[论文解读] Analyzing Social Interaction Networks from Twitter for Planned Special Events

Arif Mohaimin Sadri, Samiul Hasan+2 more|arXiv (Cornell University)|Apr 8, 2017

Complex Network Analysis Techniques参考文献 38被引用 8

一句话总结

本文基于普渡大学计划内特别活动期间的Twitter数据，构建并分析了社交互动网络，运用网络科学揭示了诸如幂律度分布、小世界特性及递增传递性等结构特性。研究发现，互动网络的规模线性增长但趋于稀疏，具有稳定的最大连通分量和演化的无标度行为，支持其在活动期间用于定向信息传播。

ABSTRACT

The complex topology of real networks allows its actors to change their functional behavior. Network models provide better understanding of the evolutionary mechanisms being accountable for the growth of such networks by capturing the dynamics in the ways network agents interact and change their behavior. Considerable amount of research efforts is required for developing novel network modeling techniques to understand the structural properties such networks, reproducing similar properties based on empirical evidence, and designing such networks efficiently. First, we demonstrate how to construct social interaction networks using social media data and then present the key findings obtained from the network analytics. We analyze the characteristics and growth of such interaction networks, examine the network properties and derive important insights based on the theories of network science literature. We also discuss the application of such networks as a useful tool to effectively disseminate targeted information during planned special events. We observed that the degree-distributions of such networks follow power-law that is indicative of the existence of fewer nodes in the network with higher levels of interactions, and many other nodes with less interactions. While the network elements and average user degree grow linearly each day, densities of such networks tend to become zero. Largest connected components exhibit higher connectivity (density) when compared with the whole graph. Network radius and diameter become stable over time evidencing the small-world property. We also observe increased transitivity and higher stability of the power-law exponents as the networks grow. Data is specific to the Purdue University community and two large events, namely Purdue Day of Giving and Senator Bernie Sanders' visit to Purdue University as part of Indiana Primary Election 2016.

研究动机与目标

• 构建并分析从计划内特别活动期间的Twitter数据中提取的社交互动网络。
• 运用网络科学原理，理解这些网络的结构演化与动态特性。
• 评估此类网络在大规模活动期间实现有效、定向信息传播的实用性。
• 研究度分布、密度和连通性等网络度量随时间的演变情况。
• 利用两次重大普渡大学活动的真实数据验证研究结果：普渡大学捐赠日与伯尼·桑德斯2016年竞选访问活动。

提出的方法

• 通过识别特定活动期间的用户互动（如提及、转发）从Twitter数据中构建互动网络。
• 应用网络科学技术分析包括度分布、密度、直径、半径和聚类在内的拓扑特性。
• 追踪网络元素（节点与边）、平均度及连通性随时间的每日增长情况。
• 采用幂律拟合评估无标度网络行为，并监测指数的稳定性。
• 识别并分析最大连通分量（LCC），以对比其与全网络的特性差异。
• 利用两次真实世界活动的实证数据，使分析建立在真实社交动态基础之上。

实验结果

研究问题

• RQ1 在计划内特别活动期间，Twitter上的社交互动网络在结构与拓扑上如何演化？
• RQ2 这些网络在多大程度上表现出无标度与小世界特性？
• RQ3 在活动期间，网络度量如密度、直径和传递性如何随时间变化？
• RQ4 最大连通分量在网络连通性与信息流动中扮演何种角色？
• RQ5 此类网络能否在计划内活动中被有效用于定向信息传播？

主要发现

• 网络的度分布符合幂律，表明存在少数高度活跃用户和大量活跃度较低的用户，证实了无标度行为的存在。
• 网络规模与平均度每日线性增长，而网络密度趋向于零，表明网络正变得愈加稀疏。
• 最大连通分量的密度与连通性显著高于全网络，表明其是信息传播的核心。
• 网络直径与半径随时间趋于稳定，支持互动网络中存在小世界特性的结论。
• 随着网络增长，传递性提升，幂律指数趋于稳定，表明网络结构具有一致性与鲁棒性。
• 观察到的网络特性表明，此类互动网络可被有效用于在计划内特别活动期间实现定向信息传播。