## 3 Materials and Methods  
In this section, we discuss the architecture of the proposed model, important definitions, the problem statement and the algorithms implemented, and the technologies utilized in this study. The study addresses significant challenges in classifying social media messages, particularly data sparsity, imbalance, and ambiguity, which adversely affect classification accuracy. Data sparsity refers to the limited context and number of words typically found in short social media posts, making it difficult to extract meaningful patterns. Class imbalance occurs when the dataset contains significantly more instances of one class than the other, leading to biased model predictions. Ambiguity arises from the informal language, abbreviations, and slang commonly used in social media posts, which complicates the process of understanding the true meaning of messages [37]. The short text classification problem in our study is structured as follows. Given a short text dataset, each short text is represented by a sequence of characters, words, concepts, and sentences. The character set encompasses all unique characters in the dataset, while the vocabulary includes all the distinct words. Each short text is composed of a sequence of words. Additionally, concepts related to these words are extracted from the Probase knowledge base developed by Microsoft Research Asia, located in Beijing, China [7], forming a set of concepts. These characters, words, and concepts are integrated to create the final feature space for each short text. The objective of short text classification is to train a classifier that maps this extended feature space to the label set. This study focuses on the following problems:  
1. SM messages about traffic are often brief and informal, leading to issues like data sparsity, ambiguity, and class imbalance, which complicate accurate classification.
2. Current models struggle with short, context-poor text and fail to effectively handle informal language, domain-specific terminology, and abbreviations commonly found in traffic-related messages.
3. There is a need for a model that can integrate multi-level embeddings (character, word, and concept) with an attention mechanism to accurately classify traffic-related messages, enhancing Intelligent Transport Systems (ITS) with reliable real-time information.  
The methodological process employed in this study is illustrated in Figure 1.  
Figure 1 displays the framework of our approach with the three stages of the proposed model--an innovative approach that integrates character-level, word-level, and context-relevant features with an attention mechanism based on BiLSTM and TextCNN. It includes the embedding layer, representation layer, and output layer. In the embedding layer, in terms of short text conceptualization using the external knowledge base Probase, the short text is transformed into vectors at the character level, word level, and receipt level, respectively. In the representation layer, to obtain the full feature representations, we divide this layer into multiple stages to achieve different levels of feature representation. As a branch, each stage contains the convolutional module, TextCNN module, and attention module. In the output layer, a softmax is used as a classifier. The framework operates in two phases: the offline phase, where the DLCTC model is trained using labeled data, learning to optimize embeddings and network weights, and the online phase, where the trained model processes incoming social media messages as traffic-related or non-traffic-related. The input to the framework consists of social media posts, represented at the character, word, and concept levels, and the output is a binary classification that identifies whether each message is relevant to traffic incidents. This structured approach enables the model to capture linguistic patterns and contextual relationships, significantly improving classification performance compared to existing methods.  
Figure 1: The overall framework of the proposed method.