Statistics of co-occurring keywords on Twitter
arXiv:1401.4140
Abstract
Online social media such as the micro-blogging site Twitter has become a rich source of real-time data on online human behaviors. Here we analyze the occurrence and co-occurrence frequency of keywords in user posts on Twitter. From the occurrence rate of major international brand names, we provide examples on predictions of brand-user behaviors. From the co-occurrence rates, we further analyze the user-perceived relationships between international brand names and construct the corresponding relationship networks. In general the user activity on Twitter is highly intermittent and we show that the occurrence rate of brand names forms a highly correlated time signal.
13 pages, 8 figures
References in corpus (6)
- Maps of random walks on complex networks reveal community structure
- Human dynamics revealed through Web analytics
- Collective dynamics of social annotation
- Excitable human dynamics driven by extrinsic events in massive communities
- Communication dynamics in finite capacity social networks
- Synchronization Model for Stock Market Asymmetry