# Causal Understanding of Fake News Dissemination on Social Media

arXiv: https://arxiv.org/abs/2010.10580 · HTML: https://mlsanity.com/paper/2010.10580/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 27 of 27)

- [Socially Responsible AI Algorithms: Issues, Purposes, and Challenges](https://mlsanity.com/paper/2101.02032/) (2021)
- [Generalizing to the Future: Mitigating Entity Bias in Fake News Detection](https://mlsanity.com/paper/2204.09484/) (2022)
- [CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts](https://mlsanity.com/paper/2207.04674/) (2022)
- [Alleviating Structural Distribution Shift in Graph Anomaly Detection](https://mlsanity.com/paper/2401.14155/) (2024)
- [Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media](https://mlsanity.com/paper/2210.07518/) (2022)
- [Robust Domain Misinformation Detection via Multi-modal Feature Alignment](https://mlsanity.com/paper/2311.14315/) (2023)
- ["This is Fake! Shared it by Mistake": Assessing the Intent of Fake News Spreaders](https://mlsanity.com/paper/2202.04752/) (2022)
- [Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks](https://mlsanity.com/paper/2212.12621/) (2022)
- [Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Models](https://mlsanity.com/paper/2406.02143/) (2024)
- [Rumor Detection with Diverse Counterfactual Evidence](https://mlsanity.com/paper/2307.09296/) (2023)
- [Explaining Veracity Predictions with Evidence Summarization: A Multi-Task Model Approach](https://mlsanity.com/paper/2402.06443/) (2024)
- [Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish](https://mlsanity.com/paper/2403.00411/) (2024)
- [Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detection](https://mlsanity.com/paper/2512.18133/) (2025)
- [False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations](https://mlsanity.com/paper/2308.12497/) (2023)
- [Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach](https://mlsanity.com/paper/2311.09630/) (2023)
- [Court of LLMs: Evidence-Augmented Generation via Multi-LLM Collaboration for Text-Attributed Graph Anomaly Detection](https://mlsanity.com/paper/2508.00507/) (2025)
- [Linguistic-style-aware Neural Networks for Fake News Detection](https://mlsanity.com/paper/2301.02792/) (2023)
- [Preemptive Detection of Fake Accounts on Social Networks via Multi-Class Preferential Attachment Classifiers](https://mlsanity.com/paper/2308.05353/) (2023)
- [UMGAD: Unsupervised Multiplex Graph Anomaly Detection](https://mlsanity.com/paper/2411.12556/) (2024)
- [Multi-Evidence based Fact Verification via A Confidential Graph Neural Network](https://mlsanity.com/paper/2405.10481/) (2024)
- [Estimating Topic Exposure for Under-Represented Users on Social Media](https://mlsanity.com/paper/2208.03796/) (2022)
- [Analyzing User Characteristics of Hate Speech Spreaders on Social Media](https://mlsanity.com/paper/2310.15772/) (2023)
- [Balanced Edge Pruning for Graph Anomaly Detection with Noisy Labels](https://mlsanity.com/paper/2407.05934/) (2024)
- [Evaluating LLMs Capabilities Towards Understanding Social Dynamics](https://mlsanity.com/paper/2411.13008/) (2024)
- [CausalMamba: Interpretable State Space Modeling for Temporal Rumor Causality](https://mlsanity.com/paper/2511.16191/) (2025)
- [Multi-Scale Adaptive Neighborhood Awareness Transformer For Graph Fraud Detection](https://mlsanity.com/paper/2603.03106/) (2026)
- [Graph Negative Feedback Bias Correction Framework for Adaptive Heterophily Modeling](https://mlsanity.com/paper/2603.03662/) (2026)

## Abstract (arXiv)

Recent years have witnessed remarkable progress towards computational fake news detection. To mitigate its negative impact, we argue that it is critical to understand what user attributes potentially cause users to share fake news. The key to this causal-inference problem is to identify confounders -- variables that cause spurious associations between treatments (e.g., user attributes) and outcome (e.g., user susceptibility). In fake news dissemination, confounders can be characterized by fake news sharing behavior that inherently relates to user attributes and online activities. Learning such user behavior is typically subject to selection bias in users who are susceptible to share news on social media. Drawing on causal inference theories, we first propose a principled approach to alleviating selection bias in fake news dissemination. We then consider the learned unbiased fake news sharing behavior as the surrogate confounder that can fully capture the causal links between user attributes and user susceptibility. We theoretically and empirically characterize the effectiveness of the proposed approach and find that it could be useful in protecting society from the perils of fake news.