The Simulation of Rumor’S Spreading and Controlling in Micro-Blog Users’ Network

In this work, a rumor’s spreading and controlling in a directed Micro-blog user network being consisted with 580 000 nodes are simulated. By defining some authority nodes that release anti-rumor information as the prevention strategy, the effect of the nodes’ role in network on rumor’s suppression is studied. The findings show that rumor will be spread out fast and reach a stable level within limited steps. The suppression of rumor is more predominated by the intervening opportunity, the earlier the intervention strategy was implemented, the better the rumor’s controlling could be achieved. The controlling effect is less relevant with the role of the authority nodes in network.

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.

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.

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.

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.

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。

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。

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。

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。

防止地震后错误信息扩散的沟通指南介绍

2022年2月25日,瑞士地震局联合苏黎世联邦理工学院、剑桥大学、美国地质调查局等机构发布了《防止地震后错误信息扩散的沟通指南》,就如何预防和防止地震后错误信息的扩散提供了一般性建议。指南概述了何时可以获得不同类型的地震信息,以及如何在整个地震周期的各个阶段对信息扩散进行战略规划。详细介绍了指南的主要内容,以期为地震行业从事地震信息传播的工作人员提供信息参考。

复杂系统理论在社会治理中的应用综述

经济持续发展和社会不断进步对社会治理现代化提出了新的要求。社会是一个典型的复杂系统,社会治理研究迫切需要从复杂系统的角度,依靠多学科的交叉融合,从整体上加以解决。该文选取复杂系统相关研究方法,对社会治理中疫情防控、谣言管控、智慧交通和智慧消防4个典型场景进行了重点分析,总结了复杂系统理论在社会治理领域应用中存在的问题,对下一步主要发展的方向进行了展望。

基于多层次不真实性传播结构的社交媒体谣言检测

当前谣言检测工作主要研究谣言传播的方向特性,而忽视了谣言传播的全局结构特性,导致不能充分挖掘谣言潜在的结构特征;此外,现有研究忽略了谣言原始传播结构中存在的不真实关系,从而限制了传播节点特征的学习。为此,该文提出一种多层次的动态传播注意力网络模型(Multi-level Dynamic Propagation Attention Networks,MDPAN)用于检测谣言。该模型通过节点级注意力学习谣言传播图中所有连接边的贡献度,动态地关注对识别谣言有用的传播关系,并基于图卷积网络分别提取谣言不同层次的传播特征、扩散特征以及全局结构特征,最后引入基于注意力机制的池化方法对这些多层次的特征进行有效融合。在公开的Twitter15、Twitter16和Weibo16数据集上的实验结果表明,该文所提出的模型对比主流基于传播结构的EBGCN模型,整体准确率分别提高了2.1%、0.7%和1.7%。

多层次结构与半监督学习的谣言检测研究

当前谣言检测工作主要基于监督学习,需要人为标记数据而导致检测具有滞后性。为了充分利用大量的未标记数据,及时检测社交网络中的虚假谣言。提出了一种基于多层次结构与半监督学习谣言检测模型(multi-level semi spuervised graph convolutional neural network, MSGCN)。该模型构建了一种多层次检测模块,基于图卷积网络对有限的标记样本进行训练以提取多层次传播结构特征、扩散结构特征和全局结构特征。其次,引入随机模型扰动集成无标签数据的动态输出进行一致性预测,提出互补伪标签法来获取高质量伪标签数据,并将其加入标记数据扩充样本。最后在有监督交叉熵损失和无监督一致性损失约束下提高模型质量。在公开的Twitter15、Twitter16和Weibo数据集上的实验结果表明,所提出模型在30%标记样本下准确率达到88.3%、90.1%和95.5%,在少量的标记样本下便可达到优异的成绩。

