摘要
现有针对谣言澄清问题的研究,往往忽略了实施谣言澄清策略需要付出的代价问题.为此,本文构建了一种在线社交网络(online social networks,OSN)中,同时考虑信息整体流行度和个体传播倾向的动态线性阈值谣言传播模型(dynamic linear threshold based rumor spreading,DLTRS);并基于该模型,设计了一种考虑辟谣信息传播时间、种子节点数量、信息内容等谣言抑制代价的谣言澄清算法(rumor clarification considering cost restraint,RCCR);利用构建的网络和真实OSN环境下的数据集,对提出的谣言传播模型和澄清算法进行了仿真实验,并进一步分析了各参数变化对算法性能的影响.
The existing researches on rumor clarification often ignore the cost of implementing rumor clarification strategy.Therefore,this paper establishes a dynamic linear threshold rumor propagation model called DLTRS in online social network(OSN),which takes into account both the overall popularity of information and individual propagation tendency.Based on this model,a rumor clarification algorithm called RCCR is designed,which takes into account the rumor suppression cost,such as the truth propagation time,number of seed nodes,and information content.In a synthetic network and five real OSNs,the proposed rumor propagation model and clarification algorithm are simulated,and the influence of parameter changes on the algorithm performance is further analyzed.
作者
丁学君
蒋曼
田勇
李梦雨
明盛智
DING Xuejun;JIANG Man;TIAN Yong;LI Mengyu;MING Shengzhi(School of Management Science and Engineering,Dongbei University of Finance and Economics,Dalian 116025,China;School of Management,Dalian University of Finance and Economics,Dalian 116622,China;School of Physics and Electronic Technology,Liaoning Normal University,Dalian 116029,China)
出处
《系统工程理论与实践》
EI
CSSCI
CSCD
北大核心
2021年第5期1119-1137,共19页
Systems Engineering-Theory & Practice
基金
国家自然科学基金(71874025,62076114)
教育部人文社会科学研究项目(20YJA630058)。
关键词
在线社交网络
谣言澄清
抑制代价
记忆机制
online social network
rumor clarification
cost restraint
memory mechanism
