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The coverage of different stakeholders mentioned in the news articlessignificantly impacts the slant or polarity detection of the concerned newspublishers. For instance, the pro-government media outlets would give morecoverage to the government stakeholders to increase their accessibility to thenews audiences. In contrast, the anti-government news agencies would focus moreon the views of the opponent stakeholders to inform the readers about theshortcomings of government policies. In this paper, we address the problem ofstakeholder extraction from news articles and thereby determine the inherentbias present in news reporting. Identifying potential stakeholders inmulti-topic news scenarios is challenging because each news topic has differentstakeholders. The research presented in this paper utilizes both contextualinformation and external knowledge to identify the topic-specific stakeholdersfrom news articles. We also apply a sequential incremental clustering algorithmto group the entities with similar stakeholder types. We carried out all ourexperiments on news articles on four Indian government policies published bynumerous national and international news agencies. We also further generalizeour system, and the experimental results show that the proposed model can beextended to other news topics.

'If you build they will come': Automatic Identification of News-Stakeholders to detect Party Preference in News Coverage