ORIGINAL

Abstract

African American Vernacular English (AAVE) is a dialect utilized by a large percentage of the African American population. Today, AAVE's usage is stronger than ever, with its presence easily recognizable in social media comments and other online platforms. This phenomenon, defined as Mock AAVE, grows stronger and stronger in its employment among digital spaces. The observation of these occurrences led to a series of questions that this paper sought to answer: does the usage of AAVE qualities in text comments lead to more internet engagement and where in online spaces do these comments tend to cluster? The approach here details exploring a unique binary text classification system, one that takes into account AAVE vocabulary and a "Southern Similarity Index" inspired by the linguistic origins under the AAVE Dialect Hypothesis. This required the original contribution of Southern and non- Southern English datasets alongside a Southern American English classifier. After comparing a model with the contribution of the Southern Similarity Index to a model without that feature, it was shown that the performances of the two classifiers were comparable in the current methodology. This model was then run on a manually curated YouTube dataset that contains a subset of popular Black and non-Black content creators. The result of running this classifier yielded results that showed no inherent "comment reply" or "comment like" bias for comments classified as AAVE.