Dynamic Multi-Relational Chinese Restaurant Process for Analyzing Influences on Users in Social Media
This addresses the challenge of modeling evolving influences in social media for researchers and practitioners, though it appears incremental as it builds on existing non-parametric methods.
The authors tackled the problem of analyzing multiple influences on user messages in social media, proposing a new non-parametric model and scalable inference algorithm that outperformed state-of-the-art baselines in authorship and commenting prediction on Twitter and Facebook data.
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.