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
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
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 compound disaster rumor is a special type of rumor that involves multiple public safety events.Its pattern of spread is distinct from that of a general disaster rumor,which involves one public safety event.This work...A compound disaster rumor is a special type of rumor that involves multiple public safety events.Its pattern of spread is distinct from that of a general disaster rumor,which involves one public safety event.This work examines and verifies the amplification effect of the spread of compound disaster rumors(relative to general disaster rumors).A new rumor spread model based on infectious disease dynamics is proposed for compound disaster rumors involving two simultaneously occurring public safety events.The new model considers a special group of people,termed“double-hazard sensitive ignorants.”Taking this group as the initial crowd,it adds a new spread chain to existing rumor spread models.This modeling method successfully captures the amplification effect of the spread of compound disaster rumors involving two public safety events.A real case is selected for empirical analysis:the spread of a compound disaster rumor in a double-hazard scenario,consisting of an earthquake and the pandemic,in Sichuan,China in 2022.The results confirm that the spread of a pandemicrelated natural disaster compound rumor has a higher peak than that of a general disaster rumor.The new model is applied in this real scenario and captures the amplification effect of the spread of compound rumors.Our study sheds light on the spreading pattern of compound disaster rumors,thereby providing guidance and assistance for future disaster rumor management.展开更多
By manually collecting data on Internet-based rumors concerning COVID-19,we investigate the market reactions to the spread of such rumors and the government’s refutation of them.We find that frightening(reassuring)ru...By manually collecting data on Internet-based rumors concerning COVID-19,we investigate the market reactions to the spread of such rumors and the government’s refutation of them.We find that frightening(reassuring)rumors have a negative(positive)impact on investors.The refutation of frightening rumors triggers a positive market response,whereas the refutation of reassuring rumors does not cause a significant market reaction.Further analysis shows that there is a stock price drift when frightening rumors are refuted by governments.Our conclusions remain robust after considering endogeneity.Our findings support the notion that epidemic-related rumors affect investors’decisions,which add to literatures of the market responses of companies in the context of the COVID-19 pandemic and provide incremental evidence for the“the spiral of silence”theory.展开更多
Seismic rumors can mislead the public and trigger unnecessary actions,underscoring the importance of their control in disaster management.This study examined the impact of two diff erent intervention tools—rule-based...Seismic rumors can mislead the public and trigger unnecessary actions,underscoring the importance of their control in disaster management.This study examined the impact of two diff erent intervention tools—rule-based intervention and knowledge-based intervention—on the trust and sharing of seismic rumors.We designed a survey experiment to explore this issue,and 500 respondents participated in the experiment.The results indicate that the rule-based intervention signifi cantly reduced the public's trust in and intention to share seismic rumors,but the knowledge-based intervention failed.Possible mechanisms are that the rule-based intervention raises awareness of the unreliability of disaster information sources and costs associated with sharing rumors.It is suggested that communicating the existing rules and policies regarding disaster information release might be an eff ective approach to rendering disaster rumors uncreditable and then reducing people's intention to share.These fi ndings enrich our understanding of the eff ectiveness of diff erent intervention tools regarding rumor behavior in disaster scenarios and off er insights for practical seismic rumor management.展开更多
Using rumor verification data from investor interactive platforms,we investigate the effect of stock market rumors on price efficiency.We find favorable rumors are positively correlated with stock price synchronicity,...Using rumor verification data from investor interactive platforms,we investigate the effect of stock market rumors on price efficiency.We find favorable rumors are positively correlated with stock price synchronicity,while unfavorable rumors are negatively correlated with stock price synchronicity.Both favorable and unfavorable rumors are positively correlated with stock mispricing levels,and stock price crash risk.Mechanism tests reveal that favorable rumors about industry leaders have industry spillover effects.The effect of rumors on mispricing levels and stock price crash risk are more pronounced when there are more retail investors.Further analysis shows stronger detrimental impacts of rumors on price efficiency for small-cap companies,companies with low information transparency and companies with low institutional ownership.展开更多
