rumor spreading in online social networks, this paper proposes a new rumor spreading model. This is an improved SIS rumor spreading model in online social networks that combines the tra...Based on the characteristics of rumor spreading in online social networks, this paper proposes a new rumor spreading model. This is an improved SIS rumor spreading model in online social networks that combines the transmission dynamics and population dynamics with consideration of the impact of both of the changing number of online social network users and different levels of user activity. We numerically simulate the rumor spreading process. The results of numerical simulation show that the improved SIS model can successfully characterize the rumor spreading behavior in online social networks. We also give the effective strategies of curbing the rumor spreading in online social networks.展开更多
Based on the infectious disease model with disease latency, this paper proposes a new model for the rumor spreading process in online social network. In this paper what we establish an SEIR rumor spreading model to de...Based on the infectious disease model with disease latency, this paper proposes a new model for the rumor spreading process in online social network. In this paper what we establish an SEIR rumor spreading model to describe the online social network with varying total number of users and user deactivation rate. We calculate the exact equilibrium points and reproduction number for this model. Furthermore, we perform the rumor spreading process in the online social network with increasing population size based on the original real world Facebook network. The simulation results indicate that the SEIR model of rumor spreading in online social network with changing total number of users can accurately reveal the inherent characteristics of rumor spreading process in online social network.展开更多
In order to prevent and control the spread of rumors, the implementation of immunization strategies for ignorant individuals is very necessary, where the immunization usually means letting them learn the truth of rumo...In order to prevent and control the spread of rumors, the implementation of immunization strategies for ignorant individuals is very necessary, where the immunization usually means letting them learn the truth of rumors.Considering the facts that there is always a delay time between rumor spreading and implementing immunization, and that the truth of rumors can also be spread out, this paper constructs a novel susceptible-infected-removed(SIR) model.The propagation dynamical behaviors of the SIR model on homogeneous networks are investigated by using the meanfield theory and the Monte Carlo method. Research shows that the greater the delay time, the worse the immune effect of the immunization strategy. It is also found that the spread of the truth can inhibit to some extent the propagation of rumors, and the trend will become more obvious with the increase of reliability of the truth. Moreover, under the influence of delay time, the existence of nodes' identification force still slightly reduces the propagation degree of rumors.展开更多
In real life, the rumor propagation is influenced by many factors. The complexity and uncertainty of human psychology make the diffusion model more challenging to depict. In order to establish a comprehensive propagat...In real life, the rumor propagation is influenced by many factors. The complexity and uncertainty of human psychology make the diffusion model more challenging to depict. In order to establish a comprehensive propagation model, in this paper, we take some psychological factors into consideration to mirror rumor propagation. Firstly, we use the Ridenour model to combine the trust mechanism with the correlation mechanism and propose a modified rumor propagation model. Secondly, the mean-field equations which describe the dynamics of the modified SIR model on homogenous and heterogeneous networks are derived. Thirdly, a steady-state analysis is conducted for the spreading threshold and the final rumor size. Fourthly, we investigate rumor immunization strategies and obtain immunization thresholds. Next, simulations on different networks are carried out to verify the theoretical results and the effectiveness of the immunization strategies.The results indicate that the utilization of trust and correlation mechanisms leads to a larger final rumor size and a smaller terminal time. Moreover, different immunization strategies have disparate effectiveness in rumor propagation.展开更多
On the multilingual online social networks of global information sharing,the wanton spread of rumors has an enormous negative impact on people's lives.Thus,it is essential to explore the rumor-spreading rules in m...On the multilingual online social networks of global information sharing,the wanton spread of rumors has an enormous negative impact on people's lives.Thus,it is essential to explore the rumor-spreading rules in multilingual environment and formulate corresponding control strategies to reduce the harm caused by rumor propagation.In this paper,considering the multilingual environment and intervention mechanism in the rumor-spreading process,an improved ignorants–spreaders-1–spreaders-2–removers(I2SR)rumor-spreading model with time delay and the nonlinear incidence is established in heterogeneous networks.Firstly,based on the mean-field equations corresponding to the model,the basic reproduction number is derived to ensure the existence of rumor-spreading equilibrium.Secondly,by applying Lyapunov stability theory and graph theory,the global stability of rumor-spreading equilibrium is analyzed in detail.In particular,aiming at the lowest control cost,the optimal control scheme is designed to optimize the intervention mechanism,and the optimal control conditions are derived using the Pontryagin's minimum principle.Finally,some illustrative examples are provided to verify the effectiveness of the theoretical results.The results show that optimizing the intervention mechanism can effectively reduce the densities of spreaders-1 and spreaders-2 within the expected time,which provides guiding insights for public opinion managers to control rumors.展开更多
