Open-Domain Event Graph Induction for Mitigating Framing Bias
This addresses the issue of framing bias for news understanding, offering a novel approach to improve trustworthiness in event analysis.
The paper tackles the problem of framing bias in news articles by proposing a neutral event graph induction task, which constructs event networks with minimal bias from multiple sources, and demonstrates effectiveness using graph prediction and bias-focused metrics.
Researchers have proposed various information extraction (IE) techniques to convert news articles into structured knowledge for news understanding. However, none of the existing methods have explicitly addressed the issue of framing bias that is inherent in news articles. We argue that studying and identifying framing bias is a crucial step towards trustworthy event understanding. We propose a novel task, neutral event graph induction, to address this problem. An event graph is a network of events and their temporal relations. Our task aims to induce such structural knowledge with minimal framing bias in an open domain. We propose a three-step framework to induce a neutral event graph from multiple input sources. The process starts by inducing an event graph from each input source, then merging them into one merged event graph, and lastly using a Graph Convolutional Network to remove event nodes with biased connotations. We demonstrate the effectiveness of our framework through the use of graph prediction metrics and bias-focused metrics.