Linguistically Differentiating Acts and Recalls of Racial Microaggressions on Social Media

dc.contributor.authorGunturi, Uma Sushmithaen
dc.contributor.authorKumar, Anishaen
dc.contributor.authorDing, Xiaohanen
dc.contributor.authorRho, Eugeniaen
dc.date.accessioned2024-05-02T12:34:19Zen
dc.date.available2024-05-02T12:34:19Zen
dc.date.issued2024-04-23en
dc.date.updated2024-05-01T07:49:21Zen
dc.description.abstractIn 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.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1145/3637366en
dc.identifier.urihttps://hdl.handle.net/10919/118728en
dc.language.isoenen
dc.publisherACMen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.holderThe author(s)en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleLinguistically Differentiating Acts and Recalls of Racial Microaggressions on Social Mediaen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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