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Uncovering Coordinated Communities on Twitter During the 2020 U.S. Election by Renan S. Linhares; José M. Rosa; Carlos H. G. Ferreira; Fabricio Murai; Gabriel Nobre; Jussara Almeida is a scholarly article available to read on EtoBox.

What is Uncovering Coordinated Communities on Twitter During the 2020 U.S. Election about?

A large volume of content related to claims of election fraud, often associated with hate speech and extremism, was reported on Twitter during the 2020 US election, with evidence that coordinated efforts took place to promote such content on the platform. In response, Twitter announced the suspension of thousands of user accounts allegedly involved in such actions. Motivated by these events, we here propose a novel network-based approach to uncover evidence of coordination in a set of user interactions. Our approach is designed to address the challenges incurred by the often sheer volume of noisy edges in the network (i.e., edges that are unrelated to coordination) and the effects of data sampling. To that end, it exploits the joint use of two network backbone extraction techniques, namely Disparity Filter and Neighborhood Overlap, to reveal strongly tied groups of users (here referred to as communities) exhibiting repeatedly common behavior, consistent with coordination. We employ our strategy to a large dataset of tweets related to the aforementioned fraud claims, in which users were labeled as suspended, deleted or active, according to their accounts status after the election. O

Author

Renan S. Linhares; José M. Rosa; Carlos H. G. Ferreira; Fabricio Murai; Gabriel Nobre; Jussara Almeida

Publisher

IEEE

Published

2022

Language

EN