Fine-Tuning Pre-trained Language Models to Detect In-Game Trash Talks
Daniel Fesalbon Arvin De La Cruz Marvin Mallari Nelson Rodelas
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.