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ere is an overwhelming number of news articles published every day around the globe. Following the evolution of a news-story is a di cult task given that there is no such mechanism available to track back in time to study the di usion of the relevant events in digital news feeds. e techniques developed so far to extract meaningful information from a massive corpus rely on similarity search, which results in a myopic loopback to the same topic without providing the needed insights to hypothesize the origin of a story that may be completely di erent than the news today. In this paper, we present an algorithm that mines historical data to detect the origin of an event, segments the timeline into disjoint groups of coherent news articles, and outlines the most important documents in a timeline with a so probability to provide a be er understanding of the evolution of a story.alitative and quantitative approaches to evaluate our framework demonstrate that our algorithm discovers statistically signi cant and meaningful stories in reasonable time. Additionally, a relevant case study on a set of news articles demonstrates that the generated output of the algorithm holds the promise to aid prediction of future entities in a story.

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Semantics in natural language processing is largely dependent on contextual relationships between words and entities in a document collection. The context of a word may evolve. For example, the word ``apple'' currently has two contexts -- a fruit and a technology company. The changes in the context of words or entities in text data such as scientific publications, and news articles can help us understand the evolution of innovation or events of interest. In this work, we present a new diffusion-based temporal word embedding model that can capture short and long-term changes in the semantics of entities in different domains. Our model captures how the context of each entity shifts over time. Existing temporal word embeddings capture semantic evolution at a discrete/granular level, aiming to study how a language developed over a long period. Unlike existing temporal embedding methods, our approach provides temporally smooth embeddings, facilitating prediction and trend analysis better than those of existing models. Extensive evaluations demonstrate that our proposed temporal embedding model performs better in sense-making and predicting relationships between entities in the future compared to other existing models.