Common problems in playing online mobile and computer games were related to
toxic behavior and abusive communication among players. Based on different
reports and studies, the study also discusses the impact of online hate speech
and toxicity on players' in-game performance and overall well-being. This study
investigates the capability of pre-trained language models to classify or
detect trash talk or toxic in-game messages The study employs and evaluates the
performance of pre-trained BERT and GPT language models in detecting toxicity
within in-game chats. Using publicly available APIs, in-game chat data from
DOTA 2 game matches were collected, processed, reviewed, and labeled as
non-toxic, mild (toxicity), and toxic. The study was able to collect around two
thousand in-game chats to train and test BERT (Base-uncased), BERT
(Large-uncased), and GPT-3 models. Based on the three models' state-of-the-art
performance, this study concludes pre-trained language models' promising
potential for addressing online hate speech and in-game insulting trash talk.

研究探讨了在线游戏中存在的有害行为和恶劣沟通问题，特别关注在线仇恨言论和毒性对玩家游戏表现和整体幸福感的影响。研究使用预训练语言模型来分类或检测游戏中的恶意言论和有毒信息，并评估了 BERT 和 GPT 语言模型在检测游戏内聊天中毒性的性能。通过公开可用的 API，收集、处理、审查和标注了 DOTA 2 游戏匹配中的游戏聊天数据，并使用 BERT（基于小写）模型、BERT（基于大写）模型和 GPT-3 模型进行了训练和测试。根据这三种模型的最新性能，本研究得出了预训练语言模型在解决在线仇恨言论和游戏中恶意侮辱性言论问题方面的潜力。