In recent years,rumors have been shown to have a significant impact on individual and societal activities.As renewables play an increasingly significant role in electricity markets,certain rumors may deviate the biddi...In recent years,rumors have been shown to have a significant impact on individual and societal activities.As renewables play an increasingly significant role in electricity markets,certain rumors may deviate the bidding behavior of market entities and eventually affect the performance of market operations.In this study,we attempt to reveal the general threats caused by rumors in the context of day-ahead electricity markets considering the integration of volatile renewables.First,we model the propagation of rumors in the societal system considering the weight of propagation resistance,which principally reflects the communication accessibility of market entities.Second,we develop an integrated two-layer network model to uncover the inherent coupling mechanism between market operations and rumor propagation.In particular,the role of electricity market operations on rumor propagation is characterized by changes in the truthfulness of rumors associated with electricity prices.The rumors,in turn,affect the bidding quantities of market entities in electricity market operations.Finally,numerical experiments are conducted on modified IEEE 6-bus and 118-bus systems.The results demonstrate the potential threats of rumors to electricity market operations with different penetration levels of renewables.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
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
In 2021,the abnormal short-term price fluctuations of GameStop,which were triggered by internet stock discussions,drew the attention of academics,financial analysts,and stock trading commissions alike,prompting calls ...In 2021,the abnormal short-term price fluctuations of GameStop,which were triggered by internet stock discussions,drew the attention of academics,financial analysts,and stock trading commissions alike,prompting calls to address such events and maintain market stability.However,the impact of stock discussions on volatile trading behavior has received comparatively less attention than traditional fundamentals.Furthermore,data mining methods are less often used to predict stock trading despite their higher accuracy.This study adopts an innovative approach using social media data to obtain stock rumors,and then trains three decision trees to demonstrate the impact of rumor propagation on stock trading behavior.Our findings show that rumor propagation outperforms traditional fundamentals in predicting abnormal trading behavior.The study serves as an impetus for further research using data mining as a method of inquiry.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 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
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
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
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
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.
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.
基金supported by National Natural Science Foundation of China(72034004)National Science Fund for Distinguished Young Scholars of China(71725006).
文摘A compound disaster rumor is a special type of rumor that involves multiple public safety events.Its pattern of spread is distinct from that of a general disaster rumor,which involves one public safety event.This work examines and verifies the amplification effect of the spread of compound disaster rumors(relative to general disaster rumors).A new rumor spread model based on infectious disease dynamics is proposed for compound disaster rumors involving two simultaneously occurring public safety events.The new model considers a special group of people,termed“double-hazard sensitive ignorants.”Taking this group as the initial crowd,it adds a new spread chain to existing rumor spread models.This modeling method successfully captures the amplification effect of the spread of compound disaster rumors involving two public safety events.A real case is selected for empirical analysis:the spread of a compound disaster rumor in a double-hazard scenario,consisting of an earthquake and the pandemic,in Sichuan,China in 2022.The results confirm that the spread of a pandemicrelated natural disaster compound rumor has a higher peak than that of a general disaster rumor.The new model is applied in this real scenario and captures the amplification effect of the spread of compound rumors.Our study sheds light on the spreading pattern of compound disaster rumors,thereby providing guidance and assistance for future disaster rumor management.
基金funded by the National Natural Science Foundation of China(Grant No.71672208 and71902210)National Social Science Foundation(Grant No.21BGL095)+1 种基金Humanities and Social Sciences Foundation of Ministry of Education of China(No.19YJC630092)Program for Innovation Research in Central University of Finance and Economics
文摘By manually collecting data on Internet-based rumors concerning COVID-19,we investigate the market reactions to the spread of such rumors and the government’s refutation of them.We find that frightening(reassuring)rumors have a negative(positive)impact on investors.The refutation of frightening rumors triggers a positive market response,whereas the refutation of reassuring rumors does not cause a significant market reaction.Further analysis shows that there is a stock price drift when frightening rumors are refuted by governments.Our conclusions remain robust after considering endogeneity.Our findings support the notion that epidemic-related rumors affect investors’decisions,which add to literatures of the market responses of companies in the context of the COVID-19 pandemic and provide incremental evidence for the“the spiral of silence”theory.
