Open-Domain Event Graph Induction for Mitigating Framing Bias
Siyi Liu Hongming Zhang Hongwei Wang Kaiqiang Song Dan Roth Dong Yu
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.