Drawing the Faces of “Perpetual Foreigners”: A Multimodal Narrative Analysis of #StopAsianHate on Instagram

Abstract

Racism against Asians has been a historical problem around the world. The Internet turned into a battleground for racist and antiracist discourses and activism during the pandemic. As such, discursive engagement is often organized around hashtags such as #StopAsianHate, hashtag activism has become one of the key manifestations of the movement. Based on the affective narrative theory, this study aims to understand how multimodal messages including texts and visuals can be used to derive narratives and affects. By examining 46,621 Instagram posts published from January 1, 2020 to November 1, 2022, this study finds #StopAsianHate was short-lived and single-peaked and featured almost an equal amount of positive and negative sentiments. By also analyzing posts with more than 100,000 total interactions (N = 92), this study further delineates multiple affective narratives derived at the iconic, grammatical, and collective levels through layered interpretation. The findings suggest all hashtag movements are equal, pointing to the need to analyze plot twists and ugly feelings in affective narratives using multimodal data and methods.