Dynamic properties of rumor propagation model induced by L´evy noise on social networks

Social networks are inevitably subject to disruptions from the physical world,such as sudden internet outages that sever local connections and impede information flow.While Gaussian white noise,commonly used to simulate stochastic disruptions,only fluctuates within a narrow range around its mean and fails to capture large-scale variations,L´evy noise can effectively compensate for this limitation.Therefore,a susceptible–infected–removed rumor propagation model with L´evy noise is constructed on homogeneous and heterogeneous networks,respectively.Then,the existence of a global positive solution and the asymptotic path-wise of the solution are derived on heterogeneous networks,and the sufficient conditions of rumor extinction and persistence are investigated.Subsequently,theoretical results are verified through numerical calculations and the sensitivity analysis related to the threshold is conducted on the model parameters.Through simulation experiments on Watts–Strogatz(WS)and Barab´asi–Albert networks,it is found that the addition of noise can inhibit the spread of rumors,resulting in a stochastic resonance phenomenon,and the optimal noise intensity is obtained on the WS network.The validity of the model is verified on three real datasets by particle swarm optimization algorithm.

CRB:A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization

The virtuality and openness of online social platforms make networks a hotbed for the rapid propagation of various rumors.In order to block the outbreak of rumor,one of the most effective containment measures is spreading positive information to counterbalance the diffusion of rumor.The spreading mechanism of rumors and effective suppression strategies are significant and challenging research issues.Firstly,in order to simulate the dissemination of multiple types of information,we propose a competitive linear threshold model with state transition(CLTST)to describe the spreading process of rumor and anti-rumor in the same network.Subsequently,we put forward a community-based rumor blocking(CRB)algorithm based on influence maximization theory in social networks.Its crucial step is to identify a set of influential seeds that propagate anti-rumor information to other nodes,which includes community detection,selection of candidate anti-rumor seeds and generation of anti-rumor seed set.Under the CLTST model,the CRB algorithm has been compared with six state-of-the-art algorithms on nine online social networks to verify the performance.Experimental results show that the proposed model can better reflect the process of rumor propagation,and review the propagation mechanism of rumor and anti-rumor in online social networks.Moreover,the proposed CRB algorithm has better performance in weakening the rumor dissemination ability,which can select anti-rumor seeds in networks more accurately and achieve better performance in influence spread,sensitivity analysis,seeds distribution and running time.

An Ensemble Learning Based Approach for Detecting and Tracking COVID19 Rumors

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.

Research on the Communication Mechanism of Online Rumors Under the Empowerment of New Media Technology-Take the“Nabobess Having an Affair With Courier”Rumor Incident as an Example

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.

BIFURCATION CONTROL FOR A FRACTIONAL-ORDER DELAYED SEIR RUMOR SPREADING MODEL WITH INCOMMENSURATE ORDERS

A fractional-order delayed SEIR rumor spreading model with a nonlinear incidence function is established in this paper,and a novel strategy to control the bifurcation of this model is proposed.First,Hopf bifurcation is investigated by considering time delay as bifurcation parameter for the system without a feedback controller.Then,a state feedback controller is designed to control the occurrence of bifurcation in advance or to delay it by changing the parameters of the controller.Finally,in order to verify the theoretical results,some numerical simulations are given.

健康谣言的干预策略:基于信息生命周期的视角

如何有效治理健康谣言是社交媒体时代的一大难题。健康谣言传播具有信息和信息加工主体两个层面的驱动因素,可依据信息生命周期理论将其分为产生期、评价期和传播期三阶段,系统归纳与分析不同阶段的健康谣言干预策略:产生期重在预防,强调对大众进行心理接种、提升自身健康素养;评价期关注个体的主观能动性,包括聚焦信息准确性以及促进自身分析式加工;传播期通过提高辟谣信息可信度、善用反驳文本和调节个体情绪状态,实现精准辟谣。未来研究可继续创新信息生命周期视角下健康谣言的治理策略,细化考察不同类型与易感人群下的干预手段;归纳与验证不同策略在现实世界中的应用效果,充分发挥行为科学与网络平台对建立健康谣言干预的长效机制的作用。

