Choosing the political party nominees, who will appear on
the ballot for the US presidency, is a long process that starts
two years before the general election. The news media plays
a particular role in this process by continuously covering the
state of the race. How can this news coverage be characterized?
Given that there are thousands of news organizations,
but each of us is exposed to only a few of them, we might
be missing most of it. Online news aggregators, which aggregate
news stories from a multitude of news sources and
perspectives, could provide an important lens for the analysis.
One such aggregator is Google’s Top stories, a recent addition
to Google’s search result page. For the duration of 2019, we
have collected the news headlines that Google Top stories has
displayed for 30 candidates of both US political parties. Our
dataset contains 79,903 news story URLs published by 2,168
unique news sources. Our analysis indicates that despite this
large number of news sources, there is a very skewed distribution
of where the Top stories are originating, with a very
small number of sources contributing the majority of stories.
We are sharing our dataset1 so that other researchers can answer
questions related to algorithmic curation of news as well
as media agenda setting in the context of political elections.
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Auditing Google's Search Headlines as a Potential Gateway to Misleading Content: Evidence from the 2020 US Election
The prevalence and spread of online misinformation during the 2020 US presidential election served to perpetuate a false belief in widespread election fraud. Though much research has focused on how social media platforms connected people to election-related rumors and conspiracy theories, less is known about the search engine pathways that linked users to news content with the potential to undermine trust in elections. In this paper, we present novel data related to the content of political headlines during the 2020 US election period. We scraped over 800,000 headlines from Google's search engine results pages (SERP) in response to 20 election-related keywords—10 general (e.g., "Ballots") and 10 conspiratorial (e.g., "Voter fraud")—when searched from 20 cities across 16 states. We present results from qualitative coding of 5,600 headlines focused on the prevalence of delegitimizing information. Our results reveal that videos (as compared to stories, search results, and advertisements) are the most problematic in terms of exposing users to delegitimizing headlines. We also illustrate how headline content varies when searching from a swing state, adopting a conspiratorial search keyword, or reading from media domains with higher political bias. We conclude with policy recommendations on data transparency that allow researchers to continue to monitor search engines during elections.
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- NSF-PAR ID:
- 10428058
- Date Published:
- Journal Name:
- Journal of Online Trust and Safety
- Volume:
- 1
- Issue:
- 4
- ISSN:
- 2770-3142
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.54501/jots.v1i4.72
