Algorithmically elevating news quality amid the 2020 coronavirus pandemic

A draft case study framework for identifying ways to algorithmically elevate quality news during breaking news cycles such as COVID-19.

What does it mean to be able to algorithmically promote the best examples of journalism during a breaking news cycle? The 2020 coronavirus pandemic has offered a challenge to this problem. Since algorithms are rule-bound, and information on the virus is complicated and ever-changing, the question of whether quality journalism on the topic can be identified in structured or measured ways has been a critical one to answer.

The NewsQ initiative will present a draft framework to think through what a structured analysis might look like. A work in progress, this casebook will be updated with examples, reference documents, and case studies in order to flesh out the feasibility of analyzing the quality of knowledgeable news at a larger scale.

The session will be an under-the-hood look into the process of creating the framework, and the challenges and possibilities of algorithmically elevating quality in a climate of evolving and breaking news. It will also include a Q&A where audience members can discuss the framework and provide feedback.

Suggested Speaker(s)

  • Connie Moon Sehat
    NewsQ Director, NewsQ - Hacks/Hackers
  • Jeff Jarvis
    Director of the Tow-Knight Center for Entrepreneurial Journalism, Craig Newmark Graduate School of Journalism at CUNY