Statistics of co-occurring keywords on Twitter

TL;DR

This paper analyzes the frequency and co-occurrence of keywords on Twitter, revealing patterns in user behavior and relationships between brands through network analysis of keyword interactions.

Contribution

It introduces a detailed analysis of keyword occurrence and co-occurrence on Twitter, including the construction of relationship networks based on user-perceived brand associations.

Findings

Brand occurrence rates are highly intermittent and correlated over time.

Co-occurrence networks reveal user-perceived relationships between brands.

Keyword activity exhibits highly correlated time signals.

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.