In 2016 I co wrote the State of Automated Fact Checking [PDF], an in-depth report looking at where we are with automated factchecking globally, and where we could get to with the necessary funding.

It set out Full Fact’s roadmap for our own work on automated fact checking, and our design principles for the tools we were starting to build. We proposed principles of collaboration for factchecking organisations, researchers and computer scientists around the world.

It’s split into two parts:

• Part One: A roadmap for automated factchecking
• Part Two: What we can do now and what remains to be done

Summary of its findings

• We can scale up and speed up fact checking dramatically using technology that exists now.
  
• We are months—and relatively small amounts of money—away from handing practical automated tools to fact checkers and journalists. This is not the horizon of artificial intelligence; it is simply the application of existing technology to factchecking.
  
• Automated fact checking projects are taking place across the world, but they are fragmented. This means fact checkers and researchers are wasting time and money reinventing the wheel.
  
• We propose open standards. Automated fact checking will come to fruition in a more coherent and efficient way if key players think in terms of similar questions and design principles, learn from existing language processing tasks, and build shared infrastructure.
  
• International collaboration is vital so that the system works in several languages and countries.
  
• Research into machine learning must continue, but we can make serious progress harnessing other technologies in the meantime.