HOW FACT-CHECKERS ARE BECOMING MACHINE LEARNERS: A CASE OF META’S THIRD PARTY PROGRAMME

Resumen

A recent development in the field of fact-checking is what some scholars call the “debunking turn” in which fact-checking organisations move from fact-checking expressions of politicians and public figures to checking claims made on social media. A main driver of this change is the proliferation of a paid program initiated by Meta, where fact-checkers check and label claims on the platform in exchange for monetary remuneration. This paper draws on interviews with and fieldwork amongst fact-checkers who are or have been part of the Meta partnership. Based on the empirical insights we argue that the human-machine assemblage in fact-checking is (1) enabling a move beyond the ‘debunking turn’ by turning journalists into ‘machine learners’ and (2) cements a ‘politics of demarcation’ in which public contestation over public facts is diminished and moved into networked infrastructures. With this argument, the paper highlights an additional aspect of the platformisation of journalism, as the labelling and claim-checking work of journalists now also enables large tech platforms to expand technical infrastructures that commodify journalistic work by turning it into training data aimed at improving their ML systems and algorithms. This enables platforms to move further beyond their current market role, as they also participate in the further industrialisation and standardisation of fact-checking. As large tech companies become industry leaders in the provision of ML systems, for example, for fact-checking, the need to understand what politics they produce equally increases, as they become integral in the production of democratic ideals of citizens and public debate.