Detecting Online Gambling Promotions on Indonesian Twitter Using Text Mining Algorithm

This study addresses the pressing challenge of detecting online gambling promotions on Indonesian Twitter using text mining algorithms for text classification and analytics. Amid limited research on this subject, especially in the Indonesian context, we aim to identify common textual features used in gambling promotions and determine the most effective classification models. By analyzing a dataset of 6038 tweets collected and using methods such as Random Forest, Logistic Regression, and Convolutional Neural Networks, complemented by a comparison analysis of text representation methods, we identified frequently occurring words such as ‘link’, ‘situs’, ‘prediksi’, ‘jackpot’, ‘maxwin’, and ‘togel’. The results indicate that the combination of TF-IDF and Random Forest is the most effective method for detecting online gambling promotion content on Indonesian Twitter, achieving a recall value of 0.958 and a precision value of 0.966. These findings can contribute to cybersecurity and support law enforcement in mitigating the negative effects of such promotions, particularly on the Twitter platform in Indonesia.