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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" 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. CCS CONCEPTS• Human-centered computing → Empirical studies in HCI;• Information systems → Search interfaces.

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.