In recent times, the rising cases of racial abuse toward football players are gravely concerning. This trend is becoming a recurrent decimal on social networks with little or no proactive measures to serve as a deterrent to others in most cases. There was a public outcry due to massive racial abuse remarks targeting some England footballers during the Euro2020 final between England and Italy on social media. This motivated us to investigate and recommend a better solution to the social pandemic through the application of transfer learning. This will go a long way to maintain unity in diversity in the game of football. Tweets with related hashtags to the football match were scraped. The researchers framed this problem as a multi-class classification task. The Valence Aware Dictionary and sEntiment Reasoner (VADER) compound score was employed to label the dataset as positive (POS), negative (NEG), and neutral (NEU). Seven pre-trained models were fine-tuned and corresponding models were built for identifying negative sentiments toward the football players. Three contributions were made – a review of literature on racial abuse targeting footballers, a new dataset to further research in this direction and a robust transformer-based model identified for policing Twitter during football games. Among the models built, the distilled version of Bidirectional Encoder Representations from Transformer (DistilBERT) proved superior with an F1-score of 0.99.

中文翻译：

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用于识别针对足球运动员的推文中的负面情绪的迁移学习方法

近年来，针对足球运动员的种族虐待案件不断增加，令人严重担忧。这种趋势正在成为社交网络上经常出现的小数点，在大多数情况下很少或根本没有主动措施来威慑他人。在2020年欧洲杯决赛英格兰与意大利之间，社交媒体上针对一些英格兰足球运动员的大规模种族歧视言论引起了公众的强烈抗议。这促使我们通过应用迁移学习来研究并推荐更好的解决社会流行病的方案。这对于保持足球比赛多样性的统一大有帮助。带有与足球比赛相关标签的推文被删除。研究人员将这个问题定义为多类分类任务。采用效价感知词典和情感推理器 (VADER) 复合评分将数据集标记为阳性 (POS)、阴性 (NEG) 和中性 (NEU)。对七个预训练模型进行了微调，并建立了相应的模型来识别对足球运动员的负面情绪。做出了三项贡献——对针对足球运动员的种族虐待的文献进行了回顾，一个用于进一步研究这一方向的新数据集，以及一个用于在足球比赛期间监管 Twitter 的强大的基于 Transformer 的模型。在构建的模型中，来自 Transformer 的双向编码器表示 (DistilBERT) 的精炼版本表现出色，F1 分数为 0.99。