We introduce the generalized rumor spreading model and investigate some properties of this model on different complex social networks. Despite pervious rumor models that both the spreader-spreader (SS) and the spreade...We introduce the generalized rumor spreading model and investigate some properties of this model on different complex social networks. Despite pervious rumor models that both the spreader-spreader (SS) and the spreader-stifler (SR) interactions have the same rate α, we define α(1) and α(2) for SS and SR interactions, respectively. The effect of variation of α(1) and α(2) on the final density of stiflers is investigated. Furthermore, the influence of the topological structure of the network in rumor spreading is studied by analyzing the behavior of several global parameters such as reliability and efficiency. Our results show that while networks with homogeneous connectivity patterns reach a higher reliability, scale-free topologies need a less time to reach a steady state with respect the rumor.展开更多
In a previous work(2018,Commun.Theor.Phys.70,795–802),a new compartment model for the spreading of rumors was introduced and analyzed.However,only the local asymptotic stability of this model was discussed.In the pre...In a previous work(2018,Commun.Theor.Phys.70,795–802),a new compartment model for the spreading of rumors was introduced and analyzed.However,only the local asymptotic stability of this model was discussed.In the present work,we first provide a rigorous mathematical analysis for the global asymptotic stability(GAS)of the above-mentioned rumor spreading model.By constructing suitable Lyapunov candidate functions,we obtain the GAS of a rumor-free(boundary)equilibrium point and a unique rumor-spreading(positive)equilibrium point.After that,we utilize the approach based on the Lyapunov candidate functions to study the GAS of another rumor spreading model with control strategies,which was proposed in(2022,Physica A 606,128157).As an important consequence,the GAS of the rumor spreading model with control strategies is determined fully without resorting to technical hypotheses used in the benchmark work.Lastly,the theoretical findings are supported by a set of illustrative numerical examples.The obtained results not only improve the ones constructed in the two abovementioned benchmark papers but also can be extended to study the global dynamics of other rumor propagation models in the context of both integer-order and fractional-order derivatives.展开更多
With the rapid development of Internet science and technology, the self-media industry is rising gradually. As an important way of information dissemination, more and more self-media platforms are established and the ...With the rapid development of Internet science and technology, the self-media industry is rising gradually. As an important way of information dissemination, more and more self-media platforms are established and the main body of information communication becomes more complex. The self-media not only brings convenience to people’s life but also brings some negative effects. The self-media has more remarkable characteristics in information dissemination. The birth of self-media makes the network appear more suspicious information that cannot be effectively verified. Internet rumors fly all over the sky, which has caused certain influence on the stability of the society. The prevention and control measures of online rumors from the perspective of self-media are studied in this paper for creating a healthier network environment. Firstly, the concepts of self-media and Internet rumors are briefly summarized. Then, the main characteristics of Internet rumors from the perspective of self-media are analyzed. Finally, the prevention and control measures of online rumors, including strengthening supervision, improving the quality of self-media, and strengthening public identification of rumors, are proposed.展开更多
We study the dynamics of an epidemic-like model for the spread of a rumor on a connecting multi-small-world- network （CM-SWN） model, which represents organizational communication in the real world. It has been shown...We study the dynamics of an epidemic-like model for the spread of a rumor on a connecting multi-small-world- network （CM-SWN） model, which represents organizational communication in the real world. It has been shown that this model exhibits a transition between regimes of localization and propagation at a finite value of network randomness. Here, by numerical means, we perform a quantitative characterization of the evolution in the three groups under two evolution rules, namely the conformity and obeying principles. The variant of a dynamic CM-SWN, where the quenched disorder of small-world networks is replaced by randomly changing connections between individuals in a single network and stable connection by star nodes between networks, is also analysed in detail and compared with a mean-field approximation.展开更多
基金funded by the Hunan Provincial Natural Science Foundation of China(Grant No.2025JJ70105)the Hunan Provincial College Students’Innovation and Entrepreneurship Training Program(Project No.S202411342056)The article processing charge(APC)was funded by the Project No.2025JJ70105.
