Project Example: The Fake News Field Guide
When the debate on fake news was raging in the wake of the 2016 US elections, the Public Data Lab was approached by First Draft, a non-profit coalition of fact-checking initiatives devoted to provide practical and ethical guidance in how to find, verify and publish content sourced from the social web.
Members of the Public Data Lab had been spearheading the research on innovative digital methods to explore social interactions on online platforms and First Draft believed we could help in finding new approaches to detect and debunk fake news - a classic computational social science task.
As we started working with First Draft and its vast networks of journalists and social media actors, we quickly realised the very notion of “fake news” was highly problematic. The notion implied a clear-cut separation between true and fake news which is nonexistent in media studies literature and even more in online communication, where one finds instead a flourishing diversity of satirical, partisan, click-baity, outraging, conspiratorial, humoristic contents largely overlapping with the more institutional, mainstream, commercial news. Because of its inherent ambiguity, the notion of “fake news” was soon appropriated by all sorts of political actors, who started to use it as a polemical weapon against their opponents and against all press coverage that they disliked.
Such a situation forced us to shift the focus of our data inquiry into online misinformation. Instead of devising tools to separate “good” from “bad” news, we invited journalists and fact-checkers to join us in a series of data sprints (see a description of this format of workshop here) to co-design a set of mapmaking recipes that would help develop a richer and more complex understanding of digital misinformation - from a binary true/false question, to a.
The result was the Field Guide to Fake News and Other Information Disorders, a digital book gathering a series of methodological recipes to study various aspects of online misinformation, e.g. its circulation; its monetization strategies; the use of Internet memes and images; the connection with trolling, etc.
Below is an example of one of these recipes: the one exploring the overlapping of the marketing infrastructure of mainstream and “fake” news, by comparing the trackers on which they rely.
Acknowledging rather than denying our hesitation about how to deal with fake news and using it as an occasion for co-design with the actors in the field, we progressively discovered that the most relevant contribution to the fight against misinformation had little to do with detecting of lies or inaccuracies, and much to do with revealing the multiple ways in which public debate its polluted by the information disorders of online media.