NewsHomepages: Homepage Layouts Capture Information Prioritization Decisions

TL;DR

This paper introduces NewsHomepages, a large dataset of website homepages over three years, and models to infer news item importance, demonstrating applications in ranking local policies and understanding information prioritization.

Contribution

The work provides a new dataset of homepage layouts and develops models for inferring news significance, with broader implications for understanding organizational information prioritization.

Findings

Successfully created a dataset of 3,000+ homepages over three years

Developed models for pairwise comparison of news importance

Applied models to rank local policies by newsworthiness

Abstract

Information prioritization plays an important role in how humans perceive and understand the world. Homepage layouts serve as a tangible proxy for this prioritization. In this work, we present NewsHomepages, a large dataset of over 3,000 new website homepages (including local, national and topic-specific outlets) captured twice daily over a three-year period. We develop models to perform pairwise comparisons between news items to infer their relative significance. To illustrate that modeling organizational hierarchies has broader implications, we applied our models to rank-order a collection of local city council policies passed over a ten-year period in San Francisco, assessing their "newsworthiness". Our findings lay the groundwork for leveraging implicit organizational cues to deepen our understanding of information prioritization.