In the digital age, the volume of news data available from diverse sources is vast and continually growing. On the one hand, the quantity of information can overwhelm reporters and on the other hand, news reporting is further complicated by the inherent complexities of multifaceted events that evolve over time, as well as the biases and perspectives that different reporters and media outlets bring to their coverage. Despite such challenges, journalists must report on events in a timely and ethical manner. However, there is a lack of computational methods for analyzing massive news streams in an explainable and responsible way. In this paper, we propose a content based news analysis framework based on news comparison that enables modeling various analytical tasks such as analyzing the perspectives of news publishers, monitoring the progression of news events from various perspectives, exploring the evolution patterns of events over time and analyzing news article variants and for uncovering underlying story-lines. Our approach utilizes a knowledge graph to represent key concepts in the news domain, such as events and their contextual information, across various dimensions. This facilitates a multi-dimensional and comparative analysis of news article variants. We demonstrate the practical applicability of our method through a running example. By adopting a model-based approach, our framework offers the flexibility needed to represent a broad spectrum of domain concepts.