Are Crowded Events Forecastable from Promotional Announcements with Large Language Models?

Soto Anno, Dario Tenore, Kota Tsubouchi, Masamichi Shimosaka

Abstract: Forecasting the number of visitors at a public event, termed event crowd forecasting (ECF), has recently garnered attention due to its social significance. Although existing ECF methods have pioneered successful feature design by considering event contents with contexts (e.g., weather, type of day, time), their scalability across different event types is limited due to the necessity of costly feature engineering. To address this issue, we propose a novel ECF framework, named EventOutlook. Based on our observation of various events, online event announcements indicate the factors that induce crowded events. Thus, we incorporate event announcements into ECF methods. To handle such unstructured data, which have no unified format among events, we leverage large language models (LLM) to extract crowding factors and embed them into an LLM-driven crowding-indicator feature (LCIF). Empirical experiments with real-world event data show that EventOutlook significantly improved ECF performance compared to state-of-the-art methods.