# Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learning

arXiv: https://arxiv.org/abs/2402.03357 · HTML: https://mlsanity.com/paper/2402.03357/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 48 references that are in the map.
- First posted on arXiv 28 Jan 2024.

## Builds on (most cited first, 19 of 19)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards](https://mlsanity.com/paper/1707.08817/) (2017)
- [Self-Imitation Learning](https://mlsanity.com/paper/1806.05635/) (2018)
- [Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads](https://mlsanity.com/paper/1511.07487/) (2015)
- [Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News](https://mlsanity.com/paper/2010.03159/) (2020)
- [Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation](https://mlsanity.com/paper/2303.06433/) (2023)
- [Learning Self-Imitating Diverse Policies](https://mlsanity.com/paper/1805.10309/) (2018)
- [Deep Q-learning from Demonstrations](https://mlsanity.com/paper/1704.03732/) (2017)
- [Generative Adversarial Self-Imitation Learning](https://mlsanity.com/paper/1812.00950/) (2018)
- [Fake News Mitigation via Point Process Based Intervention](https://mlsanity.com/paper/1703.07823/) (2017)
- [The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News](https://mlsanity.com/paper/1806.07516/) (2018)
- [Studying Fake News via Network Analysis: Detection and Mitigation](https://mlsanity.com/paper/1804.10233/) (2018)
- [Identifying Cost-effective Debunkers for Multi-stage Fake News Mitigation Campaigns](https://mlsanity.com/paper/2203.16854/) (2022)
- [Multistage Campaigning in Social Networks](https://mlsanity.com/paper/1606.03816/) (2016)
- [Cluster-Based Social Reinforcement Learning](https://mlsanity.com/paper/2003.00627/) (2020)

## Built on by (most cited first, 5 of 5)

- [Truth over Tricks: Measuring and Mitigating Shortcut Learning in Misinformation Detection](https://mlsanity.com/paper/2506.02350/) (2025)
- [Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques](https://mlsanity.com/paper/2506.05924/) (2025)
- [DQ4FairIM: Fairness-aware Influence Maximization using Deep Reinforcement Learning](https://mlsanity.com/paper/2512.00545/) (2025)
- [SARC: Sentiment-Augmented Deep Role Clustering for Fake News Detection](https://mlsanity.com/paper/2511.04692/) (2025)
- [Can Multimodal Large Language Models Generate and Detect Multimodal Social Media Fake News?](https://mlsanity.com/paper/2609.35809/) (2026)

## Abstract (arXiv)

This study aims to minimize the influence of fake news on social networks by deploying debunkers to propagate true news. This is framed as a reinforcement learning problem, where, at each stage, one user is selected to propagate true news. A challenging issue is episodic reward where the "net" effect of selecting individual debunkers cannot be discerned from the interleaving information propagation on social networks, and only the collective effect from mitigation efforts can be observed. Existing Self-Imitation Learning (SIL) methods have shown promise in learning from episodic rewards, but are ill-suited to the real-world application of fake news mitigation because of their poor sample efficiency. To learn a more effective debunker selection policy for fake news mitigation, this study proposes NAGASIL - Negative sampling and state Augmented Generative Adversarial Self-Imitation Learning, which consists of two improvements geared towards fake news mitigation: learning from negative samples, and an augmented state representation to capture the "real" environment state by integrating the current observed state with the previous state-action pairs from the same campaign. Experiments on two social networks show that NAGASIL yields superior performance to standard GASIL and state-of-the-art fake news mitigation models.