Abstract

Modern protests are not limited to on-the-ground operations, and the ease and speed at which users can upload images to social media platforms has enabled protests to manifest online. Previous analysis of protest imagery from social media sites categorized these images into groups including texts, screenshots, memes, and artwork. However, large-scale manual annotation to identify different types of images is not feasible. By applying machine learning to a large Twitter dataset focused on the Stop Asian Hate movement, we found the type of image an account posted during protests on Twitter is tied to the credibility and political leaning of posted content, type of witnessing (remote or connective), and community formation.

Date

2021

Authors

Oliver Melbourne Allen, Emily Chen, Emilio Ferrara

Conference

2021 IEEE 18th international conference on mobile ad hoc and smart systems (MASS)

Pages

667-668

Publisher

IEEE