Deciphering Digital Discourse: Detecting Cyberbullying Patterns in Filipino Tweets Using Machine Learning

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The research addresses the escalating challenge of cyberbullying in the Philippines, a concern magnified by widespread social media use. A dataset of 146,661 tweets is analyzed using a pre-trained natural language processing model tailored to detect derogatory Filipino terms. The methodology is designed to preprocess data for clarity and analyze derogatory phrases, using the 23 key terms to indicate cyberbullying. Through quantitative analysis, specific patterns of derogatory term co-occurrence are uncovered. The research specifically focuses on Filipino digital discourse, uncovering patterns of derogatory language usage, which is unique to this context. Combining data mining and machine learning techniques, including Frequent Pattern (FP)-growth for pattern identification, cosine similarity for phrase correlation, and classification technique, the research achieves an accuracy rate of 97.91%. To assess the model’s reliability and precision, a 10-fold cross-validation is utilized. Moreover, by examining specific tweets, the analysis highlights the alignment between automated classifications and human judgment. The co-occurrence of derogatory terms, identified through methods like FP-growth and cosine similarity, reveals underlying cyberbullying narratives that are not immediately obvious. This approach validates the high accuracy of the models and emphasizes the importance of a comprehensive framework for detecting cyberbullying in a linguistically and culturally specific context. The findings substantiate the effectiveness of the targeted approach, providing essential insights for developing cyberbullying prevention strategies. Furthermore, the research enriches the literature on digital discourse analysis and online harassment prevention by addressing cyberbullying patterns and behaviors. Importantly, the research offers valuable guidance for policymakers in crafting more effective online safety measures in the Philippines.