## 8 Limitations  
The present work provides significant insights into hate speech identification utilizing the Twitter_hatespeech dataset; nonetheless, it is essential to recognize many constraints that may influence the generalizability and application of the findings. The dataset utilized in this research constitutes a temporal snapshot of data gathered from Twitter, presently known as X [22]. The algorithms of platforms, content control rules, and user behaviors have probably advanced since the dataset's inception. Modifications to the platform's functionality and moderation systems may affect the reliability of hate speech detection models developed on outdated data, rendering them less dependable for contemporary use [22]. A further issue relates to the dataset's inherent characteristics, which may inadequately represent the range of hate speech manifestations across various languages, cultures, or social circumstances. The dataset's predominance in English limits its applicability to non-English material and multilingual contexts. Moreover, biases inherent in the dataset--such as the disproportionate representation of certain demographic groups--may result in distorted model predictions, affecting fairness and accuracy in practical applications. Ultimately, although we employed machine learning methods to improve the detection efficacy, the models rely on the quality and variety of training data. The dependence on established labels within the dataset creates the potential for annotation bias, which may restrict the models' efficacy in identifying more subtle or novel manifestations of hate speech. As stated above, the continuous changes in the X (formerly Twitter) API are problematic in the sense that they present researchers with difficulties, especially when it comes to data scraping in the framework of social media research [23]. Some of these limitations \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**SI. No.**} & \multirow{2}{*}{**Methods Used/Reference**} & \multirow{2}{*}{
\begin{tabular}{c} **Vectorization** \\ **Techniques** \\ \end{tabular} } & \multicolumn{6}{c}{**Performance Metric**} \\ \cline{3-8}  & & & **A** & **P** & **R** & **F1** & **S** & **ROC\_AUC** \\ \hline
1 & Logistic Regression [10] & Hashing vectorizer & 55.0 & 56.1 & 66.0 & 60.0 & - & 59.0 \\ \hline
2 & Decision Tree (DT) [13] & Bag of Words (BoW) & 94.3 & 91.9 & 88.1 & 89.9 & 91.1 & - \\ \hline
3 & Naïve Bayes (NB) [14] & Count Vectorizer & 95.7 & 94.0 & 93.0 & - & - & 50.0 \\ \hline
4 & Naïve Bayes (NB) [15] & TF-IDF Transformer & 87.5 & 88.0 & 87.6 & 87.3 & - & 95.8 \\ \hline
5 & K-Nearest Neighbor (KNN) [16] & Count Vectorizer & 92.4 & 92.3 & 92.5 & 92.8 & 91.8 & - \\ \hline
6 & Support Vector Machine [17] & TF-IDF & - & 68.7 & 82.7 & 74.9 & - & - \\ \hline \multirow{3}{*}{7} & Support Vector Machine with & & & & & & & \\  & Particle Swarm Optimization & TF, TF-IDF & 77.0 & 77.5 & 76.1 & - & - & - \\  & (SVM-PSO) [18] & & & & & & & \\ \hline
8 & Support Vector Machine (SVM) [19] & Word2 vec & 82.6 & - & - & 62.0 & - & - \\ \hline
9 & Stepwise PRISMA framework [20] & NLP and DL & - & - & - & - & - & - \\ \hline
10 & Naïve Bayes [21] & TF-IDF & - & 94.6 & 94.64 & 94.62 & - & - \\ \hline
11 & Proposed model (Random Forest) & CBow, Skip-gram & 96.1 & 98.9 & 93.3 & 96.0 & 92.9 & 96.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Algorithm analysis and metric evaluation. affect the ability to acquire real-time or big data which are often critical for carrying out comprehensive analyses. However, this should not be seen as an impossibility of carrying out research but as a challenge that poses the need to invent new ways of conducting research as well as developing new methodologies. Some of the other options open to a researcher are the use of existing open-source datasets, searching other data collection tools on the internet, and liaising with organizations that must have access to broader datasets.