文摘With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 62266030 and 61863025)。
文摘Regulation plays a pivotal role in mitigating the spread of rumors, serving as a vital tool for maintaining social stability and facilitating its evolution. A central challenge lies in establishing an effective regulatory framework despite limited resources available for combating rumor propagation. To address this challenge, this paper proposes a dynamic and adaptive regulatory system. First, based on observed regulatory patterns in real-world social networks, the rumor propagation process is divided into two distinct phases: regulation and intervention. Regulatory intensity is introduced as an indicator of user state transitions. Unlike traditional, non-adaptive regulatory models that allocate costs uniformly,the adaptive model facilitates flexible cost distribution through a manageable individual regulatory intensity. Moreover,by introducing adaptive strength, the two cost allocation models are integrated within a unified framework, leading to the development of a dynamic model for rumor suppression. Finally, simulation experiments on Barabási–Albert(BA)networks demonstrate that the adaptive regulatory mechanism significantly reduces both the scope and duration of rumor propagation. Furthermore, when traditional non-adaptive regulatory models show limited effectiveness, the adaptive model effectively curbs rumor propagation by optimizing cost allocation between regulatory and intervention processes, and by adjusting per-unit cost benefit differentials.
基金partially supported by Research Programs of Henan Science and Technology Department(252102210022,232102210054)Henan Province Key Research and Development Project(231111212000)+2 种基金Henan Center for Out-standingOverseas Scientists(GZS2022011)Henan Province Collaborative Innovation Center of Aeronautics and Astronautics Electronic Information TechnologyHenan International Joint Laboratory of Aerospace Intelligent Technology and Systems.
文摘Rumor Control(RC),aimed at minimizing the spread of rumors in social networks,is of paramount importance,as the spread of rumors can lead to significant economic losses,societal disruptions,and even widespread panic.The RC problem has garnered extensive research attention,however,most existing solutions for rumor control face a trade-off between efficiency and effectiveness,which limits their practical application in real-world scenarios.In this light,this paper studies the Truth-spreading-based Rumor Control(TRC)problem,and introduces the Subgraphbased Greedy algorithm Optimized with CELF(SGOC),which employs subgraph techniques and the CELF strategy,as the basic solution for the TRC problem.To improve the performance of SGOC,we carefully design a shortest path length dictionary SPR and an Immune Nodes Set(INS),leading to the Shortest Path-Based Rumor Control(SPRC)algorithm.To further enhance the SPRC algorithm,we develop a pruning method that accelerates the construction process of INS,proposing the Improved Shortest Path-Based Rumor Control(ISPRC)algorithm,which demonstrates superior efficiency compared to both SPRC and SGOC.Extensive experiments conducted on five real-world datasets,demonstrate the effectiveness and efficiency of the proposed algorithms.
基金supported by General Scientific Research Project of Zhejiang Provincial Department of Education(Y202353247).
文摘The proliferation of rumors on social media has caused serious harm to society.Although previous research has attempted to use deep learning methods for rumor detection,they did not simultaneously consider the two key features of temporal and spatial domains.More importantly,these methods struggle to automatically generate convincing explanations for the detection results,which is crucial for preventing the further spread of rumors.To address these limitations,this paper proposes a novel method that integrates both temporal and spatial features while leveraging Large Language Models(LLMs)to automatically generate explanations for the detection results.Our method constructs a dynamic graph model to represent the evolving,tree-like propagation structure of rumors across different time periods.Spatial features are extracted using a Graph Convolutional Network,which captures the interactions and relationships between entities within the rumor network.Temporal features are extracted using a Recurrent Neural Network,which accounts for the dynamics of rumor spread over time.To automatically generate explanations,we utilize Llama-3-8B,a large language model,to provide clear and contextually relevant rationales for the detected rumors.We evaluate our method on two real-world datasets and demonstrate that it outperforms current state-of-the-art techniques,achieving superior detection accuracy while also offering the added capability of automatically generating interpretable and convincing explanations.Our results highlight the effectiveness of combining temporal and spatial features,along with LLMs,for improving rumor detection and understanding.
