Characterizing reticulation in online social networks during disasters
Characterizing reticulation in online social networks during disasters
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DOI：
10.1007/s41109-020-00271-5
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发表时间：
2020-06
影响因子：
2.2
通讯作者：
Chao Fan;Jiayi Shen;A. Mostafavi;Xia Hu
中科院分区：
文献类型：
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作者：
Chao Fan;Jiayi Shen;A. Mostafavi;Xia Hu
在线社交网络已经成为社区传播灾害态势信息的一种新的基础设施形式。开发有效的干预措施以改善信息传播的网络性能，对于人们在应对灾害和随后的中断中快速检索信息至关重要。现有的研究已经调查了固定情况下在线社交网络的多个方面，并以不同的方式进行了研究。然而，网络是动态的，在不断变化的灾害情况下，网络的不同属性是相互关联的。特别是，灾难事件激励人们进行在线交流，建立和加强他们的联系，并导致在线社交网络的动态网状结构。为了理解这些要素之间的关系，我们提出了一个在线网络网格化(ONR)框架，以考察在线社交网络演变中的四种模式(即制定、激活、网状化和网络性能)，以分析灾难中的破坏性事件、用户活动和社交媒体上的信息传播绩效之间的相互作用。因此，我们考察了表征网状化的四个要素的时间变化：活动时间、活动类型(帖子、分享、回复)、网状化机制(创建新链接与加强现有链接)、以及通信实例的结构(自环、汇聚和互惠)。最后，使用属性网络嵌入方法，以用户之间的平均潜在距离作为衡量网络信息传播性能的指标，考察了网络网格化的聚合效果。在对2017年休斯顿哈维飓风期间建筑环境破坏事件的推特网络联网研究中，演示了所提出的框架的应用。结果表明，在不断变化的情况下，网络网格化的主要潜在机制是由常规用户创建新的链接。通信实例的主要结构是聚合，表明通信实例受破坏性事件后的信息寻求行为驱动。随着网络的演化，收敛结构与自环和互易结构的比例没有明显变化，表明网络结构存在标度不变性。研究结果表明，所提出的在线网络网状框架能够刻画灾难期间在线社交网络中事件、活动和网络性能之间的复杂关系。
Online social network has become a new form of infrastructure for communities in spreading situational information in disasters. Developing effective interventions to improve the network performance of information diffusion is essential for people to rapidly retrieve information in coping with disasters and subsequent disruptions. Existing studies have investigated multiple aspects of online social networks in stationary situations and a separate manner. However, the networks are dynamic and different properties of the networks are co-related in the evolving disaster situations. In particular, disaster events motivate people to communicate online, create and reinforce their connections, and lead to a dynamic reticulation of the online social networks. To understand the relationship among these elements, we proposed an Online Network Reticulation (ONR) framework to examine four modalities (i.e., enactment, activation, reticulation, and network performance) in the evolution of online social networks to analyze the interplays among disruptive events in disasters, user activities, and information diffusion performance on social media. Accordingly, we examine the temporal changes in four elements for characterization of reticulation: activity timing, activity types (post, share, reply), reticulation mechanism (creation of new links versus reinforcement of existing links), and structure of communication instances (self-loop, converging, and reciprocal). Finally, the aggregated effects of network reticulation, using attributed network-embedding approach, are examined in the average latent distance among users as a measure of network performance for information propagation. The application of the proposed framework is demonstrated in a study of network reticulation on Twitter for a built environment disruption event during 2017 Hurricane Harvey in Houston. The results show that the main underlying mechanism of network reticulation in evolving situations was the creation of new links by regular users. The main structure for communication instances was converging, indicating communication instances driven by information-seeking behaviors in the wake of a disruptive event. With the evolution of the network, the proportion of converging structures to self-loop and reciprocal structures did not change significantly, indicating the existence of a scale-invariance property for network structures. The findings demonstrate the capability of the proposed online network reticulation framework for characterizing the complex relationships between events, activities, and network performance in online social networks during disasters.