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 researchersmore »
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 more »
- Award ID(s):
- 1751087
- Publication Date:
- NSF-PAR ID:
- 10177074
- Journal Name:
- The 31st ACM Conference on HyperText and Social Media
- Page Range or eLocation-ID:
- 59-68
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Concerns about the spread of misinformation online via news articles have led to the development of many tools and processes involving human annotation of their credibility. However, much is still unknown about how different people judge news credibility or the quality or reliability of news credibility ratings from populations of varying expertise. In this work, we consider credibility ratings from two “crowd” populations: 1) students within journalism or media programs, and 2) crowd workers on UpWork, and compare them with the ratings of two sets of experts: journalists and climate scientists, on a set of 50 climate-science articles. We find that both groups’ credibility ratings have higher correlation to journalism experts compared to the science experts, with 10-15 raters to achieve convergence. We also find that raters’ gender and political leaning impact their ratings. Among article genre of news/opinion/analysis and article source leaning of left/center/right, crowd ratings were more similar to experts respectively with opinion and strong left sources.
-
Concerns about the spread of misinformation online via news articles have led to the development of many tools and processes involving human annotation of their credibility. However, much is still unknown about how different people judge news credibility or the quality or reliability of news credibility ratings from populations of varying expertise. In this work, we consider credibility ratings from two “crowd” populations: 1) students within journalism or media programs, and 2) crowd workers on UpWork, and compare them with the ratings of two sets of experts: journalists and climate scientists, on a set of 50 climate-science articles. We find that both groups’ credibility ratings have higher correlation to journalism experts compared to the science experts, with 10-15 raters to achieve convergence. We also find that raters’ gender and political leaning impact their ratings. Among article genre of news/opinion/analysis and article source leaning of left/center/right, crowd ratings were more similar to experts respectively with opinion and strong left sources.
-
Misinformation in online spaces can stoke mistrust of established media, misinform the public and lead to radicalization. Hence, multiple automated algorithms for misinformation detection have been proposed in the recent past. However, the fairness (e.g., performance across left- and right- leaning news articles) of these algorithms has been repeatedly questioned, leading to decreased trust in such systems. This work motivates and grounds the need for an audit of machine learning based misinformation detection algorithms and possible ways to mitigate bias (if found). Using a large (N>100K) corpus of news articles, we report that multiple standard machine learning based misinformation detection approaches are susceptible to bias. Further, we find that an intuitive post-processing approach (Reject Option Classifier) can reduce bias while maintaining high accuracy in the above setting. The results pave the way for accurate yet fair misinformation detection algorithms.
-
Many news outlets allow users to contribute comments on topics about daily world events. News articles are the seeds that spring users' interest to contribute content, that is, comments. A news outlet may allow users to contribute comments on all their articles or a selected number of them. The topic of an article may lead to an apathetic user commenting activity (several tens of comments) or to a spontaneous fervent one (several thousands of comments). This environment creates a social dynamic that is little studied. The social dynamics around articles have the potential to reveal interesting facets of the user population at a news outlet. In this paper, we report the salient findings about these social media from 15 months worth of data collected from 17 news outlets comprising of over 38,000 news articles and about 21 million user comments. Analysis of the data reveals interesting insights such as there is an uneven relationship between news outlets and their user populations across outlets. Such observations and others have not been revealed, to our knowledge. We believe our analysis in this paper can contribute to news predictive analytics (e.g., user reaction to a news article or predicting the volume of comments postedmore »
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/https://doi.org/10.1145/3372923.3404794
