At The Markup we pioneered an array of scientifically inspired methods that used automation and computational power to supercharge our journalism. Reflecting on our work, I came up with 10 of the most important lessons I’ve learned using this approach.
1) Important ≠ secret
In a resource-constrained world, choosing a topic to investigate is the most important decision a newsroom makes.
At The Markup, we developed an investigative checklist that reporters filled out before embarking on a project. Top of the checklist was not novelty, but scale—how many people were affected by the problem we were investigating. In other words, we chose to tackle things that were important but not secret.
For instance, anyone using Google has probably noticed that Google takes up a lot of the search result page for its own properties. Nevertheless, we decided to invest nearly a year into quantifying how much Google was boosting its own products over direct links to source material because the quality of Google search results affects nearly everyone in the world.
This type of work has an impact. The European Union has now passed a law banning tech platforms from this type of self-preferencing, and there is legislation pending in Congress to do the same.