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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The ‘Fairness Doctrine’ lives on? Theorizing about the Algorithmic News Curation of Google’s Top Stories
When one searches for political candidates on Google, a panel composed of recent news stories, known as Top stories, is commonly shown at the top of the search results page. These stories are selected by an algorithm that chooses from hundreds of thousands of articles published by thousands of news publishers. In our previous work, we identified 56 news sources that contributed 2/3 of all Top
stories for 30 political candidates running in the primaries of 2020 US Presidential Election. In this paper, we survey US voters to elicit their familiarity and trust with these 56 news outlets. We find that some of the most frequent outlets are not familiar to all voters (e.g. The Hill or Politico), or particularly trusted by voters of any political stripes (e.g. Washington Examiner or The Daily Beast). Why then, are such sources shown so frequently in Top stories? We theorize that Google is sampling news articles from sources with different political leanings to offer a balanced coverage. This is reminiscent of the so-called “fairness doctrine” (1949-1987) policy in the United States that required broadcasters (radio or TV stations) to air contrasting views about controversial matters. Because there are fewer right-leaning publications than center or left-leaning ones, in order to maintain this “fair” balance, hyper-partisan far-right news sources of low trust receive more visibility than some news sources that are more familiar to and trusted by the public.
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- Award ID(s):
- 1751087
- NSF-PAR ID:
- 10177074
- Date Published:
- Journal Name:
- The 31st ACM Conference on HyperText and Social Media
- Page Range / eLocation ID:
- 59-68
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/https://doi.org/10.1145/3372923.3404794
