Tracking Idea Flows between Social Groups

TL;DR

This paper introduces a novel tensor-based method utilizing Bayesian conditional cointegration and dynamic time warping to model and track idea flows between social groups, effectively identifying lead-lag relationships and clustering ideas.

Contribution

It presents a new approach combining tensor representation and advanced statistical techniques to analyze idea flow dynamics between social groups, outperforming traditional methods.

Findings

More effective than traditional clustering methods

Achieves higher accuracy in identifying idea flows

Demonstrated usefulness in social media analysis

Abstract

In many applications, ideas that are described by a set of words often flow between different groups. To facilitate users in analyzing the flow, we present a method to model the flow behaviors that aims at identifying the lead-lag relationships between word clusters of different user groups. In particular, an improved Bayesian conditional cointegration based on dynamic time warping is employed to learn links between words in different groups. A tensor-based technique is developed to cluster these linked words into different clusters (ideas) and track the flow of ideas. The main feature of the tensor representation is that we introduce two additional dimensions to represent both time and lead-lag relationships. Experiments on both synthetic and real datasets show that our method is more effective than methods based on traditional clustering techniques and achieves better accuracy. A case study was conducted to demonstrate the usefulness of our method in helping users understand the flow of ideas between different user groups on social media