Spatio-temporal and events based analysis of topic popularity in twitter

Proceedings of the 22nd ACM international conference on Information & Knowledge Management Pub Date : 2013-10-27 DOI:10.1145/2505515.2505525

S. Ardon, A. Bagchi, A. Mahanti, Amit Ruhela, Aaditeshwar Seth, R. M. Tripathy, Sipat Triukose

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引用次数: 63

Abstract

We present the first comprehensive characterization of the diffusion of ideas on Twitter, studying more than 5.96 million topics that include both popular and less popular topics. On a data set containing approximately 10 million users and a comprehensive scraping of 196 million tweets, we perform a rigorous temporal and spatial analysis, investigating the time-evolving properties of the subgraphs formed by the users discussing each topic. We focus on two different notions of the spatial: the network topology formed by follower-following links on Twitter, and the geospatial location of the users. We investigate the effect of initiators on the popularity of topics and find that users with a high number of followers have a strong impact on topic popularity. We deduce that topics become popular when disjoint clusters of users discussing them begin to merge and form one giant component that grows to cover a significant fraction of the network. Our geospatial analysis shows that highly popular topics are those that cross regional boundaries aggressively.

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基于时空和事件的twitter话题流行度分析

我们首次对Twitter上的思想传播进行了全面表征，研究了超过596万个话题，其中包括热门话题和不太热门的话题。在包含大约1000万用户和1.96亿条推文的数据集上，我们执行了严格的时间和空间分析，调查了由讨论每个主题的用户形成的子图的时间演变属性。我们关注两种不同的空间概念:由Twitter上的关注者-关注者链接形成的网络拓扑，以及用户的地理空间位置。我们研究了发起者对话题受欢迎程度的影响，发现拥有大量关注者的用户对话题受欢迎程度有很强的影响。我们推断，当讨论话题的不相关的用户群开始合并并形成一个巨大的组成部分，并逐渐覆盖网络的很大一部分时，话题就会变得流行起来。我们的地理空间分析表明，最受欢迎的话题是那些积极跨越区域边界的话题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the 22nd ACM international conference on Information & Knowledge Management

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