News articles are published all over the world to cover important events. Journalists need to keep track of ongoing events in a fair and accountable manner and analyze them for newsworthiness. It requires an enormous amount of time and effort for journalists to process information coming from mainstream news media, social media from all over the world, as well as policy and law circulated by governments and international organizations. News articles published by different news providers and reporters may also be subjective due to the influence of reporters’ backgrounds, world views and opinions. In today’s journalistic practice there is a lack of computational methods to support journalists to investigate fairness and monitor and analyze massive information streams. In this paper we present a model-based approach to analyze the perspectives of news publishers and monitor the progression of news events from various perspectives. The key concepts in the news domain such as the news events and their contextual information is represented across various dimensions in a knowledge graph. We presented a multi dimensional and comparative news event analysis method for analyzing news article variants and for uncovering underlying storylines. To show the applicability of the proposed method in real life, we also demonstrate a running example. The utilization of a model-based approach ensures the adaptability of our proposed method for representing a wide array of domain concepts within the news domain.