Causal Understanding of Fake News Dissemination on Social Media
Lu Cheng,Ruocheng Guo,Kai Shu,Huan Liu +3 more
- pp 148-157
TLDR
In this paper, a principled approach to alleviating selection bias in fake news dissemination is proposed, which considers the learned unbiased fake news sharing behavior as the surrogate confounder that can fully capture the causal links between user attributes and user susceptibility.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 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.read more
Citations
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Proceedings ArticleDOI
Generalizing to the Future: Mitigating Entity Bias in Fake News Detection
TL;DR: This paper proposes an entity debiasing framework (ENDEF) which generalizes fake news detection models to the future data by mitigating entity bias from a cause-effect perspective and demonstrates that the proposed framework can largely improve the performance of base fake news detectors, and online tests verify its superiority in practice.
Proceedings ArticleDOI
Reinforcement Subgraph Reasoning for Fake News Detection
TL;DR: A subgraph reasoning paradigm for fake news detection is proposed, which provides a crystal type of explainability by revealing which subgraphs of the news propagation network are the most important for news verification, and concurrently improves the generalization and discrimination power of graph-based detection models by removing task-irrelevant information.
Proceedings ArticleDOI
CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts
TL;DR: The annotation schema for this task of causal analysis of Mental health in Social media posts (CAMS) is introduced and the experimental results show that the hybrid CNN-LSTM model gives the best performance over CAMS dataset.
Proceedings ArticleDOI
Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media
Yizhou Zhang,Defu Cao,Yang Liu +2 more
TL;DR: An efficient yet precise way to estimate the Individual Treatment Effect (ITE) via neural temporal point process and gaussian mixture models and results indicate that the model recognized identiflable causal effect of misinformation that hurts people’s subjective emotions toward the vaccines.
Proceedings ArticleDOI
“This is Fake! Shared it by Mistake”:Assessing the Intent of Fake News Spreaders
TL;DR: This work proposes an influence graph, using which to model individuals’ intent in fake news spreading, and shows that the assessed intent can help significantly differentiate between intentional and unintentional fake news spreaders.
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