Micro-Reader Responses to the 'Angry Black Woman' Stereotype in Viral TikTok Content: A Critical Textual Analysis

This study investigates how the Angry Black Woman (ABW) stereotype is negotiated, reframed, and resisted through collective discursive practices within digital environments. By examining the most-liked comments in viral TikTok videos featuring Black women, the study employs a qualitative design that integrates micro-reading (close textual analysis) with Critical Discourse Analysis (CDA). The dataset consists of 60 comments sourced from three purposively selected viral videos, each meeting a minimum threshold of one million views and representing distinct emotional contexts, including sensory overstimulation, workplace interactions, and everyday affective complexity. The findings demonstrate that commenters do not merely reproduce the Angry Black Woman (ABW) stereotype but instead enact forms of symbolic resistance by reinterpreting presumed “anger” through contextualized and humanizing explanations such as sensory fatigue, mental exhaustion, and menstrual cycles. The comments additionally reveal the emotional biases embedded in professional settings, where neutral facial expressions are frequently misread as hostility. Within these digital spaces, comment sections operate as critical sites of affective solidarity and communal validation, facilitated through emojis, humor, and shared experiential narratives. The study concludes that platforms like TikTok function as transformative sites through which Black women reclaim narrative authority over their emotional identities, dismantle racialized tropes, and cultivate resilient communal spaces. Theoretically, this research contributes to ongoing discussions on digital discourse, racial representation, and marginalized agency in the algorithmic era.

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Publication Details

Journal
JELITA
Published
2026-02-11
DOI
https://doi.org/10.56185/jelita.v7i1.1214
Primary Topic
Digital Communication and Language
Type
article
Field-Weighted Citation Impact
0.00
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article

Micro-Reader Responses to the 'Angry Black Woman' Stereotype in Viral TikTok Content: A Critical Textual Analysis

Syaila Nur Amaliyah
JELITA
Digital Communication and Language
article

Micro-Reader Responses to the 'Angry Black Woman' Stereotype in Viral TikTok Content: A Critical Textual Analysis

Syaila Nur Amaliyah
article en

Abstract

This study investigates how the Angry Black Woman (ABW) stereotype is negotiated, reframed, and resisted through collective discursive practices within digital environments. By examining the most-liked comments in viral TikTok videos featuring Black women, the study employs a qualitative design that integrates micro-reading (close textual analysis) with Critical Discourse Analysis (CDA). The dataset consists of 60 comments sourced from three purposively selected viral videos, each meeting a minimum threshold of one million views and representing distinct emotional contexts, including sensory overstimulation, workplace interactions, and everyday affective complexity. The findings demonstrate that commenters do not merely reproduce the Angry Black Woman (ABW) stereotype but instead enact forms of symbolic resistance by reinterpreting presumed “anger” through contextualized and humanizing explanations such as sensory fatigue, mental exhaustion, and menstrual cycles. The comments additionally reveal the emotional biases embedded in professional settings, where neutral facial expressions are frequently misread as hostility. Within these digital spaces, comment sections operate as critical sites of affective solidarity and communal validation, facilitated through emojis, humor, and shared experiential narratives. The study concludes that platforms like TikTok function as transformative sites through which Black women reclaim narrative authority over their emotional identities, dismantle racialized tropes, and cultivate resilient communal spaces. Theoretically, this research contributes to ongoing discussions on digital discourse, racial representation, and marginalized agency in the algorithmic era.

JELITAVol. 7(1)
Universitas Bosowa (ID)
Openalex Percentile: Top 11%
Digital Communication and Language
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