Novelty in news search: a longitudinal study of the 2020 US elections

TL;DR

This study examines how news search engines perform in delivering novel information during the 2020 US elections, revealing regional and algorithmic differences that impact political visibility.

Contribution

It introduces a longitudinal methodology to measure novelty in news search results during a major electoral event, highlighting search engine and regional disparities.

Findings

More new items emerge for election-related queries.

Differences exist across search engines and regions over time.

Imbalances in search results influence political candidate visibility.

Abstract

The 2020 US elections news coverage was extensive, with new pieces of information generated rapidly. This evolving scenario presented an opportunity to study the performance of search engines in a context in which they had to quickly process information as it was published. We analyze novelty, a measurement of new items that emerge in the top news search results, to compare the coverage and visibility of different topics. We conduct a longitudinal study of news results of five search engines collected in short-bursts (every 21 minutes) from two regions (Oregon, US and Frankfurt, Germany), starting on election day and lasting until one day after the announcement of Biden as the winner. We find more new items emerging for election related queries ("joe biden", "donald trump" and "us elections") compared to topical (e.g., "coronavirus") or stable (e.g., "holocaust") queries. We demonstrate differences across search engines and regions over time, and we highlight imbalances between candidate queries. When it comes to news search, search engines are responsible for such imbalances, either due to their algorithms or the set of news sources they rely on. We argue that such imbalances affect the visibility of political candidates in news searches during electoral periods.