在线社交网络中的多主题谣言溯源

随着通信技术的快速发展,用户之间的信息可以很快地流通,同时也导致谣言在社交网络中传播,因此亟需对谣言来源进行检测以确保社交网络的公信力。目前关于谣言溯源的研究方向基本注重于单主题谣言传播,然而社交网络中存在大量不同主题的谣言,谣言源头以及谣言主题数量越多,产生的不良影响越大。针对多主题谣言同时存在的情况,信息的传播过程需要被重新定义。因此,该文提出了一种多主题独立级联模型,并在该模型的基础上定义了谣言溯源问题。从已感染的网络子图中,基于影响力最大化的原则找出前k个可疑节点,这组节点被认为是最可能的谣言来源。并证明了该问题是NP难的,以及目标函数是单调且子模的。在此基础上,提出了一种基于影响力最大化的近似比为(1-1/e)的贪婪算法。在大型真实数据集上的实验表明,平均误差距离控制在1跳之内。而且与其他算法相比,该算法具有更高的准确性以及有效性。

基于粗糙集的不完备谣言信息系统的知识获取与决策

网络谣言可能扰乱人们的思想、心理和行为,引发社会震荡、危害公共安全,而微博等社交平台的广泛应用使得谣言造成的影响与危害变得更大,因此,谣言检测对于网络空间的有序健康发展具有重要的意义。当前谣言的自动检测技术更多关注检测模型的构建和输入数据的表现形式,而在改善数据质量以提高谣言识别效果方面的研究很少。基于此,本文将粗糙集理论应用于不完备谣言信息系统进行知识获取与决策,实质上是通过粗糙集理论解决不完备谣言信息系统的不确定性度量,冗余性以及不完备性等问题,以获得高质量的数据,改善谣言检测效果。首先系统总结了粗糙集理论中不确定性度量的方法,包括香农熵、粗糙熵、Liang熵以及信息粒度等四种不确定度量方法,并整理和推导了这四种不确定度量方法从完备信息系统到不完备信息系统的一致性拓展。基于上述总结的四种不确定度量方法,提出了基于最大相关最小冗余(MCMR,Maximum Correlation Minimum Redundancy)的知识约简算法。该方法基于熵度量方式,能够综合考量决策信息与冗余噪音,在UCI及Weibo等8个数据集上实验验证,结果表明本文算法优于几种基线算法,能够有效解决信息系统的冗余性。另外,提出了一种基于极大相容块的不完备决策树算法,在不同缺失程度数据上实验验证,结果表明本文算法能够有效解决信息系统的不完备性。

一种基于动态异构图的谣言检测模型

随着互联网技术和自媒体行业的快速发展,人们可以方便快捷地从社交媒体中获取最新信息,但也让更多的谣言在网络中盛行.现有谣言检测模型多从文本内容、用户信息和传播模式中挖掘有效特征.然而,现有模型未充分学习文本的语义信息和谣言传播过程中的结构信息,并忽略了谣言传播的动态过程.针对上述问题,本文提出一种基于动态异构图的谣言检测模型DHGNN(Dynamic Heterogeneous Graph Neural Network).首先,为了增强帖子的文本语义表示,本文提出一种多级注意力网络,引导模型关注源帖子和相应评论中关键的词和句子,充分学习源帖与相应评论之间的语义关联.其次,引入了基于异构图的图神经网络,通过对异构传播图中的用户、帖子节点和转发(或评论)关系进行建模,为不同类型的节点和边生成特定的表示,充分学习异构传播图中的结构信息.最后,提出一种基于旋转记忆单元的时序注意力,分别为每个异构传播图快照建立记忆,捕获谣言动态传播的演化模式.在Twitter15、Twitter16数据集上的实验结果表明,DHGNN模型的性能优于最新的对比模型.

时序感知的异质图神经谣言检测

近年来,在线社交媒体的发展大大加速了谣言的滋生和传播,谣言的危害性使得谣言的自动检测技术受到研究学者的广泛关注.本文同时考虑事件与事件之间的全局结构关系以及事件内部消息传播的时序关系,以异质图为载体共同显式建模两种关系,提出一种新的时序感知的异质图神经谣言检测模型.该模型利用时序感知的自注意力机制捕获事件内部转发(或评论)贴之间的时序关系,并将具有时序信息的转发(或评论)贴与源贴融合,得到事件的局部时序表征;接着利用元素级注意力机制捕捉事件与事件之间的全局结构关系,学习事件的全局结构表征;最后将二者融合用于检测谣言.实验结果表明,该模型优于大多数现有模型,可以提高谣言检测性能,并且同样具有优秀的早期检测性能.