An Empirically Grounded Location-Aware Framework for Event-Centric Consumer Behavior Modeling Using Social Media Analytics
AUTHORS
Mohd Azhar,Department of Marketing, New Delhi Institute of Management, New Delhi, India
Seema Nagar,IBM Research, Yorktown Heights, NY, United States
ABSTRACT
The increasing integration of social media into everyday communication has opened new avenues for examining consumer behavior in dynamic, event-centric environments. In particular, large-scale international events generate substantial volumes of user-generated content, offering opportunities to derive context-aware insights for strategic marketing interventions. In this context, the present study proposes an analytics-driven, location-aware consumer behavior modeling framework designed to capture and interpret spatiotemporal signals embedded in geo-tagged social media data. Departing from conventional approaches that predominantly rely on explicit mobility tracking or isolated sentiment analysis, the proposed framework adopts an integrated perspective by combining location intelligence with semantic and behavioral analytics. The study uses Twitter data associated with global events to construct a structured pipeline encompassing data acquisition, geolocation filtering, contextual preprocessing, and sentiment-based classification. Further, ensemble learning techniques, including bagging and boosting paradigms, are employed to model user behavioral inclination, with particular emphasis on comparative performance evaluation across classifiers. The results demonstrate that ensemble-based approaches, particularly Random Forest under the bagging framework, exhibit superior predictive performance in capturing behavioral tendencies across multiple performance indicators. Additionally, the analysis reveals that fine-grained spatial attributes and retweet-driven content diffusion are critical to enhancing the effectiveness of targeted marketing strategies. From a broader perspective, the study contributes to the emerging discourse on intelligent marketing systems by proposing a scalable, adaptable framework that supports real-time, context-aware decision-making in high-density event environments. The findings hold practical relevance for event organizers, urban retailers, and digital marketers seeking to align promotional strategies with evolving consumer sentiment and location-specific dynamics.
KEYWORDS
Social media analytics, Location intelligence, Consumer behavior modeling, Event-centric marketing, Ensemble learning, Geo-tagged data, Sentiment analysis, Decision support system
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