## 4 Results  
We performed experiments on three datasets, each divided into training and testing sets with a 70/30 ratio as discussed in Section 3. Comparison with Other Deep Learning Models  
We evaluated the performance of the following four models using the collected dataset:  
* Logistic Regression (LR) [40]: A linear classification model that predicts the probability of a class by modeling the relationship between input features and the target variable. * Support Vector Machine (SVM) [41]: a supervised learning algorithm that finds the optimal hyperplane to separate classes by maximizing the margin between data points. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Optimizer & Adam \\ \hline Learning rate & 0.001 \\ \hline LSTM units & 256 \\ \hline Batch size & 64 \\ \hline Dropout (1 st layer) & 0.4 \\ \hline Dropout (2 nd layer) & 0.3 \\ \hline Max pool size & 2 \\ \hline Epochs & 20 \\ \hline Validation split & 0.2 \\ \hline Kernel sizes (conv1d) & 3, 4, 5 \\ \hline Conv1d filters & 256 \\ \hline BiLSTM layer & Bidirectional \\ \hline Class weight & Balanced \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental parameters. * Long Short-Term Memory (LSTM) networks [42]: a type of recurrent neural network capable of learning long-term dependencies in sequence data through specialized gating mechanisms. * Convolutional Neural Networks (CNN) [43]: deep learning models that capture local patterns in data through convolutional layers, often used for image and text classification. We also evaluated the performance of the following three combination models from the literature on the collected dataset:
* Doc2 Vec + SVM [27]: A method where documents are converted into fixed-length vector representations using Doc2 Vec and then classified using a Support Vector Machine. * RoBERTa [3]: A robustly optimized BERT pretraining approach that enhances the BERT model by training with more data and computational power for better language understanding. * BiLSTM + ELMo + Attention mechanism [44]: A model combining bidirectional LSTMs with ELMo contextualized embeddings and an attention mechanism to capture complex linguistic patterns for improved performance. The DLCTC model demonstrated a clear advantage over other models on the collected X dataset, achieving an F1 score of 87.64% and an accuracy of 85.14%, as outlined in Table 4. This enhanced performance can be attributed to the model's unique integration of character, word, and concept embeddings, combined with BiLSTM and attention mechanisms. Such architecture effectively captures detailed linguistic features and context, significantly boosting classification accuracy. The use of Probase for concept embeddings adds another layer of understanding, allowing the model to disambiguate meanings in tweets that are often informal and ambiguous. The model's ability to handle nuanced language makes it exceptionally well-suited for analyzing short social media texts. In contrast, traditional machine learning models like LR and SVM achieved F1 scores of around 62% and accuracies of approximately 75%. While these models provided reasonable performance, their simpler designs were not capable of grasping complex language patterns or understanding the context of social media content as effectively. Deep learning models, such as LSTM and CNN, reached F1 scores of 56.9% and 61.53%. Although these models can learn sequential and spatial features, they cannot handle informal language nuances effectively and do not benefit from the character, word, and concept-level embeddings that DLCTC utilizes. A significant factor in the DLCTC model's success is the extensive preprocessing it employs. Techniques like text normalization, Byte Pair Encoding (BPE) for handling abbreviations and slang, and converting emojis into text all contribute to ensuring that the input