CHS: Small: Assessing the Role of Platform Algorithms in Shaping News Attention

This research will conduct a series of algorithm audits of two major media platforms that dominate human attention to news information: Google and Facebook. Online platforms such as these are used by more than half of Americans for receiving news information. But little is known about how the algorithmic curation processes of these information intermediaries serve to drive public exposure and salience of quality news information. The prevalence and magnitude of the problem is unclear as are the attention patterns of real users around such information. More generally: What types and sources of news are made available and prioritized, and are there diverse perspectives represented in the algorithmic curation of these major platforms? The overriding question this project seeks to answer is: How do platform algorithms shape the availability, magnitude, and quality of human attention to news media? Answering these questions will provide key insights into the role of these platforms in providing diverse and quality information. These contributions will advance the fields of computational journalism and communication, and information science both methodologically and in relation to social scientific questions of information content dissemination, consumption, and mediation. This project will provide knowledge that will impact the use, design, and perhaps even governance of algorithmic curation systems in mediating and shaping attention of personal and civic importance, thus helping to improve the public's access to diverse and quality news information. News article URLs surfaced specifically on the "In the News" section of Google and the "Trends" section of Facebook will be collected both automatically and using crowdsourcing methods so that factors such as news source diversity, quality, and attention can be measured as they relate to and vary with mediating considerations such as personalization, locality, and temporality. A variety of metrics will be derived in order to benchmark the information surfaced and to put it into a broader context of media availability. Moreover, an innovative marriage of data from an industry news metrics collaborator will enable a transformative understanding of how algorithmic curation affects attention patterns at scale. Insights from these studies may lead to the identification of design opportunities for platforms or end-users to improve and fill gaps in the diversity or quality of news information available around issues of personal or civic importance. The main intellectual contributions of this project are (1) to develop repeatable algorithm audit methods that can be deployed now and in the future, and (2) to apply those methods to develop new knowledge of how algorithmically-driven information intermediaries affect news information exposure and attention patterns.