An improved ISR-WV rumor propagation model based on multichannels with time delay and pulse vaccination

The rapid development of the Internet has broadened the channels of dissemination of information,it has also led to the rapid and widespread propagation of rumors,which can have a serious negative impact socially.In this paper,an improved ISR-WV rumor propagation model integrating multichannels is proposed by considering the system’s time delay,and the influence of different channels of propagation on the dynamic process is further analyzed.Moreover,the basic reproduction number R0,rumor-free equilibrium,and rumor-prevailing equilibrium,as well as their stability,are deduced.Then,an optimal control problem with pulse vaccination is designed.Finally,the validity of the model and theoretical results is verified by numerical simulations and a practical application.The results show that the rumor propagation threshold R0 is more sensitive to the rate of the propagation of the information base channel.The shorter the thinking timeτ_(1)required for the ignorant to react after obtaining the information,the larger the final scale of propagation.Under this condition,the time delayτ_(2)spent by a spreader in producing a video is negatively related to the final scale of the propagation;conversely,a longerτ_(1)implies that the person tends to more cognizant,which can suppress the spread of rumors.Under this condition,τ_(2)has little effect on the final scale of propagation.In addition,the results also prove that timely implementation of the pulse vaccination control strategy of popular science education can effectively control the propagation of rumors and reduce their negative impact.

Stability and optimal control for delayed rumor-spreading model with nonlinear incidence over heterogeneous networks

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.

Predicting abnormal trading behavior from internet rumor propagation:a machine learning approach

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.

Deterministic and Stochastic Analysis of a New Rumor Propagation Model with Nonlinear Propagation Rate in Social Network

This paper presents a study on a new rumor propagation model with nonlinear propagation rate and secondary propagation rate. We divide the total population into three groups, the ignorant, the spreader and the aware. The nonlinear incidence rate describes the psychological impact of certain serious rumors on social groups when the number of individuals spreading rumors becomes larger. The main contributions of this work are the development of a new rumor propagation model and some results of deterministic and stochastic analysis of the rumor propagation model. The results show the influence of nonlinear propagation rate and stochastic fluctuation on the dynamic behavior of the rumor propagation model by using Lyapunov function method and stochastic related knowledge. Numerical examples and simulation results are given to illustrate the results obtained.

基于知识图谱的多特征融合谣言检测方法

为了解决谣言检测中由于缺乏外部知识而导致模型难以感知内隐信息,进而限制了模型挖掘深层信息的能力这个问题,提出了基于知识图谱的多特征融合谣言检测方法(KGMRD)。首先,对于每个事件,将帖子和评论共同构建为一个文本序列,并利用分类器从中提取情感特征,利用ConceptNet基于文本构造其知识图谱,将知识图谱中的实体表示利用注意力机制与文本的语义特征进行聚合,进而得到增强的语义特征表示;其次,在传播结构方面,对于每个事件,基于帖子的传播转发关系构建传播结构图,使用DropEdge对传播结构图进行剪枝,从而得到更有效的传播结构特征;最后,将得到的特征进行融合处理得到一个新的表示。在Weibo、Twitter15和Twitter16三个真实数据集上,使用SVM-RBF等七个模型作为基线进行了对比实验。结果表明:对比当前效果最好的基线,KGMRD方法在Weibo数据集的ACC指标提升了1.1%;在Twitter15和Twitter16数据集的ACC指标上提升了2.2%,证明了KGMRD方法是合理的、有效的。

