Linguistically Differentiating Acts and Recalls of Racial Microaggressions on Social Media

Fuente: arXiv

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Bibliographic Details
Main Authors:	Gunturi, Uma Sushmitha, Kumar, Anisha, Ding, Xiaohan, Rho, Eugenia H.
Format:	Preprint
Published:	2024
Subjects:	Human-Computer Interaction Social and Information Networks
Online Access:	Acceder al recurso
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_version_	1866910381651263488
author	Gunturi, Uma Sushmitha Kumar, Anisha Ding, Xiaohan Rho, Eugenia H.
author_facet	Gunturi, Uma Sushmitha Kumar, Anisha Ding, Xiaohan Rho, Eugenia H.
contents	In this work, we examine the linguistic signature of online racial microaggressions (acts) and how it differs from that of personal narratives recalling experiences of such aggressions (recalls) by Black social media users. We manually curate and annotate a corpus of acts and recalls from in-the-wild social media discussions, and verify labels with Black workshop participants. We leverage Natural Language Processing (NLP) and qualitative analysis on this data to classify (RQ1), interpret (RQ2), and characterize (RQ3) the language underlying acts and recalls of racial microaggressions in the context of racism in the U.S. Our findings show that neural language models (LMs) can classify acts and recalls with high accuracy (RQ1) with contextual words revealing themes that associate Blacks with objects that reify negative stereotypes (RQ2). Furthermore, overlapping linguistic signatures between acts and recalls serve functionally different purposes (RQ3), providing broader implications to the current challenges in content moderation systems on social media.
format	Preprint
id	arxiv_https___arxiv_org_abs_2403_16514
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Linguistically Differentiating Acts and Recalls of Racial Microaggressions on Social Media Gunturi, Uma Sushmitha Kumar, Anisha Ding, Xiaohan Rho, Eugenia H. Human-Computer Interaction Social and Information Networks In this work, we examine the linguistic signature of online racial microaggressions (acts) and how it differs from that of personal narratives recalling experiences of such aggressions (recalls) by Black social media users. We manually curate and annotate a corpus of acts and recalls from in-the-wild social media discussions, and verify labels with Black workshop participants. We leverage Natural Language Processing (NLP) and qualitative analysis on this data to classify (RQ1), interpret (RQ2), and characterize (RQ3) the language underlying acts and recalls of racial microaggressions in the context of racism in the U.S. Our findings show that neural language models (LMs) can classify acts and recalls with high accuracy (RQ1) with contextual words revealing themes that associate Blacks with objects that reify negative stereotypes (RQ2). Furthermore, overlapping linguistic signatures between acts and recalls serve functionally different purposes (RQ3), providing broader implications to the current challenges in content moderation systems on social media.
title	Linguistically Differentiating Acts and Recalls of Racial Microaggressions on Social Media
topic	Human-Computer Interaction Social and Information Networks
url	https://arxiv.org/abs/2403.16514