Dismantling Hate: Understanding Hate Speech Trends Against NBA Athletes

Edinam Kofi Klutse, Samuel Nuamah-Amoabeng, Hanjia Lyu, Jiebo Luo
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Abstract

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.
拆解仇恨:了解针对NBA运动员的仇恨言论趋势
社交媒体已经成为体育迷们表达对运动员表现意见的热门平台。球迷们一直对运动员抱有很高的期望,期待着一周又一周的出色表现。这种持续的现象有时会引起高度负面的情绪,最坏的情况是出现仇恨言论。美国国家篮球协会(NBA)被广泛认为是全球最受欢迎的体育联盟之一。然而,近年来出现的一个不幸的方面是联盟中存在辱骂球迷。因此,本研究的重点是确定哪些NBA运动员在推特上遭受虐待,并深入研究这种虐待背后的潜在原因。为了解决手头的研究问题,该研究采用了一组精心设计的关键字来查询Twitter API,收集了一系列可能包含针对NBA球员的仇恨言论的推文。实现了深度学习分类模型,有效识别真正表现出仇恨言论的推文。我们进一步使用关键字搜索方法来检测最容易受到仇恨言论攻击的特定群体,并确定仇恨言论推文的主题。我们的研究结果表明,某些运动员群体特别容易受到球迷仇恨言论的影响。种族主义、体格羞辱、比赛风格和反lgbtq言论是主要主题。这些发现有助于更广泛地了解NBA运动员在数字空间中面临的挑战,并为制定打击仇恨言论的策略提供基础,并为NBA社区的所有个人营造一个更具包容性的环境。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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