Along with the development of socialized media and self-help tourism,tourism industry has been going into tourism social times.Based on technology acceptance model,use and gratifications approach,and weighted and calc...Along with the development of socialized media and self-help tourism,tourism industry has been going into tourism social times.Based on technology acceptance model,use and gratifications approach,and weighted and calculated needs theory,this study explored the impact of perceived popularity,perceived characteristics,and perceived need on the use of tourism social network site and being a member of it.This study also discussed the interaction of perceived popularity,perceived characteristics,and perceived need.The findings of this paper could be used to help the management operator pay attention to strengthen the function of tourism social network site in order to provide better information for users and satisfied the needs of users.展开更多
In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is e...In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is extracted,and sequence influence maximization problem based on the influence of neighbor nodes is proposed in this paper.That is,in the time sequential social network,the propagation characteristics of the second-level neighbor nodes are considered emphatically,and k nodes are found to maximize the information propagation.Firstly,the propagation probability between nodes is calculated by the improved degree estimation algorithm.Secondly,the weighted cascade model(WCM) based on static social network is not suitable for temporal social network.Therefore,an improved weighted cascade model(IWCM) is proposed,and a second-level neighbors time sequential maximizing influence algorithm(STIM) is put forward based on node degree.It combines the consideration of neighbor nodes and the problem of overlap of influence scope between nodes,and makes it chronological.Finally,the experiment verifies that STIM algorithm has stronger practicability,superiority in influence range and running time compared with similar algorithms,and is able to solve the problem of maximizing the timing influence based on the influence of neighbor nodes.展开更多
Social media data is now widely used by many academic researchers. However, long-term social media data collection projects, which most typically involve collecting data from public-use APIs, often encounter issues wh...Social media data is now widely used by many academic researchers. However, long-term social media data collection projects, which most typically involve collecting data from public-use APIs, often encounter issues when relying on local area network servers (LANs) to collect high-volume streaming social media data over long periods of time. In this paper, we present a cloud-based data collection, pre-processing, and archiving infrastructure, and argue that this system mitigates or resolves the problems most typically encountered when running social media data collection projects on LANs at minimal cloud-computing costs. We show how this approach works in different cloud computing architectures, and how to adapt the method to collect streaming data from other social media platforms. The contribution of our research lies in the development of methodologies that researchers can use to monitor and analyze phenomena including how public opinion and public discourse change in response to events, monitoring the evolution and change of misinformation campaigns, and studying how organizations and entities change how they present and frame information online.展开更多
在机会网络中,节点的行为模式会表现出一定的社交特征,节点往往因相似的移动模式和固定的活动范围,在动态异构网络中表现出一定的集群特性,形成一个个拥有相似社会特征的独特小团体.节点的社会属性表现出长期的稳定性,可以有效应用在路...在机会网络中,节点的行为模式会表现出一定的社交特征,节点往往因相似的移动模式和固定的活动范围,在动态异构网络中表现出一定的集群特性,形成一个个拥有相似社会特征的独特小团体.节点的社会属性表现出长期的稳定性,可以有效应用在路由中.针对这一思想,本文提出了基于社交圈划分和相遇时间预测的机会网络路由算法SCEP(Social Circle division and Encounter time Prediction).该算法关注两个节点形成的直接关系与节点的社会属性特征,定义了基于强社交关系的熟悉集合拓扑,基于熟悉集合的概念以分布式方式开发社区,节点社区的合并受某些规则的约束,并对过时节点进行拓扑剪裁.同时,本文基于节点间相遇的时间间隔序列建模,利用节点间相遇历史数据预测下一次通信的时间.消息的路由通过利用社区、亲密节点集和可预测的通信时间等因素来实现.仿真实验结果表明,与EpSoc,CARA,SAAD,Prophet、NBAPR这5种算法相比,SCEP的性能更好.展开更多
基金This study was supported by a grant from the Projects of the National Natural Science Foundation of China(No.72074053).
文摘Along with the development of socialized media and self-help tourism,tourism industry has been going into tourism social times.Based on technology acceptance model,use and gratifications approach,and weighted and calculated needs theory,this study explored the impact of perceived popularity,perceived characteristics,and perceived need on the use of tourism social network site and being a member of it.This study also discussed the interaction of perceived popularity,perceived characteristics,and perceived need.The findings of this paper could be used to help the management operator pay attention to strengthen the function of tourism social network site in order to provide better information for users and satisfied the needs of users.
基金Supported by the National Natural Science Foundation of China(No.62172352,61871465,42002138)the Natural Science Foundation of Hebei Province(No.F2019203157)the Science and Technology Research Project of Hebei(No.ZD2019004)。
文摘In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is extracted,and sequence influence maximization problem based on the influence of neighbor nodes is proposed in this paper.That is,in the time sequential social network,the propagation characteristics of the second-level neighbor nodes are considered emphatically,and k nodes are found to maximize the information propagation.Firstly,the propagation probability between nodes is calculated by the improved degree estimation algorithm.Secondly,the weighted cascade model(WCM) based on static social network is not suitable for temporal social network.Therefore,an improved weighted cascade model(IWCM) is proposed,and a second-level neighbors time sequential maximizing influence algorithm(STIM) is put forward based on node degree.It combines the consideration of neighbor nodes and the problem of overlap of influence scope between nodes,and makes it chronological.Finally,the experiment verifies that STIM algorithm has stronger practicability,superiority in influence range and running time compared with similar algorithms,and is able to solve the problem of maximizing the timing influence based on the influence of neighbor nodes.
文摘Social media data is now widely used by many academic researchers. However, long-term social media data collection projects, which most typically involve collecting data from public-use APIs, often encounter issues when relying on local area network servers (LANs) to collect high-volume streaming social media data over long periods of time. In this paper, we present a cloud-based data collection, pre-processing, and archiving infrastructure, and argue that this system mitigates or resolves the problems most typically encountered when running social media data collection projects on LANs at minimal cloud-computing costs. We show how this approach works in different cloud computing architectures, and how to adapt the method to collect streaming data from other social media platforms. The contribution of our research lies in the development of methodologies that researchers can use to monitor and analyze phenomena including how public opinion and public discourse change in response to events, monitoring the evolution and change of misinformation campaigns, and studying how organizations and entities change how they present and frame information online.
文摘在机会网络中,节点的行为模式会表现出一定的社交特征,节点往往因相似的移动模式和固定的活动范围,在动态异构网络中表现出一定的集群特性,形成一个个拥有相似社会特征的独特小团体.节点的社会属性表现出长期的稳定性,可以有效应用在路由中.针对这一思想,本文提出了基于社交圈划分和相遇时间预测的机会网络路由算法SCEP(Social Circle division and Encounter time Prediction).该算法关注两个节点形成的直接关系与节点的社会属性特征,定义了基于强社交关系的熟悉集合拓扑,基于熟悉集合的概念以分布式方式开发社区,节点社区的合并受某些规则的约束,并对过时节点进行拓扑剪裁.同时,本文基于节点间相遇的时间间隔序列建模,利用节点间相遇历史数据预测下一次通信的时间.消息的路由通过利用社区、亲密节点集和可预测的通信时间等因素来实现.仿真实验结果表明,与EpSoc,CARA,SAAD,Prophet、NBAPR这5种算法相比,SCEP的性能更好.
