# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 4 Outcomes and Discussion
### Discussion and Limitations  
In our research, we formulated three specific research questions that oriented our methodology and analyses. We present a clear and detailed description of our results on these research questions below.  
These results indicate that machine and deep learning models can efficiently recognize hate speech in Roman Urdu, even when dealing with a large number of spelling variations and code-mixing. Support Vector Machines (SVMs) and Gradient Boosting Machines (GBMs) were the most robust among machine learning approaches in terms of the consistency of their performance across all types of embeddings (TF-IDF and FastText). The SVM obtained a very high F-score of 94% using the TF-IDF matrix, which demonstrates its ability to capture characteristic lexical patterns widely associated with hate speech. Similarly, the GBM was very robust to linguistic diversities using FastText embeddings by being able to accommodate subword-level features, successfully coping with spelling disparities natural to Roman Urdu.  
Among deep learning methodologies, the LSTM and CNN architectures performed particularly well and even more so when using FastText embeddings. Sequences of tokens were processed by the LSTM with its memory mechanism, which resulted in effective capturing of long-range context, with a corresponding F-score of 96.2%. CNN (95.1% F1) stood out in identifying local lexical patterns and morphological characteristics of hate speech. The strong performance of these models also suggests that state-of-the-art deep learning approaches can overcome the linguistic challenges of Roman Urdu (e.g., spelling variations, informal expressions, code-mixing).  
With respect to RQ2, our comparative study demonstrated that the selection of feature representations has a crucial impact on classification results. Also among the feature representation methods (TF-IDF, Word2 Vec, and FastText), FastText embeddings yielded  
Figure 3: Confusion matrix for CNN.  
the best results in most of the models, as it has great discriminatory capabilities to treat non-standard spelling and morphological variations properly.  
The best result was obtained by combining FastText embeddings and LSTM with a 96.2% F-score. Results also indicated the superiority of CNN with FastText as the second best performing model (F-score 95.1%). In contrast, the old-fashioned TF-IDF representations, which may work well for some ML algorithms (e.g., SVM), were no longer satisfactory for DL models, which underscores the necessity for context-rich embeddings in deep learning applications. So the best strategy for hate speech detection in Roman Urdu is to make use of deep learning techniques, especially LSTM with strong embeddings such as FastText.  
Incorporating deep contextual embeddings significantly improved the classification performance in both traditional machine learning and deep learning models. Performance was greatly enhanced by Word2 Vec embeddings, which were able to capture semantic similarity, especially for ensemble techniques including the Random Forest and Gradient Boosting Machines. These embeddings enabled machine learning models to outperform benchmarks based on conventional TF-IDF values, as they allowed the models to make use of the semantic and syntactic distance between words, but still, they were limited to a dense embedding structure that does not suit many of the conventional classifiers.