2 research outputs found
Novelty in news search: a longitudinal study of the 2020 US elections
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