# Analyzing Evolving Stories in News Articles

## TL;DR

This paper introduces an algorithm that tracks the evolution of news stories over time by mining historical data, segmenting articles, and identifying key documents to better understand how stories develop and originate.

## Contribution

It presents a novel method for analyzing the temporal evolution of news stories, addressing limitations of similarity-based approaches by detecting story origins and key events.

## Key findings

- The algorithm effectively discovers meaningful stories in reasonable time.
- It statistically validates the significance of detected stories.
- Case study shows potential for predicting future story entities.

## Abstract

There is an overwhelming number of news articles published every day around the globe. Following the evolution of a news-story is a difficult task given that there is no such mechanism available to track back in time to study the diffusion of the relevant events in digital news feeds. The 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 different 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 soft probability to provide a better understanding of the evolution of a story. Qualitative and quantitative approaches to evaluate our framework demonstrate that our algorithm discovers statistically significant 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.