Dynamic Multi-Relational Chinese Restaurant Process for Analyzing Influences on Users in Social Media

TL;DR

This paper introduces a novel non-parametric model, the Dynamic Multi-Relational Chinese Restaurant Process, to analyze complex, evolving influences on social media users, capturing multiple factors and their temporal dynamics.

Contribution

It presents a new model that accounts for multiple types of influences and their evolution over time, along with a scalable inference algorithm for large-scale social media data.

Findings

Outperforms state-of-the-art baselines in authorship and commenting prediction.

Effectively captures topic and user personality trends over time.

Provides valuable insights into influence dynamics on social media.

Abstract

We study the problem of analyzing influence of various factors affecting individual messages posted in social media. The problem is challenging because of various types of influences propagating through the social media network that act simultaneously on any user. Additionally, the topic composition of the influencing factors and the susceptibility of users to these influences evolve over time. This problem has not studied before, and off-the-shelf models are unsuitable for this purpose. To capture the complex interplay of these various factors, we propose a new non-parametric model called the Dynamic Multi-Relational Chinese Restaurant Process. This accounts for the user network for data generation and also allows the parameters to evolve over time. Designing inference algorithms for this model suited for large scale social-media data is another challenge. To this end, we propose a scalable and multi-threaded inference algorithm based on online Gibbs Sampling. Extensive evaluations on large-scale Twitter and Facebook data show that the extracted topics when applied to authorship and commenting prediction outperform state-of-the-art baselines. More importantly, our model produces valuable insights on topic trends and user personality trends, beyond the capability of existing approaches.