This computational journalism project was developed to help political journalists by providing a useable yet comprehensive summary of the content and sentiment that flows through social media during presidential campaigns. Over the course of the 2016 presidential campaign, the Illuminating team collected all of the Facebook and Twitter messages by the 17 Republican and five Democratic candidates. The research team also developed a system to categorize each post by message type: calls to action, image, advocacy, issue, and attack messages. This system could be applied to any campaign, and is easily searchable by journalists to find trends in and compare politicians’ communications.
Project lead: Jenny Stromer-Galley
In 2016, the Illuminating project was supported by a Tow Fellowship, and was able to build an interactive website targeted to journalists, academics, and the public that provided real-time analysis of the U.S. presidential candidates’ Twitter and Facebook accounts. The Illuminating website (http://illuminating.ischool.syr.edu) generated news coverage and spawned academic conference presentations and journal articles. The Illuminating project would like to expand its work to encompass the 2018 midterm elections. Midterms pose great challenges for newsrooms. Cuts in political reporters (especially state and local reporters), a complex, multi-campaign context, and the ever-expanding communication environment challenge the abilities of newsrooms to provide comprehensive coverage of political campaigns and detect shifting public opinion. The Illuminating project's goal is to expand its website and analysis to the gubernatorial, House, and Senate races, to add topic analysis to its existing categories, and to further improve its analysis of the public’s discussion around the campaigns. Continuing its effort to support journalists, Illuminating aims to automate article briefs based on real-time analysis of social media messages so as to allow journalists easy understanding of key insights so they can report on them. The project is also working on a partnership with the Associated Press to observe newsroom practices and share leads for potential stories.
Project lead: Jenny Stromer-Galley
Algorithms are playing a growing role in determining which news stories reach audiences as they increasingly assist or replace humans in the distribution and curation of news. This development begs the question: What kind of information landscapes are algorithmic platforms creating for individuals and communities as they direct millions of people to news via recommendation engines and search interfaces? Indeed, such engines and interfaces are currently among the main sources of traffic to news sites, making them crucial objects for study.
This study will compare thousands of real-world news searches conducted by a large and diverse set of participants across different digital services (e.g., Google, YouTube and Facebook) in order to gain insight into patterns of news distribution on the most popular algorithmically driven gatekeeping platforms. It will examine if news search algorithms promote filter bubbles and fragmented audiences as feared by some scholars and in popular media—or, alternatively, if they perhaps construct relatively homogeneous and uniform news landscapes online.
The authors' previous study with the Tow Center indicated that, on Google News, people of different political leanings and backgrounds were recommended highly similar news diets regarding the 2016 U.S. presidential election, sourced primarily from a small number of mainstream national outlets. This challenged the assumption that algorithms invariably encourage echo chambers while disrupting power structures within media industries. This follow-up project expands these research questions into a broader set of platforms and topics on the news, paying special attention to the role that local news sources are assigned in these environments.
Project leads: Seth C. Lewis, Efrat Nechushtai, Rodrigo Zamith