Ulloa, Roberto; Makhortykh, Mykola; Urman, Aleksandra; Kulshrestha, Juhi (2023). Novelty in News Search: A Longitudinal Study of the 2020 US Elections. Social science computer review Sage 10.1177/08944393231195471
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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. Using virtual agents that simulate human web browsing behavior to collect search engine result pages, 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 that our method captures sudden changes in highly covered news topics as well as multiple differences across search engines and regions over time. We highlight novelty imbalances between candidate queries which affect their visibility during electoral periods, and conclude that, when it comes to news, search engines are responsible for such imbalances, either due to their algorithms or the set of news sources that they rely on.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
03 Faculty of Business, Economics and Social Sciences > Social Sciences > Institute of Communication and Media Studies (ICMB) |
UniBE Contributor: |
Makhortykh, Mykola |
Subjects: |
000 Computer science, knowledge & systems 000 Computer science, knowledge & systems > 020 Library & information sciences 000 Computer science, knowledge & systems > 070 News media, journalism & publishing 300 Social sciences, sociology & anthropology 300 Social sciences, sociology & anthropology > 320 Political science |
ISSN: |
0894-4393 |
Publisher: |
Sage |
Language: |
English |
Submitter: |
Mykola Makhortykh |
Date Deposited: |
13 Nov 2023 14:18 |
Last Modified: |
13 Nov 2023 14:18 |
Publisher DOI: |
10.1177/08944393231195471 |
Uncontrolled Keywords: |
search engine, AI, algorithm, algorithm audit, elections, US, novelty |
BORIS DOI: |
10.48350/188805 |
URI: |
https://boris.unibe.ch/id/eprint/188805 |
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