Computational Communication Methods for Examining Problematic News-Sharing Practices on Facebook at Scale

Social media, in general, and Facebook in particular, have been clearly identified as important platforms for the dissemination of mis- and disinformation and related problematic content. However, the patterns and processes of such dissemination are still not sufficiently understood. We detail a novel computational methodology that focusses on the identification of high-profile vectors of "fake news" and other problematic information in public Facebook spaces. The method enables examination of networks of content sharing that emerge between public pages and groups, and external sources, and the study of longitudinal dynamics of these networks as interests and allegiances shift and new developments (such as the COVID-19 pandemic or the US presidential elections) drive the emergence or decline of dominant themes. Through a case study of content captured between 2016 and 2021, we demonstrate how this methodology allows the development of a new and more comprehensive picture of the overall impact of "fake news," in all its forms, on contemporary societies.