  Navigating the complex landscape of news articles involves understanding the
various actors or entities involved, referred to as news stakeholders. These
stakeholders, ranging from policymakers to opposition figures, citizens, and
more, play pivotal roles in shaping news narratives. Recognizing their
stakeholder types, reflecting their roles, political alignments, social
standing, and more, is paramount for a nuanced comprehension of news content.
Despite existing works focusing on salient entity extraction, coverage
variations, and political affiliations through social media data, the automated
detection of stakeholder roles within news content remains an underexplored
domain. In this paper, we bridge this gap by introducing an effective approach
to classify stakeholder types in news articles. Our method involves
transforming the stakeholder classification problem into a natural language
inference task, utilizing contextual information from news articles and
external knowledge to enhance the accuracy of stakeholder type detection.
Moreover, our proposed model showcases efficacy in zero-shot settings, further
extending its applicability to diverse news contexts.
