Causal Understanding of Fake News Dissemination on Social Media
Lu Cheng, Ruocheng Guo, Kai Shu, Huan Liu

TL;DR
This paper develops a causal inference framework to identify user attributes that cause fake news sharing on social media, addressing selection bias and confounders to better understand and mitigate fake news dissemination.
Contribution
It introduces a principled method to alleviate selection bias and models fake news sharing behavior as a surrogate confounder for causal analysis.
Findings
The approach effectively reduces selection bias in fake news sharing studies.
Learned behavior captures causal links between user attributes and susceptibility.
Empirical results demonstrate improved understanding of fake news dissemination.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMisinformation and Its Impacts · Opinion Dynamics and Social Influence · Complex Network Analysis Techniques
