Extraction of Temporal Networks from Term Co-occurrences in Online Textual Sources

TL;DR

This paper introduces a method to extract and analyze time-varying networks of entities from online news, comparing these networks with financial data to reveal hidden relations.

Contribution

It presents a novel approach for continuous extraction of dynamic entity networks from unstructured news and evaluates their robustness against a benchmark model.

Findings

Small but significant overlap between news-derived and CDS-based networks

Method effectively captures evolving relations in financial news

Benchmark model assesses co-occurrence significance

Abstract

A stream of unstructured news can be a valuable source of hidden relations between different entities, such as financial institutions, countries, or persons. We present an approach to continuously collect online news, recognize relevant entities in them, and extract time-varying networks. The nodes of the network are the entities, and the links are their co-occurrences. We present a method to estimate the significance of co-occurrences, and a benchmark model against which their robustness is evaluated. The approach is applied to a large set of financial news, collected over a period of two years. The entities we consider are 50 countries which issue sovereign bonds, and which are insured by Credit Default Swaps (CDS) in turn. We compare the country co-occurrence networks to the CDS networks constructed from the correlations between the CDS. The results show relatively small, but significant overlap between the networks extracted from the news and those from the CDS correlations.