基金supported by the National Natural Science Foundation of China(Grant Nos.72204051 and 72104089)Guangdong Office of Philosophy and Social Science(Grant No.GD20YGL15)the Fundamental Research Funds for the Central Universities(Grant No.23JNQN08)。
文摘Seismic rumors can mislead the public and trigger unnecessary actions,underscoring the importance of their control in disaster management.This study examined the impact of two diff erent intervention tools—rule-based intervention and knowledge-based intervention—on the trust and sharing of seismic rumors.We designed a survey experiment to explore this issue,and 500 respondents participated in the experiment.The results indicate that the rule-based intervention signifi cantly reduced the public's trust in and intention to share seismic rumors,but the knowledge-based intervention failed.Possible mechanisms are that the rule-based intervention raises awareness of the unreliability of disaster information sources and costs associated with sharing rumors.It is suggested that communicating the existing rules and policies regarding disaster information release might be an eff ective approach to rendering disaster rumors uncreditable and then reducing people's intention to share.These fi ndings enrich our understanding of the eff ectiveness of diff erent intervention tools regarding rumor behavior in disaster scenarios and off er insights for practical seismic rumor management.
基金supported by the National Natural Science Foundation of China(Project Nos.71902201 and 71972189).
文摘Using rumor verification data from investor interactive platforms,we investigate the effect of stock market rumors on price efficiency.We find favorable rumors are positively correlated with stock price synchronicity,while unfavorable rumors are negatively correlated with stock price synchronicity.Both favorable and unfavorable rumors are positively correlated with stock mispricing levels,and stock price crash risk.Mechanism tests reveal that favorable rumors about industry leaders have industry spillover effects.The effect of rumors on mispricing levels and stock price crash risk are more pronounced when there are more retail investors.Further analysis shows stronger detrimental impacts of rumors on price efficiency for small-cap companies,companies with low information transparency and companies with low institutional ownership.
基金supported by the Fundamental Research Funds for the Central Universities(Zhejiang University NGICS Platform)the Zhejiang Provincial Public Welfare Technology Application Research Project(No.LGJ21E070001)。
文摘In recent years,rumors have been shown to have a significant impact on individual and societal activities.As renewables play an increasingly significant role in electricity markets,certain rumors may deviate the bidding behavior of market entities and eventually affect the performance of market operations.In this study,we attempt to reveal the general threats caused by rumors in the context of day-ahead electricity markets considering the integration of volatile renewables.First,we model the propagation of rumors in the societal system considering the weight of propagation resistance,which principally reflects the communication accessibility of market entities.Second,we develop an integrated two-layer network model to uncover the inherent coupling mechanism between market operations and rumor propagation.In particular,the role of electricity market operations on rumor propagation is characterized by changes in the truthfulness of rumors associated with electricity prices.The rumors,in turn,affect the bidding quantities of market entities in electricity market operations.Finally,numerical experiments are conducted on modified IEEE 6-bus and 118-bus systems.The results demonstrate the potential threats of rumors to electricity market operations with different penetration levels of renewables.
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.
基金supported by the National Science and Technology Council,Taiwan,under grants MOST 108-2410-H-027-020,MOST 109-2410-H-027-009-MY2 and MOST 111-2410-H-027-011-MY3.
文摘In 2021,the abnormal short-term price fluctuations of GameStop,which were triggered by internet stock discussions,drew the attention of academics,financial analysts,and stock trading commissions alike,prompting calls to address such events and maintain market stability.However,the impact of stock discussions on volatile trading behavior has received comparatively less attention than traditional fundamentals.Furthermore,data mining methods are less often used to predict stock trading despite their higher accuracy.This study adopts an innovative approach using social media data to obtain stock rumors,and then trains three decision trees to demonstrate the impact of rumor propagation on stock trading behavior.Our findings show that rumor propagation outperforms traditional fundamentals in predicting abnormal trading behavior.The study serves as an impetus for further research using data mining as a method of inquiry.
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 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 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.
