Media Attitude Detection via Framing Analysis with Events and their Relations

Framing is used to present some selective aspects of an issue and make them more salient, which aims to promote certain values, interpretations, or solutions (Entman, 1993) . This study investigates the nuances of media framing on public perception and understanding by examining how events are presented within news articles. Unlike previous research that primarily focused on word choice as a framing device, this work explores the comprehensive narrative construction through events and their relations. Our method integrates event extraction, Cross-Document Event Coreference (CDEC), and causal relationship among events to extract framing devices employed by the media to assess their role in framing the narrative. We evaluate our approach with a media attitude detection task and show that the use of event mentions, event cluster descriptors, and their causal relations effectively captures the subtle nuances of framing, thereby providing deeper insights into the attitudes conveyed by news articles. The experimental results show the framing device models surpass the baseline models and offer a more detailed and explainable analysis of media framing effects. We make the source code and dataset publicly available. 1