文摘In this study, we proposed a deterministic mathematical model that attempts to explain the propagation of a rumor using epidemiological models approach. The population is divided into four classes which consist of ignorant individuals, I(t), spreaders targeting community through media, M(t), spreaders targeting community through verbal communication, G(t) and stiflers, R(t). We explored existence of the equilibria and analyzed its stability. It was established that rumour-free equilibrium E0 is locally asymptotically stable if R0<1;meaning rumor can seize spreading in a population, and unstable if R0>1 leads to new rumor spreading in the population. Numerical simulations of the dynamic model are carried out on the system to confirm the analytical results. We see that the dynamics of rumor spreading show similar behavior to that found in the dynamics of infectious diseases except that the spread depends on the classes of spreader.
基金This research was funded by the Deanship of Scientific Research,Imam Mohammad Ibn Saud Islamic University,Saudi Arabia,Grant No.(20-12-18-013).
文摘Rumors regarding epidemic diseases such as COVID 19,medicines and treatments,diagnostic methods and public emergencies can have harmful impacts on health and political,social and other aspects of people’s lives,especially during emergency situations and health crises.With huge amounts of content being posted to social media every second during these situations,it becomes very difficult to detect fake news(rumors)that poses threats to the stability and sustainability of the healthcare sector.A rumor is defined as a statement for which truthfulness has not been verified.During COVID 19,people found difficulty in obtaining the most truthful news easily because of the huge amount of unverified information on social media.Several methods have been applied for detecting rumors and tracking their sources for COVID 19-related information.However,very few studies have been conducted for this purpose for the Arabic language,which has unique characteristics.Therefore,this paper proposes a comprehensive approach which includes two phases:detection and tracking.In the detection phase of the study carried out,several standalone and ensemble machine learning methods were applied on the Arcov-19 dataset.A new detection model was used which combined two models:The Genetic Algorithm Based Support Vector Machine(that works on users’and tweets’features)and the stacking ensemble method(that works on tweets’texts).In the tracking phase,several similarity-based techniques were used to obtain the top 1%of similar tweets to a target tweet/post,which helped to find the source of the rumors.The experiments showed interesting results in terms of accuracy,precision,recall and F1-Score for rumor detection(the accuracy reached 92.63%),and showed interesting findings in the tracking phase,in terms of ROUGE L precision,recall and F1-Score for similarity techniques.
文摘A mathematical model described the propagation of information including rumor and truth presented and its properties investigated. We explored exists of the equilibria, local stability and global asymptotical stability, and obtained the propagation threshold of rumor spreading. Numerical simulation is shown to demonstrate our results. Uncertainty and sensitivity analysis shows the importance of the parameters in our model.
文摘As new media communication becomes popular and the technological threshold of personal expression has been lowered,the phenomenon of online rumors has also emerged with new communication characteristics as individuals are continuously empowered to communicate.This paper quantifies a rumor spreading event from 2020 to 2021 that generated a lot of discussion.Using a content analysis approach,we examined how different subjects in the online public opinion chaos have influenced the spreading process of the event,and used the study of online rumor spreading mechanism to gain insight into the public opinion governance in the new media era.The study found that media platforms,the parties involved,and the general public as important participants have fully utilized the voice channel of the online communication platform to play a role behind the rumor event.
基金Supported by the National Natural Science Foundation of China under Grant Nos.61103231,61103230the Innovation Program of Graduate Scientific Research in Institution of Higher Education of Jiangsu Province of China under Grant No.CXZZ110401+1 种基金the Basic Research Foundation of Engineering University of the Chinese People's Armed Police Force under Grant No.WJY201218 the Natural Science Basic Research Plan in Shaanxi Province of China under Grant No.2011JM8012
文摘In this paper, to study rumor spreading, we propose a novel susceptible-infected-removed （SIR） model by introducing the trust mechanism. We derive mean-field equations that describe the dynamics of the SIR model on homogeneous networks and inhomogeneous networks. Then a steady-state analysis is conducted to investigate the critical threshold and the finaJ size of the rumor spreading. We show that the introduction of trust mechanism reduces the final rumor size and the velocity of rumor spreading, but increases the critical thresholds on both networks. Moreover, the trust mechanism not only greatly reduces the maximum rumor influence, but also postpones the rumor terminal time, which provides us with more time to take measures to control the rumor spreading. The theoretical results are confirmed by sufficient numerical simulations.
基金Project supported by the National Natural Science Foundation of China(Grant No.11571170)the Natural Science Research of the Jiangsu Higher Education Institutions of China(Grant No.19KJB110001)the Natural Science Foundation of Jiangsu Province,China(Grant No.SBK2019040208).
