An Inferential Commonsense-Driven Framework for Predicting Political Bias in News Headlines

Identifying political bias in news headlines holds significant importance as it influences the dissemination and consumption of news stories. However, employing conventional methods to do so poses a formidable challenge, as the short headline text is often complex and lacks sufficient syntactic and semantic context. Existing approaches fail to acknowledge the potential of commonsense reasoning in facilitating text comprehension, although it has been shown to aid numerous downstream applications. To this end, to facilitate comprehension and compensate for the lack of context, we propose leveraging inferential commonsense knowledge to simplify, interpret, and explain events that are not explicitly stated in headlines. Furthermore, to fully utilise its potential and deal with the unnecessary noise it may introduce, we present a method for emphasising significant inferences. Using this knowledge, we introduce a …