Buzzer Account Detection in Political Hate Tweets: Case Study of the Indonesian Presidential Election 2024 - Herman, Fizio Ramadhan; Ade Romadhony (International Journal on Information and Communication Technology (IJoICT), 2024)
The Indonesian Presidential Election of 2024 has seen a widespread use of social media such as Twitter for political campaigning and discussion. However, this has also enabled the spread of hate speech from buzzer accounts that are created to influence public opinions. This study implements a machine learning approach to classify buzzer accounts that are spreading hate speeches during the presidential election period. By utilizing IndoBERT for hate speech classification and a traditional machine learning model to classify buzzer accounts. This study analyzes 62,341 tweets for hate speech classification and 961 accounts for buzzer account classification. Our implementation of IndoBERT achieved a strong performance with 91.12% of precision and recall, and 91.19% accuracy and F1-score in hate speech classification. While for buzzer account classification, we compared Decision Tree, Random Forest, and XGBoost, with Decision Tree achieving the highest performance of 64% precision, recall, accuracy, and F1-Score. Our results demonstrate the effectiveness of combining deep learning for hate speech classification with traditional machine learning for buzzer account classification, contributing to the development of more effective content filtering for election discourse on social media.