文摘With the advent of the information age of networks,the study about rumor propagation in social networks has become increasingly significant.In this paper,a rumor propagation model with nonlinear functions and time delay in social networks is proposed.First,according to the nextgeneration matrix method,we work out the basic reproduction number.Second,we discuss the existence of the rumor-prevailing equilibrium points.Third,we demonstrate the stabilities of equilibrium points and analyze the sufficient conditions for Hopf bifurcation.Finally,the correctness of the theory is verified and several vital conclusions are obtained by numerical simulations.
基金Supported by National Natural Science Foundation of China under Grant Nos.11275017 and 11173028
文摘Based on the characteristics of rumor spreading in online social networks, this paper proposes a new rumor spreading model. This is an improved SIS rumor spreading model in online social networks that combines the transmission dynamics and population dynamics with consideration of the impact of both of the changing number of online social network users and different levels of user activity. We numerically simulate the rumor spreading process. The results of numerical simulation show that the improved SIS model can successfully characterize the rumor spreading behavior in online social networks. We also give the effective strategies of curbing the rumor spreading in online social networks.
基金Supported by National Natural Science Foundation of China under Grant Nos.11275017 and 11173028
文摘Based on the infectious disease model with disease latency, this paper proposes a new model for the rumor spreading process in online social network. In this paper what we establish an SEIR rumor spreading model to describe the online social network with varying total number of users and user deactivation rate. We calculate the exact equilibrium points and reproduction number for this model. Furthermore, we perform the rumor spreading process in the online social network with increasing population size based on the original real world Facebook network. The simulation results indicate that the SEIR model of rumor spreading in online social network with changing total number of users can accurately reveal the inherent characteristics of rumor spreading process in online social network.
基金Supported by the National Natural Science Foundation of China under Grant No.61402531the Natural Science Basic Research Plan in Shaanxi Province of China under Grant Nos.2014JQ8358,2015JQ6231,and 2014JQ8307+1 种基金the China Postdoctoral Science Foundation under Grant No.2015M582910the Basic Research Foundation of Engineering University of the Chinese People’s Armed Police Force under Grant Nos.WJY201419,WJY201605 and JLX201686
文摘In order to prevent and control the spread of rumors, the implementation of immunization strategies for ignorant individuals is very necessary, where the immunization usually means letting them learn the truth of rumors.Considering the facts that there is always a delay time between rumor spreading and implementing immunization, and that the truth of rumors can also be spread out, this paper constructs a novel susceptible-infected-removed(SIR) model.The propagation dynamical behaviors of the SIR model on homogeneous networks are investigated by using the meanfield theory and the Monte Carlo method. Research shows that the greater the delay time, the worse the immune effect of the immunization strategy. It is also found that the spread of the truth can inhibit to some extent the propagation of rumors, and the trend will become more obvious with the increase of reliability of the truth. Moreover, under the influence of delay time, the existence of nodes' identification force still slightly reduces the propagation degree of rumors.
基金Project supported by the National Natural Science Foundation of China (Grant No. 62071248)the Postgraduate Research Innovation Program of Jiangsu Province,China(Grant No. KYCX20 0730)。
文摘In real life, the rumor propagation is influenced by many factors. The complexity and uncertainty of human psychology make the diffusion model more challenging to depict. In order to establish a comprehensive propagation model, in this paper, we take some psychological factors into consideration to mirror rumor propagation. Firstly, we use the Ridenour model to combine the trust mechanism with the correlation mechanism and propose a modified rumor propagation model. Secondly, the mean-field equations which describe the dynamics of the modified SIR model on homogenous and heterogeneous networks are derived. Thirdly, a steady-state analysis is conducted for the spreading threshold and the final rumor size. Fourthly, we investigate rumor immunization strategies and obtain immunization thresholds. Next, simulations on different networks are carried out to verify the theoretical results and the effectiveness of the immunization strategies.The results indicate that the utilization of trust and correlation mechanisms leads to a larger final rumor size and a smaller terminal time. Moreover, different immunization strategies have disparate effectiveness in rumor propagation.
