<![CDATA[The accelerating growth of social media has transformed digital platforms into spaces where people express opinions on social and political issues. One event that generated numerous public comments was the demonstration held on August 25–31, 2025. Many comments contained harsh language, provocation, insults, and calls for conflict that had the potential to trigger negative emotions in digital spaces. This study aims to classify provocative and non-provocative comments on Instagram using the Naïve Bayes algorithm and compare its performance with Support Vector Machine (SVM) and Decision Tree algorithms. The data were collected through a web scraping process, resulting in 3,396 comments. After the cleansing and preprocessing stages, the dataset was reduced to 2,490 comments. The preprocessing stages included transform case, tokenizing, stopwords removal, filter tokens, and stemming. Furthermore, word weighting was carried out using the TF-IDF method and implemented in RapidMiner with an 80:20 data split ratio. Based on manual labeling, 1,279 provocative comments and 1,211 non-provocative comments were obtained. The evaluation results showed that Naïve Bayes achieved an accuracy of 72.15%, SVM achieved 69.44%, and Decision Tree achieved 72.91%. Although Decision Tree produced a slightly higher accuracy, Naïve Bayes demonstrated a more balanced performance in detecting both comment classes, even though the accuracy value was still in the moderate category. The findings provide insights into the effectiveness of machine learning algorithms for identifying provocative comments and may support the development of automated content moderation on social media platforms.]]>
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