“She’ll Never Be a Man” A Corpus-Based Forensic Linguistic Analysis of Misgendering Discrimination on X

Abstract

Misgendering is a form of microaggression that reinforces gender binarism and involves the use of incorrect pronouns, names or gendered language when referring to a transgender and gender non-conforming (TGNC) individual. Despite growing awareness, it remains a persistent form of discrimination, and it is crucial not only to understand and address misgendering but also to analyse its impact within online discourse towards the TGNC community. The present study examines misgendering directed at the TGNC community present on platform X. To achieve this, a representative sample of 400 tweets targeting two TGNC individuals is compiled, applying an annotation scheme to manually classify the polarity of each tweet and instances of misgendering, and then comparing the manual annotations with those of an automatic sentiment detection system. The analysis focuses on the context and frequency of intentional misgendering, using word lists to examine the data. The results confirm that misgendering perpetuates discrimination, tends to co-occur with other forms of aggression, and is not effectively identified by automatic sentiment detection systems. Finally, the study highlights the need for improved automatic detection systems to better identify and address misgendering in online discourse and provides potentially useful tools for future research.