# Uncovering Social Network Activity Using Joint User and Topic Interaction

arXiv: https://arxiv.org/abs/2506.12842 · HTML: https://mlsanity.com/paper/2506.12842/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 74 references that are in the map.
- First posted on arXiv 15 Jun 2025.

## Builds on (most cited first, 29 of 29)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Transformer Embeddings of Irregularly Spaced Events and Their Participants](https://mlsanity.com/paper/2201.00044/) (2021)
- [Learning Granger Causality for Hawkes Processes](https://mlsanity.com/paper/1602.04511/) (2016)
- [EasyTPP: Towards Open Benchmarking Temporal Point Processes](https://mlsanity.com/paper/2307.08097/) (2023)
- [COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution](https://mlsanity.com/paper/1507.02293/) (2015)
- [Discovering Latent Network Structure in Point Process Data](https://mlsanity.com/paper/1402.0914/) (2014)
- [Exploiting Social Network Structure for Person-to-Person Sentiment Analysis](https://mlsanity.com/paper/1409.2450/) (2014)
- [A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances](https://mlsanity.com/paper/2005.11041/) (2020)
- [Learning and Forecasting Opinion Dynamics in Social Networks](https://mlsanity.com/paper/1506.05474/) (2015)
- [Efficient Non-parametric Bayesian Hawkes Processes](https://mlsanity.com/paper/1810.03730/) (2018)
- [Learning Hawkes Processes from a Handful of Events](https://mlsanity.com/paper/1911.00292/) (2019)
- [The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process](https://mlsanity.com/paper/1612.09328/) (2016)
- [Unmasking the Web of Deceit: Uncovering Coordinated Activity to Expose Information Operations on Twitter](https://mlsanity.com/paper/2310.09884/) (2023)
- [Spatial-Temporal-Textual Point Processes for Crime Linkage Detection](https://mlsanity.com/paper/1902.00440/) (2019)
- [Origins of power-law degree distribution in the heterogeneity of human activity in social networks](https://mlsanity.com/paper/1304.4523/) (2013)
- [The Simple Rules of Social Contagion](https://mlsanity.com/paper/1308.5015/) (2013)
- [Emergence of polarized ideological opinions in multidimensional topic spaces](https://mlsanity.com/paper/2007.00601/) (2020)
- [Recurrent Poisson Factorization for Temporal Recommendation](https://mlsanity.com/paper/1703.01442/) (2017)
- [Multivariate Hawkes Processes for Large-scale Inference](https://mlsanity.com/paper/1602.08418/) (2016)
- [Universality, criticality and complexity of information propagation in social media](https://mlsanity.com/paper/2109.00116/) (2021)
- [MuxViz: A Tool for Multilayer Analysis and Visualization of Networks](https://mlsanity.com/paper/1405.0843/) (2014)
- [Correlated Cascades: Compete or Cooperate](https://mlsanity.com/paper/1510.00936/) (2015)
- [The Bursty Dynamics of the Twitter Information Network](https://mlsanity.com/paper/1403.2732/) (2014)
- [Flexible Triggering Kernels for Hawkes Process Modeling](https://mlsanity.com/paper/2202.01869/) (2022)
- [DeGroot-based opinion formation under a global steering mechanism](https://mlsanity.com/paper/2210.12274/) (2022)
- [Conjugate Bayesian Two-step Change Point Detection for Hawkes Process](https://mlsanity.com/paper/2409.17591/) (2024)
- [Smart Information Spreading for Opinion Maximization in Social Networks](https://mlsanity.com/paper/1901.00209/) (2019)
- [Multivariate Powered Dirichlet Hawkes Process](https://mlsanity.com/paper/2212.05995/) (2022)
- [Voter model can accurately predict individual opinions in online populations](https://mlsanity.com/paper/2501.13215/) (2025)

## Abstract (arXiv)

The emergence of online social platforms, such as social networks and social media, has drastically affected the way people apprehend the information flows to which they are exposed. In such platforms, various information cascades spreading among users is the main force creating complex dynamics of opinion formation, each user being characterized by their own behavior adoption mechanism. Moreover, the spread of multiple pieces of information or beliefs in a networked population is rarely uncorrelated. In this paper, we introduce the Mixture of Interacting Cascades (MIC), a model of marked multidimensional Hawkes processes with the capacity to model jointly non-trivial interaction between cascades and users. We emphasize on the interplay between information cascades and user activity, and use a mixture of temporal point processes to build a coupled user/cascade point process model. Experiments on synthetic and real data highlight the benefits of this approach and demonstrate that MIC achieves superior performance to existing methods in modeling the spread of information cascades. Finally, we demonstrate how MIC can provide, through its learned parameters, insightful bi-layered visualizations of real social network activity data.