Spatio-Temporal and Events Based Analysis of Topic Popularity in Twitter

被引：40

作者：

Ardon, Sebastien ^{[2
]}

Bagchi, Amitabha ^{[1
]}

Mahanti, Anirban ^{[2
]}

Ruhela, Amit ^{[1
,3
]}

Seth, Aaditeshwar ^{[1
]}

Tripathy, Rudra Mohan ^{[1
]}

Triukose, Sipat ^{[2
]}

机构：

[1] IIT Delhi, Delhi, India

[2] NICTA, Sydney, NSW, Australia

[3] C DOT Delhi, Delhi, India

来源：

PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13) | 2013年

关键词：

Online Social Network; Topics; Diffusion; Events;

D O I：

10.1145/2505515.2505525

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：219 / 228

页数：10

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