社交网络中基于EHEM的两阶段谣言抑制方法

在线社交网络的兴起带来了一系列的挑战与风险,其中包括虚假以及恶意谣言的传播,这可能会误导民众,破坏社会的稳定。因此,对谣言的传播进行抑制成为当前社交网络领域的热点问题。目前已经积累较多谣言抑制的工作,但是还存在模型不能准确描述信息在社交网络上传播的问题,因此提出了一种新的刻画信息传播的模型——扩展热量模型(Extended Heat Energy Model, EHEM)。该模型充分考虑了信息传播中节点激活概率的动态调整机制、信息传播的持续级联机制以及节点状态的动态转变机制,更加精准地捕捉了信息在网络上传播的爆炸性和复杂性;其次,考虑到在真实世界相信谣言的节点在接触真相后存在将信仰转变到相信真相的可能性,提出了校正阈值来确定节点是否会发生信仰的转换;节点的重要程度决定了它们自身的影响力,因此还提出了节点多维质量来衡量节点的重要程度;最后提出了两阶段的谣言抑制(Two Stage Rumor Containment, TSRC)算法,该算法首先使用节点多维质量对网络进行剪枝处理,之后通过模拟的方式从网络中选出最优的正种子集合。在4个真实数据集上进行实验,结果表明,所提算法在多个指标上优于Random, Betweenness, MD, PR, PWD和ContrId这6种对比算法。

基于深度学习节点表示的谣言源定位方法

随着互联网的普及,网络上的信息以惊人的速度传播给公众。然而,由于级联效应,虚假信息和谣言同时也在迅速蔓延,对社会造成了巨大的危害。在社交网络上找到谣言的传播源头,对抑制谣言的传播起到至关重要的作用。传统的谣言源定位方法大多未能够融合多源特征且定位准确率还需进一步提高,因此,提出一种基于深度学习的谣言源定位方法,根据观测受谣言影响的节点多源特征来识别谣言源。首先,根据节点与观测节点之间的影响力相似度得到节点的影响力向量。然后,利用自编码网络对节点的影响力向量进行编码,得到包含节点信息、扩散路径和传播时间信息在内的节点的新的嵌入表示。最后,根据节点新的影响力向量计算节点为谣言源的概率,以定位谣言源。在2个模拟网络和4个真实网络上的实验结果表明,与其他方法相比,所提方法能够以更快的速度定位谣言源,且谣言源定位的准确率提升了25%以上。

在信息社会中架好科技新闻与大众的“沟通之桥”

[研究目的]探讨信息社会中科技新闻传播面临的挑战,以及如何提高网络时代科技新闻的传播度和影响力。[研究方法]采用文献分析法和案例分析法,探讨信息社会中科技新闻传播的现实窘境与应对目标,试析新闻记者提高科技报道专业技能的三种沟通范式,并从四个维度浅析媒体平台提高科技新闻影响力的公众沟通策略。[研究结论]信息社会中科技新闻传播存在“一升两降”的现实窘境,即遏制谣言的难度提升、科技新闻真实性和准确性下降、公众关注度和理解度下降。应对目标可以定位于对应的“一降两升”:降低遏制谣言的难度、提升科技新闻的真实性和准确性、提升公众的关注度和理解度。实现该应对目标,可从新闻记者和媒体平台两个层面来搭建与大众的沟通之桥、提升科技新闻影响力。在新闻记者层面,逻辑沟通之桥可以破除专业壁垒,互动沟通之桥可以消弭时空隔阂,共情沟通之桥可以拉近心理距离。而在媒体平台层面,从品牌维护、渠道拓展、技术进步、受众选择四大维度出发,采取相应的公众沟通策略,架好媒体平台与公众的沟通之桥,不断提升平台的美誉度和可信度,增强媒体的公信力和影响力,才是提升科技新闻影响力的根本。

基于广度-深度采样和图卷积网络的谣言检测方法

现有谣言检测方法存在早期数据丢失、特征利用不充分问题,为此提出新的检测方法.为了充分挖掘事件的早期传播特征,提出广度采样方法并构建与事件对应的传播序列,利用Transformer挖掘长距离评论间的语义相关性并构建事件的传播序列特征.为了有效挖掘事件的传播结构特征,提出基于路径长度的深度采样方法,构建事件对应的信息传播子图和信息聚合子图,利用图卷积网络在挖掘图结构特征方面的优势,获得与事件对应的传播结构特征.将事件对应的传播序列特征表示与传播结构特征表示进行拼接,得到事件对应的最终特征表示.在公开数据集Weibo2016和CED上开展所提方法的有效性验证实验.结果表明,所提方法普遍优于现有典型方法.与基线方法相比,所提方法的准确率和F1值均有显著提升,所提方法在谣言检测领域的有效性得到验证.

