  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.
