  Social media has emerged as a popular platform for sports fans to express
their opinions regarding athletes' performance. Fans consistently hold high
expectations for athletes, anticipating exceptional performances week after
week. This ongoing phenomenon sometimes gives rise to highly negative
sentiments, with the worst-case scenario involving the occurrence of hate
speech. The National Basketball Association (NBA) is widely recognized as one
of the most popular sports leagues globally. However, an unfortunate aspect
that has emerged in recent years is the presence of abusive fans within the
league. Consequently, the focus of this research is to identify which NBA
athletes experience abuse on Twitter and delve deeper into the underlying
reasons behind such mistreatment. To address the research questions at hand,
the study employs a curated set of keywords to query the Twitter API, gathering
a comprehensive collection of tweets that potentially contain hate speech
directed toward NBA players. A deep learning classification model is
implemented, effectively identifying tweets that genuinely exhibit hate speech.
We further use keyword search methods to detect the specific groups that are
targeted by hate speech the most and identify topics of hate speech tweets. The
findings of our research indicate that certain groups of athletes are
particularly vulnerable to hate speech from fans. Racism, physique shaming,
play style, and anti-LGBTQ remarks are the major themes. These findings
contribute to a broader understanding of the challenges faced by NBA athletes
in the digital space and provide a foundation for developing strategies to
combat hate speech and foster a more inclusive environment for all individuals
involved in the NBA community.