基于度量学习的多模态谣言检测

目前主流的多模态谣言检测模型,主要侧重于建模过程中模态的特征提取与拼接方法研究,而各模态局部特征关系、模态内与模态间的信息交互往往被忽略,这在一定程度上影响到了谣言检测的效果。针对该问题,提出了一种基于度量学习的多模态谣言检测方法。考虑到各模态局部特征关系对模态整体特征表示的影响,采用了句法分析和注意力机制技术分别挖掘文本和图片的局部特征关系;同时,将度量学习应用到谣言检测中,通过三元组学习和对比学习找出模态内与模态间的关联信息。在Twitter和Weibo两个公开的数据集上进行了性能测试实验,准确率分别达到92.8%和85.2%,结果表明将各模态局部特征关系、模态内与模态间的信息交互加入谣言检测模型中能够进一步提升谣言检测的精准度。

公安机关运用融媒体治理网络谣言的对策研究

突发公共事件中网络谣言的爆发以及融媒体的崛起对公安机关网络谣言治理工作提出了新的挑战。通过分析特定时段网络谣言具有的负面影响和积极因素,巩固网络谣言治理基础。公安机关运用融媒体治理网络谣言的现实困境包括融媒体应对的联动效能差、信息沟通不畅、平台建设良莠不齐、网络谣言治理中监测、引导、管理缺位等,针对问题分析原因,提出公安机关运用融媒体治理网络谣言预警体系、控制体系及防范体系建设对策,以期对突发公共事件处置中遏制网络谣言滋生蔓延起到积极作用。

社交媒体网络辟谣回音室效应分析模型及实验研究

[目的/意义]社交媒体因其受众广和传播速度快的特点,成为网络谣言传播的重要媒介。鉴于网络谣言的危害性,如何有效控制谣言传播和及时进行辟谣,对网络谣言的辟谣工作和网络空间生态治理具有重要的理论和现实意义。[方法/过程]本文提出网络辟谣主题识别及用户情感态度划分的方法,根据LDA算法确定划分主题数,在最优主题数下构建用户层级和社区层级的回音室网络,并给出社交媒体网络辟谣的回音室效应分析模型,确立了回音室效应分析公式及检验参数。结合“东航MU5735坠机”网络舆情话题中网络谣言辟谣数据进行分析,对6个网络辟谣主题进行识别和情感分布进行分析,并结合6个辟谣主题进行用户评论网络与转发网络回音室网络结构进行分析,计算每个辟谣主题下回音室效应的虚拟社区层级的评论网络和转发网络中每个虚拟社区主题的同质性。[结果/结论]研究发现,社交网络中辟谣信息的传播过程中存在回音室效应,其中转发过程比评论过程具有更强的网络同质性,而评论用户比转发用户具有更高的矛盾心理。

校园人际谣言与高中生抑郁的纵向关系——心理僵化与侵入性反刍的链式中介作用

为探讨校园人际谣言对高中生抑郁的纵向影响方式,以及为间接欺凌的防范工作提供理论依据,采用校园人际谣言量表、青少年回避与融合问卷、事件相关反刍沉思量表和流调中心用抑郁自评量表,对367名高中生进行间隔半年的两次追踪调查,研究结果表明:校园人际谣言能够显著正向预测高中生6个月后的抑郁水平;校园人际谣言分别通过心理僵化与侵入性反刍间接影响高中生抑郁;校园人际谣言通过“心理僵化—侵入性反刍”的链式中介影响高中生抑郁。

公共卫生事件中谣言形成机理与应对策略研究

公共卫生事件发生发展过程中,谣言肆意传播的现象时有发生,特别是在全媒体时代,这种情况愈发严重。谣言危害性极大,其对公众认知与行为、社会情绪的负面影响会对公共卫生事件处置和社会稳定带来严重不良后果。因此,探讨公共卫生事件中谣言形成机理和传播特征,有效应对并降低其危害性,已成为当前公共管理领域的重要任务。本文首先对公共卫生事件中谣言的形成机理进行分析,根据谣言的传播特征建立基于系统动力学的仿真模型,并在Vensim仿真平台进行仿真实验研究,总结提出更具科学性和可靠性的公共卫生事件谣言应对策略。