| Abstract |
Highlights: What are the main findings? Enhanced Event Detection Accuracy: The introduction of the SemConvTree model, which integrates improved versions of BERTopic, TSB-ARTM, and SBert-Zero-Shot, enables a significant enhancement in the detection accuracy of urban events. The model’s ability to incorporate semantic analysis along with statistical evaluations allows for discerning and categorizing events from social media data more precisely. This results in approximately a 40% increase in the F1-score for event detection compared to previous methods. Semantic Analysis for Event Identification: The SemConvTree model leverages semi-supervised learning techniques to analyze the semantic content of social media posts. This approach helps in understanding the nuanced contexts of urban events, improving the identification process. The model not only recognizes the occurrence of events but also categorizes them into meaningful groups based on their semantic characteristics, which is crucial for effective urban management and planning. What are the implications of the main findings? The increased accuracy in event detection ensures that urban planners and emergency services can respond more effectively to both planned and unplanned urban events. More accurate data leads to better resource allocation, ensuring that services are deployed where they are most needed. This could lead to enhanced safety, improved traffic management, and better crowd control during events, ultimately enhancing urban living conditions. By effectively categorizing urban events based on their semantic characteristics, city administrators can gain insights into the types of events that are prevalent in different areas of the city. This can inform more targeted community engagement strategies, help in the planning of public services and facilities, and ensure that urban policies are closely aligned with the actual dynamics of the city. Additionally, this can aid in long-term urban development strategies by identifying evolving trends and shifts in urban activity patterns. The digital world is increasingly permeating our reality, creating a significant reflection of the processes and activities occurring in smart cities. Such activities include well-known urban events, celebrations, and those with a very local character. These widespread events have a significant influence on shaping the spirit and atmosphere of urban environments. This work presents SemConvTree, an enhanced semantic version of the ConvTree algorithm. It incorporates the semantic component of data through semi-supervised learning of a topic modeling ensemble, which consists of improved models: BERTopic, TSB-ARTM, and SBert-Zero-Shot. We also present an improved event search algorithm based on both statistical evaluations and semantic analysis of posts. This algorithm allows for fine-tuning the mechanism of discovering the required entities with the specified particularity (such as a particular topic). Experimental studies were conducted within the area of New York City. They showed an improvement in the detection of posts devoted to events (about 40% higher f1-score) due to the accurate handling of events of different scales. These results suggest the long-term potential for creating a semantic platform for the analysis and monitoring of urban events in the future. © 2024 by the authors. |
