## 3 Results
### Experimental Results of the Machine Learning Based Approach
**SVM's Consistent Performance:** One notable trend is the consistent increase in accuracy exhibited by the Support Vector Machine (SVM) across different scenarios. SVM, a robust classification algorithm, has demonstrated its efficacy in hate speech detection, consistently outperforming other methods. This aligns with the findings in Refs. [58; 59]. The robust performance of SVM is highlighted in diverse contexts. The incremental accuracy observed across scenarios reinforces SVM's suitability for hate speech detection tasks. **DT's Superior Performance in the Last Scenario:** In the last scenario (5%), we employed only 5% labeled data with a threshold of 0.9. The DT with TF-IDF emerged as a powerful performer, showcasing superior performance with an impressive accuracy of 97.1% (refer to Table 11). This exceptional result underscores the adaptability and strength of DT in handling hate speech detection tasks. It is especially notable under conditions of minimal labeled data and stringent similarity thresholds. **Method-Specific Variations:** While SVM consistently performs well and DT excels in specific scenarios, certain methods experience a reduction of up to 8% in specific contexts. This reduction underscores the nuanced nature of hate speech detection. Method-specific variations impact performance. Analyzing these variations is crucial for understanding the strengths and limitations of each method under different conditions. **Impact of Threshold Variations:** Threshold values are pivotal in influencing the accuracy of hate speech detection. The systematic exploration of varying threshold values is illustrated in Figures 3-5. This exploration allows for a nuanced understanding of how these values affect methods performance. The highest accuracy is consistently achieved at the most significant threshold value, particularly 0.9. It indicates the importance of optimizing similarity thresholds for accurate hate speech detection. **Optimization with Minimal Labeled Data:** An intriguing observation is the improvement in accuracy when reducing labeled data from 20% to 5%. This aligns with the calculated weights of available data. It suggests that the smaller the data, the higher the accuracy of the annotation process. The self-learning performance of the meta-learner and meta-vectorizer relies on an ensemble approach to optimize calculated weights. It contributes to enhanced performance. #### 3.3.3 Discussion on Biases and Mitigation Strategies

Our hate speech detection model relies on Indonesian YouTube comments. It may be subject to linguistic and thematic biases inherent in the dataset. These biases are influenced by cultural nuances and the focus on specific themes, such as the 2019 Indonesian presidential debate and COVID-19. They could limit the model's applicability to diverse linguistic and thematic contexts. Additionally, biases are introduced through the model's design. It includes the choice of machine learning algorithms (SVM, DT, KNN, and Naive Bayes) and feature extraction methods (Word2 Vec and TF-IDF). Annotation bias, stemming from subjective human judgment during annotation, further contributes to potential bias in the model's learning. Addressing these biases requires ongoing vigilance, continuous model evaluation, and a commitment to algorithmic fairness for equitable performance. Future research should consider diversifying datasets to include various themes, languages, and cultural contexts to mitigate biases. Employing fairness-aware machine learning techniques and continuous model evaluation and updates can address algorithmic biases over time. Proactively managing biases is crucial for promoting fairness and reliability in the responsible deployment of hate speech detection models. #### 3.3.4 Limitations

While showcasing promising results, our proposed hate speech detection approach is subject to certain limitations that warrant in-depth analysis. By dissecting these limitations, we aim to provide a nuanced understanding of the constraints and challenges associated with our methods.