Neural Total Variation Distance Estimators for Changepoint Detection in News Data

2025-06-23

AI Summary

This study addresses the challenge of modeling dynamic evolution of public discourse driven by major events in high-dimensional, sparse, and noisy news corpora. We propose a neural change-point detection method grounded in a “learning-to-confuse” framework: a temporal segmentation classifier discriminates text windows, while the total variation distance quantifies semantic distribution shifts to automatically localize semantic turning points. Inspired by phase transition detection in physical systems, our approach transcends reliance on domain expertise or manual annotations, enabling end-to-end, quantitative identification of discourse shifts. Evaluated on synthetic data and real-world *Guardian* news corpora—including coverage of 9/11, the COVID-19 pandemic, and U.S. presidential elections—our method demonstrates superior robustness to noise and enhanced interpretability in change-point detection compared to baselines.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Knowledge Representation and Reasoning: Action, Change, and Causality
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Web Mining and Content Analysis: Topic discovery and tracking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Detecting when public discourse shifts in response to major events is crucial for understanding societal dynamics. Real-world data is high-dimensional, sparse, and noisy, making changepoint detection in this domain a challenging endeavor. In this paper, we leverage neural networks for changepoint detection in news data, introducing a method based on the so-called learning-by-confusion scheme, which was originally developed for detecting phase transitions in physical systems. We train classifiers to distinguish between articles from different time periods. The resulting classification accuracy is used to estimate the total variation distance between underlying content distributions, where significant distances highlight changepoints. We demonstrate the effectiveness of this method on both synthetic datasets and real-world data from The Guardian newspaper, successfully identifying major historical events including 9/11, the COVID-19 pandemic, and presidential elections. Our approach requires minimal domain knowledge, can autonomously discover significant shifts in public discourse, and yields a quantitative measure of change in content, making it valuable for journalism, policy analysis, and crisis monitoring.

Problem

• Detecting shifts in public discourse from news data
• Handling high-dimensional sparse noisy data for changepoints
• Quantifying content change without extensive domain knowledge

Innovation

• Neural networks for changepoint detection
• Learning-by-confusion scheme adaptation
• Total variation distance estimation