###### Abstract  
Classifying social media (SM) messages into relevant or irrelevant categories is challenging due to data sparsity, imbalance, and ambiguity. This study aims to improve Intelligent Transport Systems (ITS) by enhancing short text classification of traffic-related SM data. Deep learning methods such as RNNs, CNNs, and BERT are effective at capturing context, but they can be computationally expensive, struggle with very short texts, and perform poorly with rare words. On the other hand, transfer learning leverages pre-trained knowledge but may be biased towards the pre-training domain. To address these challenges, we propose DLCTC, a novel system combining character-level, word-level, and context features with BiLSTM and TextCNN-based attention. By utilizing external knowledge, DLCTC ensures an accurate understanding of concepts and abbreviations in traffic-related short texts. BiLSTM captures context and term correlations; TextCNN captures local patterns. Multi-level attention focuses on important features across character, word, and concept levels. Experimental studies demonstrate DLCTC's effectiveness over well-known short-text classification approaches based on CNN, RNN, and BERT. short text classification; BiLSTM-TextCNN integration; multi-level attention mechanism; character-word-concept embeddings; traffic-related social media analysis; intelligent transport systems (ITS) +
Footnote †: journal: Applied sciences  
a scalable, cost-effective solution that supports traditional methods while opening new pathways for data-driven, urban decision-making. The volume of short texts being generated daily by social media (SM) platforms introduces opportunities as well as challenges in the field of natural language processing (NLP). Accurately classifying SM posts, especially in safety-critical domains such as traffic, has become increasingly important yet challenging. Unlike long texts, short texts lack the necessary context, hindering accurate interpretation of their meaning. The varied interpretations of words and use of abbreviations or specific terms like \"RT\" for Route, \"WB\" for Westbound, and \"ETA\" for Estimated Time of Arrival in traffic-related tweets further complicate identifying relevant text, highlighting the need to address nuances for effective short text classification. By classifying these data, incidents can be detected early, improving traffic flow and safety through timely interventions. This research enhances Intelligent Transport Systems by integrating social media data as supplementary to sensors for hands-on traffic management. The first step in utilizing social media for traffic management involves extracting relevant traffic data accurately classified into irrelevant or relevant traffic events. To develop effective approaches for text relevancy, NLP, machine learning (ML), and deep learning (DL) methods should be employed to accurately categorize SM messages. This process is challenged by data sparsity, imbalance, and ambiguity, as well as the use of slang, acronyms, and abbreviations (SAB), further complicating semantic extraction. Efforts went to classifying short text from social media to identify relevant text using DL methods to resolve issues in short text, such as work on the integration of context-relevant concepts into Convolutional Neural Networks, developing a model (DE-CNN) that surpassed state-of-the-art methods on three classification tasks through an attention mechanism for concept selection [1]. However, it primarily focused on performance improvements via concept integration and did not address challenges related to handling novel concepts or ambiguous short texts lacking clear context. To address the issue of context-relevant features, multi-stage attention was used, outperforming BERT on most datasets. However, it struggled with rare words to capture character-level semantics [2]. A real-time traffic detection system using X data was proposed, comparing various models and embeddings.