基金the National Natural Science Foundation of People’s Republic of China(Grant Nos.U1703262 and 62163035)the Special Project for Local Science and Technology Development Guided by the Central Government(Grant No.ZYYD2022A05)Xinjiang Key Laboratory of Applied Mathematics(Grant No.XJDX1401)。
文摘On the multilingual online social networks of global information sharing,the wanton spread of rumors has an enormous negative impact on people's lives.Thus,it is essential to explore the rumor-spreading rules in multilingual environment and formulate corresponding control strategies to reduce the harm caused by rumor propagation.In this paper,considering the multilingual environment and intervention mechanism in the rumor-spreading process,an improved ignorants–spreaders-1–spreaders-2–removers(I2SR)rumor-spreading model with time delay and the nonlinear incidence is established in heterogeneous networks.Firstly,based on the mean-field equations corresponding to the model,the basic reproduction number is derived to ensure the existence of rumor-spreading equilibrium.Secondly,by applying Lyapunov stability theory and graph theory,the global stability of rumor-spreading equilibrium is analyzed in detail.In particular,aiming at the lowest control cost,the optimal control scheme is designed to optimize the intervention mechanism,and the optimal control conditions are derived using the Pontryagin's minimum principle.Finally,some illustrative examples are provided to verify the effectiveness of the theoretical results.The results show that optimizing the intervention mechanism can effectively reduce the densities of spreaders-1 and spreaders-2 within the expected time,which provides guiding insights for public opinion managers to control rumors.
文摘We introduce the generalized rumor spreading model and investigate some properties of this model on different complex social networks. Despite pervious rumor models that both the spreader-spreader (SS) and the spreader-stifler (SR) interactions have the same rate α, we define α(1) and α(2) for SS and SR interactions, respectively. The effect of variation of α(1) and α(2) on the final density of stiflers is investigated. Furthermore, the influence of the topological structure of the network in rumor spreading is studied by analyzing the behavior of several global parameters such as reliability and efficiency. Our results show that while networks with homogeneous connectivity patterns reach a higher reliability, scale-free topologies need a less time to reach a steady state with respect the rumor.
文摘In a previous work(2018,Commun.Theor.Phys.70,795–802),a new compartment model for the spreading of rumors was introduced and analyzed.However,only the local asymptotic stability of this model was discussed.In the present work,we first provide a rigorous mathematical analysis for the global asymptotic stability(GAS)of the above-mentioned rumor spreading model.By constructing suitable Lyapunov candidate functions,we obtain the GAS of a rumor-free(boundary)equilibrium point and a unique rumor-spreading(positive)equilibrium point.After that,we utilize the approach based on the Lyapunov candidate functions to study the GAS of another rumor spreading model with control strategies,which was proposed in(2022,Physica A 606,128157).As an important consequence,the GAS of the rumor spreading model with control strategies is determined fully without resorting to technical hypotheses used in the benchmark work.Lastly,the theoretical findings are supported by a set of illustrative numerical examples.The obtained results not only improve the ones constructed in the two abovementioned benchmark papers but also can be extended to study the global dynamics of other rumor propagation models in the context of both integer-order and fractional-order derivatives.
文摘With the rapid development of Internet science and technology, the self-media industry is rising gradually. As an important way of information dissemination, more and more self-media platforms are established and the main body of information communication becomes more complex. The self-media not only brings convenience to people’s life but also brings some negative effects. The self-media has more remarkable characteristics in information dissemination. The birth of self-media makes the network appear more suspicious information that cannot be effectively verified. Internet rumors fly all over the sky, which has caused certain influence on the stability of the society. The prevention and control measures of online rumors from the perspective of self-media are studied in this paper for creating a healthier network environment. Firstly, the concepts of self-media and Internet rumors are briefly summarized. Then, the main characteristics of Internet rumors from the perspective of self-media are analyzed. Finally, the prevention and control measures of online rumors, including strengthening supervision, improving the quality of self-media, and strengthening public identification of rumors, are proposed.
文摘We study the dynamics of an epidemic-like model for the spread of a rumor on a connecting multi-small-world- network （CM-SWN） model, which represents organizational communication in the real world. It has been shown that this model exhibits a transition between regimes of localization and propagation at a finite value of network randomness. Here, by numerical means, we perform a quantitative characterization of the evolution in the three groups under two evolution rules, namely the conformity and obeying principles. The variant of a dynamic CM-SWN, where the quenched disorder of small-world networks is replaced by randomly changing connections between individuals in a single network and stable connection by star nodes between networks, is also analysed in detail and compared with a mean-field approximation.
