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• W2012098293 abstract "Using page-level metrics of a randomly selected group of 15,625 among the top 100,000 Face book check-in locations which rank high in terms of customer engagement, we explore if the short-term dynamical information on these metrics could deliver, via a clustering approach, some new insights for marketing decision making. Using a highly-scalable clustering algorithm, statistical methods, and combinatorial optimization met heuristics based on memetic algorithms, we have observed that some pages naturally cluster with others that share the same user-defined category. Our results highlight the need of suggesting other further meta-categories that encompass several user-reported categories for pages and that a priori geographical segmentation might be necessary to investigate more relevant patterns that take into account seasonal variability of behaviours and physical proximity." @default.
• W2012098293 created "2016-06-24" @default.
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• W2012098293 date "2014-12-01" @default.
• W2012098293 modified "2023-09-23" @default.
• W2012098293 title "Engagement in Motion: Exploring Short Term Dynamics in Page-Level Social Media Metrics" @default.
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• W2012098293 doi "https://doi.org/10.1109/bdcloud.2014.56" @default.
• W2012098293 hasPublicationYear "2014" @default.
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