# Fake News Mitigation via Point Process Based Intervention

arXiv: https://arxiv.org/abs/1703.07823 · HTML: https://mlsanity.com/paper/1703.07823/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2017.

## Built on by (most cited first, 24 of 24)

- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes](https://mlsanity.com/paper/2201.12569/) (2022)
- [Rumor Detection with Self-supervised Learning on Texts and Social Graph](https://mlsanity.com/paper/2204.08838/) (2022)
- [Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences](https://mlsanity.com/paper/2202.11485/) (2022)
- [Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss Functions](https://mlsanity.com/paper/2107.03354/) (2021)
- [Region Invariant Normalizing Flows for Mobility Transfer](https://mlsanity.com/paper/2109.05738/) (2021)
- [Detecting Rumours with Latency Guarantees using Massive Streaming Data](https://mlsanity.com/paper/2205.06580/) (2022)
- [Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learning](https://mlsanity.com/paper/2402.03357/) (2024)
- [Applying Automatic Text Summarization for Fake News Detection](https://mlsanity.com/paper/2204.01841/) (2022)
- [Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processes](https://mlsanity.com/paper/2206.12414/) (2022)
- [Network Inference from a Mixture of Diffusion Models for Fake News Mitigation](https://mlsanity.com/paper/2008.03450/) (2020)
- [FNR: A Similarity and Transformer-Based Approach to Detect Multi-Modal Fake News in Social Media](https://mlsanity.com/paper/2112.01131/) (2021)
- [Is Multi-Modal Necessarily Better? Robustness Evaluation of Multi-modal Fake News Detection](https://mlsanity.com/paper/2206.08788/) (2022)
- [Socially Fair Mitigation of Misinformation on Social Networks via Constraint Stochastic Optimization](https://mlsanity.com/paper/2203.12537/) (2022)
- [Social-Inverse: Inverse Decision-making of Social Contagion Management with Task Migrations](https://mlsanity.com/paper/2209.10493/) (2022)
- [Retrieving Continuous Time Event Sequences using Neural Temporal Point Processes with Learnable Hashing](https://mlsanity.com/paper/2307.09613/) (2023)
- [GRaMuFeN: Graph-based Multi-modal Fake News Detection in Social Media](https://mlsanity.com/paper/2310.07668/) (2023)
- [Towards Effective Planning Strategies for Dynamic Opinion Networks](https://mlsanity.com/paper/2410.14091/) (2024)
- [Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques](https://mlsanity.com/paper/2506.05924/) (2025)
- [Multilingual Disinformation Detection for Digital Advertising](https://mlsanity.com/paper/2207.10649/) (2022)
- [Amortized Network Intervention to Steer the Excitatory Point Processes](https://mlsanity.com/paper/2310.04159/) (2023)
- [AMIR: Automated MisInformation Rebuttal -- A COVID-19 Vaccination Datasets based Recommendation System](https://mlsanity.com/paper/2310.19834/) (2023)
- [Explainable History Distillation by Marked Temporal Point Process](https://mlsanity.com/paper/2311.07797/) (2023)
- [Differentiable Adversarial Attacks for Marked Temporal Point Processes](https://mlsanity.com/paper/2501.10606/) (2025)

## Abstract (arXiv)

We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model. The spread of fake news and mitigation events within the network is modeled by a multivariate Hawkes process with additional exogenous control terms. By choosing a feature representation of states, defining mitigation actions and constructing reward functions to measure the effectiveness of mitigation activities, we map the problem of fake news mitigation into the reinforcement learning framework. We develop a policy iteration method unique to the multivariate networked point process, with the goal of optimizing the actions for maximal total reward under budget constraints. Our method shows promising performance in real-time intervention experiments on a Twitter network to mitigate a surrogate fake news campaign, and outperforms alternatives on synthetic datasets.